Binary files /tmp/tmpublXeY/eKqjG2LwAg/agridat-1.17/build/vignette.rds and /tmp/tmpublXeY/h2cJguo6ZM/agridat-1.18/build/vignette.rds differ
diff -Nru agridat-1.17/data/damesa.maize.txt agridat-1.18/data/damesa.maize.txt
--- agridat-1.17/data/damesa.maize.txt	1970-01-01 00:00:00.000000000 +0000
+++ agridat-1.18/data/damesa.maize.txt	2020-09-22 18:34:37.000000000 +0000
@@ -0,0 +1,265 @@
+"site"	"rep"	"block"	"plot"	"gen"	"row"	"col"	"yield"
+"S1"	"R1"	"B1"	1	"G06"	1	1	9.93
+"S1"	"R1"	"B1"	2	"G22"	1	2	6.51
+"S1"	"R1"	"B2"	3	"G17"	1	3	7.92
+"S1"	"R1"	"B2"	4	"G14"	1	4	9.28
+"S1"	"R1"	"B3"	5	"G12"	1	5	7.56
+"S1"	"R1"	"B3"	6	"G10"	1	6	9.54
+"S1"	"R1"	"B4"	7	"G18"	1	7	9.09
+"S1"	"R1"	"B4"	8	"G09"	1	8	8.26
+"S1"	"R1"	"B5"	9	"G13"	1	9	8.06
+"S1"	"R1"	"B5"	10	"G05"	1	10	8.07
+"S1"	"R1"	"B6"	11	"G11"	1	11	9.09
+"S1"	"R1"	"B6"	12	"G16"	2	11	7.47
+"S1"	"R1"	"B7"	13	"G01"	2	10	7.22
+"S1"	"R1"	"B7"	14	"G02"	2	9	6.59
+"S1"	"R1"	"B8"	15	"G08"	2	8	7.88
+"S1"	"R1"	"B8"	16	"G21"	2	7	5.44
+"S1"	"R1"	"B9"	17	"G04"	2	6	7.16
+"S1"	"R1"	"B9"	18	"G03"	2	5	5.03
+"S1"	"R1"	"B10"	19	"G19"	2	4	7.07
+"S1"	"R1"	"B10"	20	"G15"	2	3	6.85
+"S1"	"R1"	"B11"	21	"G20"	2	2	5.78
+"S1"	"R1"	"B11"	22	"G07"	2	1	6.79
+"S1"	"R2"	"B12"	23	"G21"	3	1	7.8
+"S1"	"R2"	"B12"	24	"G19"	3	2	8.54
+"S1"	"R2"	"B13"	25	"G06"	3	3	8.87
+"S1"	"R2"	"B13"	26	"G07"	3	4	9.05
+"S1"	"R2"	"B14"	27	"G22"	3	5	7.01
+"S1"	"R2"	"B14"	28	"G09"	3	6	9.62
+"S1"	"R2"	"B15"	29	"G13"	3	7	7.77
+"S1"	"R2"	"B15"	30	"G20"	3	8	6.02
+"S1"	"R2"	"B16"	31	"G05"	3	9	7.42
+"S1"	"R2"	"B16"	32	"G10"	3	10	6.78
+"S1"	"R2"	"B17"	33	"G01"	3	11	7.53
+"S1"	"R2"	"B17"	34	"G18"	4	11	8.52
+"S1"	"R2"	"B18"	35	"G12"	4	10	8.93
+"S1"	"R2"	"B18"	36	"G08"	4	9	9.38
+"S1"	"R2"	"B19"	37	"G11"	4	8	8.21
+"S1"	"R2"	"B19"	38	"G17"	4	7	7.29
+"S1"	"R2"	"B20"	39	"G14"	4	6	9.86
+"S1"	"R2"	"B20"	40	"G15"	4	5	7.3
+"S1"	"R2"	"B21"	41	"G02"	4	4	7.32
+"S1"	"R2"	"B21"	42	"G03"	4	3	8.58
+"S1"	"R2"	"B22"	43	"G16"	4	2	8.33
+"S1"	"R2"	"B22"	44	"G04"	4	1	8.95
+"S1"	"R3"	"B23"	45	"G13"	5	1	9.4
+"S1"	"R3"	"B23"	46	"G06"	5	2	9.92
+"S1"	"R3"	"B24"	47	"G08"	5	3	10.07
+"S1"	"R3"	"B24"	48	"G05"	5	4	10.04
+"S1"	"R3"	"B25"	49	"G01"	5	5	9.87
+"S1"	"R3"	"B25"	50	"G22"	5	6	7.31
+"S1"	"R3"	"B26"	51	"G19"	5	7	7.87
+"S1"	"R3"	"B26"	52	"G12"	5	8	10.49
+"S1"	"R3"	"B27"	53	"G17"	5	9	8.81
+"S1"	"R3"	"B27"	54	"G04"	5	10	9.65
+"S1"	"R3"	"B28"	55	"G07"	5	11	9.51
+"S1"	"R3"	"B28"	56	"G09"	6	11	6.3
+"S1"	"R3"	"B29"	57	"G21"	6	10	7.84
+"S1"	"R3"	"B29"	58	"G14"	6	9	6.62
+"S1"	"R3"	"B30"	59	"G03"	6	8	7.83
+"S1"	"R3"	"B30"	60	"G18"	6	7	8.78
+"S1"	"R3"	"B31"	61	"G15"	6	6	7.24
+"S1"	"R3"	"B31"	62	"G11"	6	5	7.3
+"S1"	"R3"	"B32"	63	"G20"	6	4	8.28
+"S1"	"R3"	"B32"	64	"G10"	6	3	8.16
+"S1"	"R3"	"B33"	65	"G02"	6	2	8.98
+"S1"	"R3"	"B33"	66	"G16"	6	1	4.54
+"S2"	"R1"	"B1"	1	"G19"	1	1	2.21
+"S2"	"R1"	"B1"	2	"G12"	1	2	1.84
+"S2"	"R1"	"B2"	3	"G16"	1	3	2.15
+"S2"	"R1"	"B2"	4	"G04"	1	4	1.74
+"S2"	"R1"	"B3"	5	"G11"	1	5	1.85
+"S2"	"R1"	"B3"	6	"G08"	1	6	1.69
+"S2"	"R1"	"B4"	7	"G07"	1	7	3.04
+"S2"	"R1"	"B4"	8	"G03"	1	8	3.84
+"S2"	"R1"	"B5"	9	"G17"	1	9	2.47
+"S2"	"R1"	"B5"	10	"G01"	1	10	2.55
+"S2"	"R1"	"B6"	11	"G02"	1	11	2.96
+"S2"	"R1"	"B6"	12	"G18"	2	11	1.8
+"S2"	"R1"	"B7"	13	"G09"	2	10	2.14
+"S2"	"R1"	"B7"	14	"G10"	2	9	1.73
+"S2"	"R1"	"B8"	15	"G22"	2	8	2.46
+"S2"	"R1"	"B8"	16	"G20"	2	7	2.59
+"S2"	"R1"	"B9"	17	"G14"	2	6	1.71
+"S2"	"R1"	"B9"	18	"G15"	2	5	2.38
+"S2"	"R1"	"B10"	19	"G21"	2	4	1.62
+"S2"	"R1"	"B10"	20	"G05"	2	3	2.26
+"S2"	"R1"	"B11"	21	"G06"	2	2	2.31
+"S2"	"R1"	"B11"	22	"G13"	2	1	2.39
+"S2"	"R2"	"B12"	23	"G04"	3	1	3.93
+"S2"	"R2"	"B12"	24	"G12"	3	2	4.03
+"S2"	"R2"	"B13"	25	"G18"	3	3	2.1
+"S2"	"R2"	"B13"	26	"G05"	3	4	2.22
+"S2"	"R2"	"B14"	27	"G15"	3	5	3.41
+"S2"	"R2"	"B14"	28	"G03"	3	6	2.92
+"S2"	"R2"	"B15"	29	"G13"	3	7	3.11
+"S2"	"R2"	"B15"	30	"G17"	3	8	1.94
+"S2"	"R2"	"B16"	31	"G14"	3	9	0.36
+"S2"	"R2"	"B16"	32	"G10"	3	10	1.67
+"S2"	"R2"	"B17"	33	"G19"	3	11	2.61
+"S2"	"R2"	"B17"	34	"G20"	4	11	3.45
+"S2"	"R2"	"B18"	35	"G21"	4	10	2.84
+"S2"	"R2"	"B18"	36	"G22"	4	9	3.63
+"S2"	"R2"	"B19"	37	"G07"	4	8	3.15
+"S2"	"R2"	"B19"	38	"G06"	4	7	2.62
+"S2"	"R2"	"B20"	39	"G02"	4	6	3.37
+"S2"	"R2"	"B20"	40	"G11"	4	5	2.49
+"S2"	"R2"	"B21"	41	"G16"	4	4	2.74
+"S2"	"R2"	"B21"	42	"G09"	4	3	2.78
+"S2"	"R2"	"B22"	43	"G08"	4	2	2.4
+"S2"	"R2"	"B22"	44	"G01"	4	1	3.81
+"S2"	"R3"	"B23"	45	"G04"	5	1	5.16
+"S2"	"R3"	"B23"	46	"G01"	5	2	5.05
+"S2"	"R3"	"B24"	47	"G20"	5	3	3.78
+"S2"	"R3"	"B24"	48	"G06"	5	4	2.71
+"S2"	"R3"	"B25"	49	"G07"	5	5	3.89
+"S2"	"R3"	"B25"	50	"G22"	5	6	4.01
+"S2"	"R3"	"B26"	51	"G15"	5	7	4.42
+"S2"	"R3"	"B26"	52	"G05"	5	8	3.8
+"S2"	"R3"	"B27"	53	"G17"	5	9	4.31
+"S2"	"R3"	"B27"	54	"G12"	5	10	5.29
+"S2"	"R3"	"B28"	55	"G14"	5	11	3.53
+"S2"	"R3"	"B28"	56	"G18"	6	11	3.6
+"S2"	"R3"	"B29"	57	"G16"	6	10	2.93
+"S2"	"R3"	"B29"	58	"G08"	6	9	2.28
+"S2"	"R3"	"B30"	59	"G11"	6	8	4.8
+"S2"	"R3"	"B30"	60	"G09"	6	7	6.19
+"S2"	"R3"	"B31"	61	"G21"	6	6	3.85
+"S2"	"R3"	"B31"	62	"G03"	6	5	6.59
+"S2"	"R3"	"B32"	63	"G19"	6	4	5.89
+"S2"	"R3"	"B32"	64	"G13"	6	3	5.35
+"S2"	"R3"	"B33"	65	"G02"	6	2	7.42
+"S2"	"R3"	"B33"	66	"G10"	6	1	5.57
+"S3"	"R1"	"B1"	1	"G08"	1	1	8.53
+"S3"	"R1"	"B1"	2	"G16"	1	2	6.28
+"S3"	"R1"	"B2"	3	"G21"	1	3	5.59
+"S3"	"R1"	"B2"	4	"G20"	1	4	7.74
+"S3"	"R1"	"B3"	5	"G05"	1	5	8.23
+"S3"	"R1"	"B3"	6	"G19"	1	6	6.34
+"S3"	"R1"	"B4"	7	"G01"	1	7	6.63
+"S3"	"R1"	"B4"	8	"G04"	1	8	6
+"S3"	"R1"	"B5"	9	"G14"	1	9	6.72
+"S3"	"R1"	"B5"	10	"G17"	1	10	5.12
+"S3"	"R1"	"B6"	11	"G06"	1	11	5.58
+"S3"	"R1"	"B6"	12	"G18"	2	11	8.13
+"S3"	"R1"	"B7"	13	"G03"	2	10	6.65
+"S3"	"R1"	"B7"	14	"G09"	2	9	7.94
+"S3"	"R1"	"B8"	15	"G12"	2	8	6.34
+"S3"	"R1"	"B8"	16	"G13"	2	7	8.12
+"S3"	"R1"	"B9"	17	"G22"	2	6	5.05
+"S3"	"R1"	"B9"	18	"G02"	2	5	6.99
+"S3"	"R1"	"B10"	19	"G15"	2	4	5.41
+"S3"	"R1"	"B10"	20	"G10"	2	3	5.48
+"S3"	"R1"	"B11"	21	"G07"	2	2	6.16
+"S3"	"R1"	"B11"	22	"G11"	2	1	6.01
+"S3"	"R2"	"B12"	23	"G06"	3	1	5.97
+"S3"	"R2"	"B12"	24	"G04"	3	2	5.32
+"S3"	"R2"	"B13"	25	"G18"	3	3	7.04
+"S3"	"R2"	"B13"	26	"G14"	3	4	6.29
+"S3"	"R2"	"B14"	27	"G15"	3	5	5.36
+"S3"	"R2"	"B14"	28	"G12"	3	6	7.03
+"S3"	"R2"	"B15"	29	"G20"	3	7	5.8
+"S3"	"R2"	"B15"	30	"G02"	3	8	7.19
+"S3"	"R2"	"B16"	31	"G19"	3	9	6.99
+"S3"	"R2"	"B16"	32	"G07"	3	10	7.6
+"S3"	"R2"	"B17"	33	"G16"	3	11	6.49
+"S3"	"R2"	"B17"	34	"G10"	4	11	6.31
+"S3"	"R2"	"B18"	35	"G08"	4	10	7.75
+"S3"	"R2"	"B18"	36	"G11"	4	9	6.63
+"S3"	"R2"	"B19"	37	"G17"	4	8	5.77
+"S3"	"R2"	"B19"	38	"G13"	4	7	5.38
+"S3"	"R2"	"B20"	39	"G01"	4	6	7.06
+"S3"	"R2"	"B20"	40	"G21"	4	5	6.39
+"S3"	"R2"	"B21"	41	"G05"	4	4	8.78
+"S3"	"R2"	"B21"	42	"G03"	4	3	6.51
+"S3"	"R2"	"B22"	43	"G09"	4	2	8.7
+"S3"	"R2"	"B22"	44	"G22"	4	1	6.73
+"S3"	"R3"	"B23"	45	"G18"	5	1	9.18
+"S3"	"R3"	"B23"	46	"G01"	5	2	6.13
+"S3"	"R3"	"B24"	47	"G07"	5	3	7.99
+"S3"	"R3"	"B24"	48	"G16"	5	4	4.82
+"S3"	"R3"	"B25"	49	"G15"	5	5	6.65
+"S3"	"R3"	"B25"	50	"G08"	5	6	5.24
+"S3"	"R3"	"B26"	51	"G20"	5	7	3.78
+"S3"	"R3"	"B26"	52	"G04"	5	8	6.34
+"S3"	"R3"	"B27"	53	"G19"	5	9	6.14
+"S3"	"R3"	"B27"	54	"G09"	5	10	7.95
+"S3"	"R3"	"B28"	55	"G17"	5	11	5.44
+"S3"	"R3"	"B28"	56	"G06"	6	11	8.49
+"S3"	"R3"	"B29"	57	"G10"	6	10	8.52
+"S3"	"R3"	"B29"	58	"G13"	6	9	6.71
+"S3"	"R3"	"B30"	59	"G12"	6	8	6.8
+"S3"	"R3"	"B30"	60	"G14"	6	7	8.13
+"S3"	"R3"	"B31"	61	"G02"	6	6	6.89
+"S3"	"R3"	"B31"	62	"G03"	6	5	5.88
+"S3"	"R3"	"B32"	63	"G11"	6	4	6.33
+"S3"	"R3"	"B32"	64	"G05"	6	3	6.4
+"S3"	"R3"	"B33"	65	"G22"	6	2	4.01
+"S3"	"R3"	"B33"	66	"G21"	6	1	6.64
+"S4"	"R1"	"B1"	1	"G11"	1	1	0.63
+"S4"	"R1"	"B1"	2	"G19"	1	2	1.15
+"S4"	"R1"	"B2"	3	"G21"	1	3	0.82
+"S4"	"R1"	"B2"	4	"G02"	1	4	2.29
+"S4"	"R1"	"B3"	5	"G16"	1	5	0.77
+"S4"	"R1"	"B3"	6	"G22"	1	6	1.47
+"S4"	"R1"	"B4"	7	"G05"	1	7	0.37
+"S4"	"R1"	"B4"	8	"G04"	1	8	0.82
+"S4"	"R1"	"B5"	9	"G10"	1	9	0.6
+"S4"	"R1"	"B5"	10	"G13"	1	10	0.63
+"S4"	"R1"	"B6"	11	"G14"	1	11	0.2
+"S4"	"R1"	"B6"	12	"G08"	2	11	0.09
+"S4"	"R1"	"B7"	13	"G07"	2	10	0.19
+"S4"	"R1"	"B7"	14	"G09"	2	9	1.21
+"S4"	"R1"	"B8"	15	"G01"	2	8	1.28
+"S4"	"R1"	"B8"	16	"G20"	2	7	1.08
+"S4"	"R1"	"B9"	17	"G15"	2	6	0.95
+"S4"	"R1"	"B9"	18	"G12"	2	5	1.78
+"S4"	"R1"	"B10"	19	"G18"	2	4	1.56
+"S4"	"R1"	"B10"	20	"G06"	2	3	1.37
+"S4"	"R1"	"B11"	21	"G03"	2	2	2.02
+"S4"	"R1"	"B11"	22	"G17"	2	1	0.39
+"S4"	"R2"	"B12"	23	"G16"	3	1	0.61
+"S4"	"R2"	"B12"	24	"G04"	3	2	0.5
+"S4"	"R2"	"B13"	25	"G20"	3	3	1.35
+"S4"	"R2"	"B13"	26	"G18"	3	4	1.21
+"S4"	"R2"	"B14"	27	"G15"	3	5	0.2
+"S4"	"R2"	"B14"	28	"G06"	3	6	0.57
+"S4"	"R2"	"B15"	29	"G17"	3	7	0.38
+"S4"	"R2"	"B15"	30	"G13"	3	8	1.24
+"S4"	"R2"	"B16"	31	"G21"	3	9	1.05
+"S4"	"R2"	"B16"	32	"G08"	3	10	0.57
+"S4"	"R2"	"B17"	33	"G14"	3	11	0.37
+"S4"	"R2"	"B17"	34	"G09"	4	11	0.89
+"S4"	"R2"	"B18"	35	"G11"	4	10	1.1
+"S4"	"R2"	"B18"	36	"G02"	4	9	2.66
+"S4"	"R2"	"B19"	37	"G19"	4	8	0.82
+"S4"	"R2"	"B19"	38	"G10"	4	7	1.11
+"S4"	"R2"	"B20"	39	"G07"	4	6	0.57
+"S4"	"R2"	"B20"	40	"G22"	4	5	1.57
+"S4"	"R2"	"B21"	41	"G05"	4	4	1.48
+"S4"	"R2"	"B21"	42	"G12"	4	3	0.8
+"S4"	"R2"	"B22"	43	"G01"	4	2	2.01
+"S4"	"R2"	"B22"	44	"G03"	4	1	1.78
+"S4"	"R3"	"B23"	45	"G20"	5	1	2.04
+"S4"	"R3"	"B23"	46	"G14"	5	2	0.55
+"S4"	"R3"	"B24"	47	"G18"	5	3	0.6
+"S4"	"R3"	"B24"	48	"G08"	5	4	0.2
+"S4"	"R3"	"B25"	49	"G03"	5	5	1.24
+"S4"	"R3"	"B25"	50	"G07"	5	6	0.41
+"S4"	"R3"	"B26"	51	"G15"	5	7	0.19
+"S4"	"R3"	"B26"	52	"G02"	5	8	0.63
+"S4"	"R3"	"B27"	53	"G12"	5	9	0.61
+"S4"	"R3"	"B27"	54	"G11"	5	10	1.05
+"S4"	"R3"	"B28"	55	"G17"	5	11	0.19
+"S4"	"R3"	"B28"	56	"G22"	6	11	1.26
+"S4"	"R3"	"B29"	57	"G01"	6	10	1.47
+"S4"	"R3"	"B29"	58	"G09"	6	9	0.2
+"S4"	"R3"	"B30"	59	"G19"	6	8	0.4
+"S4"	"R3"	"B30"	60	"G05"	6	7	0.59
+"S4"	"R3"	"B31"	61	"G16"	6	6	0.8
+"S4"	"R3"	"B31"	62	"G13"	6	5	1.02
+"S4"	"R3"	"B32"	63	"G06"	6	4	0.3
+"S4"	"R3"	"B32"	64	"G21"	6	3	0.2
+"S4"	"R3"	"B33"	65	"G04"	6	2	0.6
+"S4"	"R3"	"B33"	66	"G10"	6	1	1.67
diff -Nru agridat-1.17/data/jayaraman.bamboo.txt agridat-1.18/data/jayaraman.bamboo.txt
--- agridat-1.17/data/jayaraman.bamboo.txt	1970-01-01 00:00:00.000000000 +0000
+++ agridat-1.18/data/jayaraman.bamboo.txt	2020-08-24 18:08:39.000000000 +0000
@@ -0,0 +1,217 @@
+"loc"	"block"	"tree"	"family"	"height"
+"Vellanikkara"	"B1"	"T1"	"F1"	142
+"Vellanikkara"	"B1"	"T2"	"F1"	95
+"Vellanikkara"	"B1"	"T3"	"F1"	138
+"Vellanikkara"	"B1"	"T4"	"F1"	53
+"Vellanikkara"	"B1"	"T4"	"F1"	95
+"Vellanikkara"	"B1"	"T6"	"F1"	128
+"Nailambur"	"B1"	"T1"	"F1"	24
+"Nailambur"	"B1"	"T2"	"F1"	58
+"Nailambur"	"B1"	"T3"	"F1"	32
+"Nailambur"	"B1"	"T4"	"F1"	27
+"Nailambur"	"B1"	"T4"	"F1"	60
+"Nailambur"	"B1"	"T6"	"F1"	75
+"Vellanikkara"	"B2"	"T1"	"F1"	185
+"Vellanikkara"	"B2"	"T2"	"F1"	117
+"Vellanikkara"	"B2"	"T3"	"F1"	135
+"Vellanikkara"	"B2"	"T4"	"F1"	155
+"Vellanikkara"	"B2"	"T4"	"F1"	152
+"Vellanikkara"	"B2"	"T6"	"F1"	111
+"Nailambur"	"B2"	"T1"	"F1"	87
+"Nailambur"	"B2"	"T2"	"F1"	102
+"Nailambur"	"B2"	"T3"	"F1"	74
+"Nailambur"	"B2"	"T4"	"F1"	102
+"Nailambur"	"B2"	"T4"	"F1"	20
+"Nailambur"	"B2"	"T6"	"F1"	80
+"Vellanikkara"	"B3"	"T1"	"F1"	134
+"Vellanikkara"	"B3"	"T2"	"F1"	35
+"Vellanikkara"	"B3"	"T3"	"F1"	128
+"Vellanikkara"	"B3"	"T4"	"F1"	89
+"Vellanikkara"	"B3"	"T4"	"F1"	99
+"Vellanikkara"	"B3"	"T6"	"F1"	29
+"Nailambur"	"B3"	"T1"	"F1"	54
+"Nailambur"	"B3"	"T2"	"F1"	92
+"Nailambur"	"B3"	"T3"	"F1"	89
+"Nailambur"	"B3"	"T4"	"F1"	144
+"Nailambur"	"B3"	"T4"	"F1"	100
+"Nailambur"	"B3"	"T6"	"F1"	92
+"Vellanikkara"	"B1"	"T1"	"F2"	104
+"Vellanikkara"	"B1"	"T2"	"F2"	77
+"Vellanikkara"	"B1"	"T3"	"F2"	129
+"Vellanikkara"	"B1"	"T4"	"F2"	126
+"Vellanikkara"	"B1"	"T4"	"F2"	68
+"Vellanikkara"	"B1"	"T6"	"F2"	48
+"Nailambur"	"B1"	"T1"	"F2"	18
+"Nailambur"	"B1"	"T2"	"F2"	50
+"Nailambur"	"B1"	"T3"	"F2"	82
+"Nailambur"	"B1"	"T4"	"F2"	23
+"Nailambur"	"B1"	"T4"	"F2"	56
+"Nailambur"	"B1"	"T6"	"F2"	61
+"Vellanikkara"	"B2"	"T1"	"F2"	129
+"Vellanikkara"	"B2"	"T2"	"F2"	131
+"Vellanikkara"	"B2"	"T3"	"F2"	135
+"Vellanikkara"	"B2"	"T4"	"F2"	88
+"Vellanikkara"	"B2"	"T4"	"F2"	75
+"Vellanikkara"	"B2"	"T6"	"F2"	41
+"Nailambur"	"B2"	"T1"	"F2"	26
+"Nailambur"	"B2"	"T2"	"F2"	103
+"Nailambur"	"B2"	"T3"	"F2"	55
+"Nailambur"	"B2"	"T4"	"F2"	43
+"Nailambur"	"B2"	"T4"	"F2"	100
+"Nailambur"	"B2"	"T6"	"F2"	98
+"Vellanikkara"	"B3"	"T1"	"F2"	53
+"Vellanikkara"	"B3"	"T2"	"F2"	82
+"Vellanikkara"	"B3"	"T3"	"F2"	71
+"Vellanikkara"	"B3"	"T4"	"F2"	43
+"Vellanikkara"	"B3"	"T4"	"F2"	71
+"Vellanikkara"	"B3"	"T6"	"F2"	26
+"Nailambur"	"B3"	"T1"	"F2"	58
+"Nailambur"	"B3"	"T2"	"F2"	47
+"Nailambur"	"B3"	"T3"	"F2"	33
+"Nailambur"	"B3"	"T4"	"F2"	108
+"Nailambur"	"B3"	"T4"	"F2"	70
+"Nailambur"	"B3"	"T6"	"F2"	46
+"Vellanikkara"	"B1"	"T1"	"F3"	152
+"Vellanikkara"	"B1"	"T2"	"F3"	98
+"Vellanikkara"	"B1"	"T3"	"F3"	85
+"Vellanikkara"	"B1"	"T4"	"F3"	118
+"Vellanikkara"	"B1"	"T4"	"F3"	25
+"Vellanikkara"	"B1"	"T6"	"F3"	51
+"Nailambur"	"B1"	"T1"	"F3"	18
+"Nailambur"	"B1"	"T2"	"F3"	24
+"Nailambur"	"B1"	"T3"	"F3"	38
+"Nailambur"	"B1"	"T4"	"F3"	65
+"Nailambur"	"B1"	"T4"	"F3"	46
+"Nailambur"	"B1"	"T6"	"F3"	104
+"Vellanikkara"	"B2"	"T1"	"F3"	78
+"Vellanikkara"	"B2"	"T2"	"F3"	161
+"Vellanikkara"	"B2"	"T3"	"F3"	121
+"Vellanikkara"	"B2"	"T4"	"F3"	124
+"Vellanikkara"	"B2"	"T4"	"F3"	118
+"Vellanikkara"	"B2"	"T6"	"F3"	61
+"Nailambur"	"B2"	"T1"	"F3"	78
+"Nailambur"	"B2"	"T2"	"F3"	57
+"Nailambur"	"B2"	"T3"	"F3"	60
+"Nailambur"	"B2"	"T4"	"F3"	26
+"Nailambur"	"B2"	"T4"	"F3"	59
+"Nailambur"	"B2"	"T6"	"F3"	70
+"Vellanikkara"	"B3"	"T1"	"F3"	145
+"Vellanikkara"	"B3"	"T2"	"F3"	86
+"Vellanikkara"	"B3"	"T3"	"F3"	141
+"Vellanikkara"	"B3"	"T4"	"F3"	156
+"Vellanikkara"	"B3"	"T4"	"F3"	121
+"Vellanikkara"	"B3"	"T6"	"F3"	55
+"Nailambur"	"B3"	"T1"	"F3"	87
+"Nailambur"	"B3"	"T2"	"F3"	93
+"Nailambur"	"B3"	"T3"	"F3"	70
+"Nailambur"	"B3"	"T4"	"F3"	47
+"Nailambur"	"B3"	"T4"	"F3"	26
+"Nailambur"	"B3"	"T6"	"F3"	40
+"Vellanikkara"	"B1"	"T1"	"F4"	111
+"Vellanikkara"	"B1"	"T2"	"F4"	29
+"Vellanikkara"	"B1"	"T3"	"F4"	64
+"Vellanikkara"	"B1"	"T4"	"F4"	52
+"Vellanikkara"	"B1"	"T4"	"F4"	19
+"Vellanikkara"	"B1"	"T6"	"F4"	25
+"Nailambur"	"B1"	"T1"	"F4"	31
+"Nailambur"	"B1"	"T2"	"F4"	26
+"Nailambur"	"B1"	"T3"	"F4"	30
+"Nailambur"	"B1"	"T4"	"F4"	86
+"Nailambur"	"B1"	"T4"	"F4"	20
+"Nailambur"	"B1"	"T6"	"F4"	28
+"Vellanikkara"	"B2"	"T1"	"F4"	28
+"Vellanikkara"	"B2"	"T2"	"F4"	26
+"Vellanikkara"	"B2"	"T3"	"F4"	25
+"Vellanikkara"	"B2"	"T4"	"F4"	76
+"Vellanikkara"	"B2"	"T4"	"F4"	43
+"Vellanikkara"	"B2"	"T6"	"F4"	86
+"Nailambur"	"B2"	"T1"	"F4"	25
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+"Nailambur"	"B2"	"T3"	"F4"	52
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+"Nailambur"	"B2"	"T6"	"F4"	97
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+"Vellanikkara"	"B3"	"T2"	"F4"	32
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+"Vellanikkara"	"B3"	"T4"	"F4"	182
+"Vellanikkara"	"B3"	"T4"	"F4"	22
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+"Nailambur"	"B3"	"T1"	"F4"	17
+"Nailambur"	"B3"	"T2"	"F4"	23
+"Nailambur"	"B3"	"T3"	"F4"	29
+"Nailambur"	"B3"	"T4"	"F4"	30
+"Nailambur"	"B3"	"T4"	"F4"	87
+"Nailambur"	"B3"	"T6"	"F4"	31
+"Vellanikkara"	"B1"	"T1"	"F5"	23
+"Vellanikkara"	"B1"	"T2"	"F5"	48
+"Vellanikkara"	"B1"	"T3"	"F5"	88
+"Vellanikkara"	"B1"	"T4"	"F5"	27
+"Vellanikkara"	"B1"	"T4"	"F5"	26
+"Vellanikkara"	"B1"	"T6"	"F5"	26
+"Nailambur"	"B1"	"T1"	"F5"	95
+"Nailambur"	"B1"	"T2"	"F5"	42
+"Nailambur"	"B1"	"T3"	"F5"	43
+"Nailambur"	"B1"	"T4"	"F5"	76
+"Nailambur"	"B1"	"T4"	"F5"	41
+"Nailambur"	"B1"	"T6"	"F5"	49
+"Vellanikkara"	"B2"	"T1"	"F5"	35
+"Vellanikkara"	"B2"	"T2"	"F5"	21
+"Vellanikkara"	"B2"	"T3"	"F5"	14
+"Vellanikkara"	"B2"	"T4"	"F5"	34
+"Vellanikkara"	"B2"	"T4"	"F5"	49
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+"Nailambur"	"B2"	"T1"	"F5"	29
+"Nailambur"	"B2"	"T2"	"F5"	72
+"Nailambur"	"B2"	"T3"	"F5"	83
+"Nailambur"	"B2"	"T4"	"F5"	40
+"Nailambur"	"B2"	"T4"	"F5"	24
+"Nailambur"	"B2"	"T6"	"F5"	54
+"Vellanikkara"	"B3"	"T1"	"F5"	72
+"Vellanikkara"	"B3"	"T2"	"F5"	113
+"Vellanikkara"	"B3"	"T3"	"F5"	37
+"Vellanikkara"	"B3"	"T4"	"F5"	19
+"Vellanikkara"	"B3"	"T4"	"F5"	24
+"Vellanikkara"	"B3"	"T6"	"F5"	20
+"Nailambur"	"B3"	"T1"	"F5"	25
+"Nailambur"	"B3"	"T2"	"F5"	30
+"Nailambur"	"B3"	"T3"	"F5"	26
+"Nailambur"	"B3"	"T4"	"F5"	36
+"Nailambur"	"B3"	"T4"	"F5"	24
+"Nailambur"	"B3"	"T6"	"F5"	37
+"Vellanikkara"	"B1"	"T1"	"F6"	153
+"Vellanikkara"	"B1"	"T2"	"F6"	51
+"Vellanikkara"	"B1"	"T3"	"F6"	181
+"Vellanikkara"	"B1"	"T4"	"F6"	212
+"Vellanikkara"	"B1"	"T4"	"F6"	161
+"Vellanikkara"	"B1"	"T6"	"F6"	210
+"Nailambur"	"B1"	"T1"	"F6"	57
+"Nailambur"	"B1"	"T2"	"F6"	94
+"Nailambur"	"B1"	"T3"	"F6"	77
+"Nailambur"	"B1"	"T4"	"F6"	39
+"Nailambur"	"B1"	"T4"	"F6"	82
+"Nailambur"	"B1"	"T6"	"F6"	29
+"Vellanikkara"	"B2"	"T1"	"F6"	140
+"Vellanikkara"	"B2"	"T2"	"F6"	79
+"Vellanikkara"	"B2"	"T3"	"F6"	158
+"Vellanikkara"	"B2"	"T4"	"F6"	93
+"Vellanikkara"	"B2"	"T4"	"F6"	151
+"Vellanikkara"	"B2"	"T6"	"F6"	171
+"Nailambur"	"B2"	"T1"	"F6"	54
+"Nailambur"	"B2"	"T2"	"F6"	56
+"Nailambur"	"B2"	"T3"	"F6"	29
+"Nailambur"	"B2"	"T4"	"F6"	67
+"Nailambur"	"B2"	"T4"	"F6"	42
+"Nailambur"	"B2"	"T6"	"F6"	47
+"Vellanikkara"	"B3"	"T1"	"F6"	109
+"Vellanikkara"	"B3"	"T2"	"F6"	50
+"Vellanikkara"	"B3"	"T3"	"F6"	64
+"Vellanikkara"	"B3"	"T4"	"F6"	82
+"Vellanikkara"	"B3"	"T4"	"F6"	77
+"Vellanikkara"	"B3"	"T6"	"F6"	123
+"Nailambur"	"B3"	"T1"	"F6"	38
+"Nailambur"	"B3"	"T2"	"F6"	38
+"Nailambur"	"B3"	"T3"	"F6"	36
+"Nailambur"	"B3"	"T4"	"F6"	72
+"Nailambur"	"B3"	"T4"	"F6"	106
+"Nailambur"	"B3"	"T6"	"F6"	61
diff -Nru agridat-1.17/data/nair.turmeric.uniformity.txt agridat-1.18/data/nair.turmeric.uniformity.txt
--- agridat-1.17/data/nair.turmeric.uniformity.txt	1970-01-01 00:00:00.000000000 +0000
+++ agridat-1.18/data/nair.turmeric.uniformity.txt	2020-07-31 20:51:34.000000000 +0000
@@ -0,0 +1,865 @@
+"row"	"col"	"yield"
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+7	3	78
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+2	4	67
+3	4	20
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diff -Nru agridat-1.17/data/shafi.tomato.uniformity.txt agridat-1.18/data/shafi.tomato.uniformity.txt
--- agridat-1.17/data/shafi.tomato.uniformity.txt	1970-01-01 00:00:00.000000000 +0000
+++ agridat-1.18/data/shafi.tomato.uniformity.txt	2020-07-30 21:45:42.000000000 +0000
@@ -0,0 +1,201 @@
+"row"	"col"	"yield"
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diff -Nru agridat-1.17/debian/changelog agridat-1.18/debian/changelog
--- agridat-1.17/debian/changelog	2020-08-03 13:13:30.000000000 +0000
+++ agridat-1.18/debian/changelog	2021-01-12 17:32:11.000000000 +0000
@@ -1,16 +1,23 @@
-agridat (1.17-1cran1.2004.0) focal; urgency=medium
+agridat (1.18-1cran1.2004.0) focal; urgency=medium
 
-  * Compilation for Ubuntu 20.04 LTS
+  * Compilation for Ubuntu 20.04.1 LTS
   * Build for c2d4u for R 4.0.0 plus
   * Focal only build amd64 packages for Launchpad
 
- -- Michael Rutter <marutter@gmail.com>  Mon, 03 Aug 2020 13:13:30 +0000
+ -- Michael Rutter <marutter@gmail.com>  Tue, 12 Jan 2021 17:32:11 +0000
+
+agridat (1.18-1cran1) testing; urgency=low
+
+  * cran2deb svn: 362M with DB version 1.
+
+ -- cran2deb4ubuntu <cran2deb4ubuntu@gmail.com>  Tue, 12 Jan 2021 11:10:30 -0500
+
 
 agridat (1.17-1cran1) testing; urgency=low
 
   * cran2deb svn: 362M with DB version 1.
 
- -- cran2deb4ubuntu <cran2deb4ubuntu@gmail.com>  Mon, 03 Aug 2020 08:24:43 -0400
+ -- cran2deb4ubuntu <cran2deb4ubuntu@gmail.com>  Mon, 03 Aug 2020 08:24:54 -0400
 
 
 agridat (1.16-1cran1) testing; urgency=low
diff -Nru agridat-1.17/debian/copyright agridat-1.18/debian/copyright
--- agridat-1.17/debian/copyright	2020-08-03 12:24:43.000000000 +0000
+++ agridat-1.18/debian/copyright	2021-01-12 16:10:30.000000000 +0000
@@ -2,7 +2,7 @@
 automatically using cran2deb4ubuntu by cran2deb4ubuntu
 <cran2deb4ubuntu@gmail.com>.
 
-The original GNU R package is Copyright (C) 2020 Kevin Wright [aut,
+The original GNU R package is Copyright (C) 2021 Kevin Wright [aut,
 cre] (<https://orcid.org/0000-0002-0617-8673>) and possibly others.
 
 The original GNU R package is maintained by Kevin Wright
diff -Nru agridat-1.17/DESCRIPTION agridat-1.18/DESCRIPTION
--- agridat-1.17/DESCRIPTION	2020-08-03 10:10:06.000000000 +0000
+++ agridat-1.18/DESCRIPTION	2021-01-12 09:40:21.000000000 +0000
@@ -1,6 +1,6 @@
 Package: agridat
 Title: Agricultural Datasets
-Version: 1.17
+Version: 1.18
 Authors@R: person("Kevin","Wright", email="kw.stat@gmail.com",
                   comment=c(ORCID = "0000-0002-0617-8673"),
                   role=c("aut","cre"))
@@ -19,11 +19,12 @@
 URL: http://kwstat.github.io/agridat/
 BugReports: https://github.com/kwstat/agridat/issues
 VignetteBuilder: knitr
+Language: en-US
 Encoding: UTF-8
 RoxygenNote: 7.1.1
 NeedsCompilation: no
-Packaged: 2020-07-30 20:44:08 UTC; wrightkevi
+Packaged: 2021-01-11 23:23:06 UTC; wrightkevi
 Author: Kevin Wright [aut, cre] (<https://orcid.org/0000-0002-0617-8673>)
 Maintainer: Kevin Wright <kw.stat@gmail.com>
 Repository: CRAN
-Date/Publication: 2020-08-03 10:10:06 UTC
+Date/Publication: 2021-01-12 09:40:21 UTC
diff -Nru agridat-1.17/inst/doc/agridat_data.html agridat-1.18/inst/doc/agridat_data.html
--- agridat-1.17/inst/doc/agridat_data.html	2020-07-30 20:43:54.000000000 +0000
+++ agridat-1.18/inst/doc/agridat_data.html	2021-01-11 23:22:58.000000000 +0000
@@ -12,10 +12,23 @@
 
 <meta name="author" content="Kevin Wright" />
 
-<meta name="date" content="2020-07-30" />
+<meta name="date" content="2021-01-11" />
 
 <title>Additional sources of agricultural data</title>
 
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+
 
 
 
@@ -231,7 +253,7 @@
 
 <h1 class="title toc-ignore">Additional sources of agricultural data</h1>
 <h4 class="author">Kevin Wright</h4>
-<h4 class="date">2020-07-30</h4>
+<h4 class="date">2021-01-11</h4>
 
 
 
@@ -239,7 +261,7 @@
 <h1>Books</h1>
 <div id="die-landwirtschaftlichen-versuchs-stations" class="section level3">
 <h3><em>Die Landwirtschaftlichen Versuchs-Stations</em></h3>
-<p><a href="http://catalog.hathitrust.org/Record/000549685" class="uri">http://catalog.hathitrust.org/Record/000549685</a></p>
+<p><a href="https://catalog.hathitrust.org/Record/000549685" class="uri">https://catalog.hathitrust.org/Record/000549685</a></p>
 <p>Full view of research station reports 1859-1920. In German.</p>
 </div>
 <div id="d.-f.-andrews-and-a.-m.-herzberg-1985.-data." class="section level3">
@@ -253,8 +275,7 @@
 </div>
 <div id="d.-bayisa-2010.-application-of-spatial-mixed-model-in-agricultural-field-experiment." class="section level3">
 <h3>D. Bayisa (2010). <em>Application of Spatial Mixed Model in Agricultural Field Experiment</em>.</h3>
-<p>Master thesis. Department of Statistics, Addis Ababa University.</p>
-<p>One dataset from wheat, RCB, with field coordinates.</p>
+<p>Master thesis. Department of Statistics, Addis Ababa University. One dataset from wheat, RCB, with field coordinates.</p>
 </div>
 <div id="m.-n.-das-narayan-c.-giri-1987.-design-and-analysis-of-experiments." class="section level3">
 <h3>M. N. Das &amp; Narayan C. Giri (1987). <em>Design and Analysis of Experiments</em>.</h3>
@@ -272,10 +293,10 @@
 </div>
 <div id="peter-diggle-patrick-heagerty-kung-yee-liang-scott-zeger.-analysis-of-longitudinal-data." class="section level3">
 <h3>Peter Diggle, Patrick Heagerty, Kung-Yee Liang, Scott Zeger. <em>Analysis of Longitudinal Data</em>.</h3>
-<p><a href="http://faculty.washington.edu/heagerty/Books/AnalysisLongitudinal/datasets.html" class="uri">http://faculty.washington.edu/heagerty/Books/AnalysisLongitudinal/datasets.html</a></p>
+<p><a href="https://faculty.washington.edu/heagerty/Books/AnalysisLongitudinal/datasets.html" class="uri">https://faculty.washington.edu/heagerty/Books/AnalysisLongitudinal/datasets.html</a></p>
 <p>Pig weight data is found in <code>SemiPar::pig.weights</code></p>
 <p>Sitka spruce data is found in: <code>geepack::spruce</code></p>
-<p>Milk protein data is found in: <code>nlme::Milk</code>. A thorough description of this data can be found in Molenberghs &amp; Kenward, <em>Missing Data in Clinical Studies</em>, p. 377. Original source: A. P. Verbyla and B. R. Cullis, Modelling in Repeated Measures Experiments. <a href="http://www.jstor.org/stable/2347384" class="uri">http://www.jstor.org/stable/2347384</a></p>
+<p>Milk protein data is found in: <code>nlme::Milk</code>. A thorough description of this data can be found in Molenberghs &amp; Kenward, <em>Missing Data in Clinical Studies</em>, p. 377. Original source: A. P. Verbyla and B. R. Cullis, Modelling in Repeated Measures Experiments. <a href="https://www.jstor.org/stable/2347384" class="uri">https://www.jstor.org/stable/2347384</a></p>
 </div>
 <div id="federer-walt-1955.-experimental-design." class="section level3">
 <h3>Federer, Walt (1955). <em>Experimental Design</em>.</h3>
@@ -295,7 +316,7 @@
 </div>
 <div id="galwey-n.w.-2014.-introduction-to-mixed-modelling-2nd-ed." class="section level3">
 <h3>Galwey, N.W. (2014). <em>Introduction to Mixed Modelling</em>, 2nd ed.</h3>
-<p><a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1119945496.html" class="uri">http://www.wiley.com/WileyCDA/WileyTitle/productCd-1119945496.html</a></p>
+<p><a href="https://www.wiley.com/WileyCDA/WileyTitle/productCd-1119945496.html" class="uri">https://www.wiley.com/WileyCDA/WileyTitle/productCd-1119945496.html</a></p>
 <pre><code> 2  83 variety x nitro split-plot - agridat::yates.oats
  3 104 doubled-haploid barley
  3 135 wheat/rye competition, heritability
@@ -316,11 +337,11 @@
 </div>
 <div id="kwanchai-a.-gomez-gomez-1984.-statistical-procedures-for-agricultural-research." class="section level3">
 <h3>Kwanchai A. Gomez &amp; Gomez (1984). <em>Statistical Procedures for Agricultural Research</em>.</h3>
-<p>Extensive collection of datasets from rice experiments.</p>
+<p>Extensive collection of datasets from rice experiments. Many added to agridat.</p>
 </div>
 <div id="cyril-h.-goulden-methods-of-statistical-analysis." class="section level3">
 <h3>Cyril H. Goulden, <em>Methods of Statistical Analysis</em>.</h3>
-<p>First edition: <a href="http://archive.org/details/methodsofstatist031744mbp" class="uri">http://archive.org/details/methodsofstatist031744mbp</a></p>
+<p>First edition: <a href="https://archive.org/details/methodsofstatist031744mbp" class="uri">https://archive.org/details/methodsofstatist031744mbp</a></p>
 <pre><code> 18 Uniformity trial - agridat::goulden.barley.uniformity 
 153 Split-split plot with factorial sub-plot treatment - agridat::goulden.splitsplit 
 194 Incomplete block 
@@ -485,7 +506,7 @@
 </div>
 <div id="oliver-schabenberger-and-francis-j.-pierce.-contemporary-statistical-models-for-the-plant-and-soil-sciences." class="section level3">
 <h3>Oliver Schabenberger and Francis J. Pierce. <em>Contemporary Statistical Models for the Plant and Soil Sciences</em>.</h3>
-<p>Many datasets</p>
+<p>Many datasets. Some added to agridat.</p>
 </div>
 <div id="s.-j.-welham-et-al.-2015.-statistical-methods-in-biology." class="section level3">
 <h3>S. J. Welham et al. (2015). <em>Statistical Methods In Biology</em>.</h3>
@@ -504,12 +525,10 @@
 </div>
 <div id="cyverse-data-commons" class="section level3">
 <h3>CyVerse Data Commons</h3>
-<p><a href="http://datacommons.cyverse.org/" class="uri">http://datacommons.cyverse.org/</a></p>
-<p><a href="http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated" class="uri">http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated</a></p>
+<p><a href="https://datacommons.cyverse.org/" class="uri">https://datacommons.cyverse.org/</a> <a href="https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated" class="uri">https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated</a></p>
 </div>
 <div id="datadryad" class="section level3">
 <h3>DataDryad</h3>
-<p><a href="http://datadryad.org/" class="uri">http://datadryad.org/</a></p>
 </div>
 <div id="harvard-dataverse" class="section level3">
 <h3>Harvard Dataverse</h3>
@@ -518,15 +537,14 @@
 </div>
 <div id="nature-scientific-data" class="section level3">
 <h3>Nature Scientific Data</h3>
-<p><a href="http://www.nature.com/sdata/" class="uri">http://www.nature.com/sdata/</a></p>
+<p><a href="https://www.nature.com/sdata/" class="uri">https://www.nature.com/sdata/</a></p>
 </div>
 <div id="open-data-journal-for-agricultural-research" class="section level3">
 <h3>Open Data Journal for Agricultural Research</h3>
-<p><a href="http://library.wur.nl/ojs/index.php/odjar/" class="uri">http://library.wur.nl/ojs/index.php/odjar/</a></p>
+<p><a href="https://library.wur.nl/ojs/index.php/odjar/" class="uri">https://library.wur.nl/ojs/index.php/odjar/</a></p>
 </div>
 <div id="plant-genomics-and-phenomics-research-data-repository" class="section level3">
 <h3>Plant Genomics and Phenomics Research Data Repository</h3>
-<p><a href="https://edal-pgp.ipk-gatersleben.de/" class="uri">https://edal-pgp.ipk-gatersleben.de/</a></p>
 </div>
 <div id="wolfram-data-repository" class="section level3">
 <h3>Wolfram Data Repository</h3>
@@ -537,7 +555,7 @@
 <h1>Journals - Bulletins</h1>
 <div id="iowa-state-agricultural-research-bulletins" class="section level3">
 <h3>Iowa State Agricultural Research Bulletins</h3>
-<p><a href="http://lib.dr.iastate.edu/ag_researchbulletins/" class="uri">http://lib.dr.iastate.edu/ag_researchbulletins/</a></p>
+<p><a href="https://lib.dr.iastate.edu/ag_researchbulletins/" class="uri">https://lib.dr.iastate.edu/ag_researchbulletins/</a></p>
 <pre><code>Vol 26/ 281. Cox: Analysis of Lattice and Triple Lattice.
 Page 11: Lattice, 81 hybs, 4 reps 
 Page 24: Triple lattice, 81 hybs, 6 reps
@@ -581,38 +599,38 @@
 </div>
 <div id="papers" class="section level1">
 <h1>Papers</h1>
-<p><strong>Xavier, Alencar et al.</strong>. Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population, <a href="http://www.g3journal.org/content/8/2/519" class="uri">http://www.g3journal.org/content/8/2/519</a></p>
+<p><strong>Xavier, Alencar et al.</strong>. Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population, <a href="https://www.g3journal.org/content/8/2/519" class="uri">https://www.g3journal.org/content/8/2/519</a></p>
 <p>Data are in the SoyNAM and NAM packages.</p>
-<p><strong>Barrero, Ivan D. et al</strong>. (2013). A multi-environment trial analysis shows slight grain yield improvement in Texas commercial maize. Field Crops Research, 149, Pages 167-176. <a href="http://doi.org/10.1016/j.fcr.2013.04.017" class="uri">http://doi.org/10.1016/j.fcr.2013.04.017</a></p>
+<p><strong>Barrero, Ivan D. et al</strong>. (2013). A multi-environment trial analysis shows slight grain yield improvement in Texas commercial maize. Field Crops Research, 149, Pages 167-176. <a href="https://doi.org/10.1016/j.fcr.2013.04.017" class="uri">https://doi.org/10.1016/j.fcr.2013.04.017</a></p>
 <p>This is a large (14500 records), multi-year, multi-location, 10-trait data. Sent a note encouraging the authors to formally publish the data. Source: <a href="http://maizeandgenetics.tamu.edu/CTP/CTP.html" class="uri">http://maizeandgenetics.tamu.edu/CTP/CTP.html</a></p>
-<p><strong>Cleveland, M.A. and John M. Hickey, Selma Forni</strong> (2012). A Common Dataset for Genomic Analysis of Livestock Populations. G3, 2, 429-435. <a href="http://doi.org/10.1534/g3.111.001453" class="uri">http://doi.org/10.1534/g3.111.001453</a></p>
+<p><strong>Cleveland, M.A. and John M. Hickey, Selma Forni</strong> (2012). A Common Dataset for Genomic Analysis of Livestock Populations. G3, 2, 429-435. <a href="https://doi.org/10.1534/g3.111.001453" class="uri">https://doi.org/10.1534/g3.111.001453</a></p>
 <p>The supplemental information for this paper contains data for 3534 pigs with high-density genotypes (50000 SNPs), and a pedigree including parents and grandparents of the animals.</p>
-<p><strong>Daillant-Spinnler</strong> (1996). Relationships between perceived sensory properties and major preference directions of 12 variaties of apples from the southern hemisphere. Food Quality and Preference, 7(2), 113-126. <a href="http://dx.doi.org/10.1016/0950-3293(95)00043-7" class="uri">http://dx.doi.org/10.1016/0950-3293(95)00043-7</a></p>
+<p><strong>Daillant-Spinnler</strong> (1996). Relationships between perceived sensory properties and major preference directions of 12 variaties of apples from the southern hemisphere. Food Quality and Preference, 7(2), 113-126. <a href="https://dx.doi.org/10.1016/0950-3293(95)00043-7" class="uri">https://dx.doi.org/10.1016/0950-3293(95)00043-7</a></p>
 <p>The data are in <code>ClustVarLV::apples_sh$pref</code> and <code>ClustVarLV::apples_sh$senso</code> 12 apple varieties, 43 traits, 60 consumers</p>
 <p><strong>Gregory, Crowther &amp; Lambert</strong> (1932). The interrelation of factors controlling the production of cotton under irrigation in the Sudan. Jour Agric Sci, 22, p. 617.</p>
 <p><strong>Hedrick</strong> (1920). Twenty years of fertilizers in an apple orchard. <a href="https://books.google.com/books?hl=en&amp;lr=&amp;id=SqlJAAAAMAAJ&amp;oi=fnd&amp;pg=PA446" class="uri">https://books.google.com/books?hl=en&amp;lr=&amp;id=SqlJAAAAMAAJ&amp;oi=fnd&amp;pg=PA446</a></p>
 <p>The authors found no significant differences between fertilizer treatments.</p>
 <p><strong>Meehan &amp; Gratton</strong> (2016). A Landscape View of Agricultural Insecticide Use across the Conterminous US from 1997 through 2012. PLOS ONE, <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166724" class="uri">https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166724</a></p>
 <p>Supplemental material contains county-level data for each of 4 years. Complete R-INLA code for analysis.</p>
-<p><strong>Monteverde et al</strong> Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (Oryza sativa L.) Grown in Subtropical Areas <a href="https://doi.org/10.1534/g3.119.400064" class="uri">https://doi.org/10.1534/g3.119.400064</a> <a href="https://gsajournals.figshare.com/articles/Supplemental_Material_for_Monteverde_et_al_2019/7685636" class="uri">https://gsajournals.figshare.com/articles/Supplemental_Material_for_Monteverde_et_al_2019/7685636</a></p>
+<p><strong>Monteverde et al</strong> Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (Oryza sativa L.) Grown in Subtropical Areas <a href="https://doi.org/10.1534/g3.119.400064" class="uri">https://doi.org/10.1534/g3.119.400064</a> <a href="https://gsajournals.figshare.com/articles/dataset/Supplemental_Material_for_Monteverde_et_al_2019/7685636" class="uri">https://gsajournals.figshare.com/articles/dataset/Supplemental_Material_for_Monteverde_et_al_2019/7685636</a></p>
 <p>Supplemental information contains phenotypic data and markers and environmental covariates for PLS analysis.</p>
 <p><strong>Roger W. Hexem, Earl O.Heady, Metin Caglar</strong> (1974) A compendium of experimental data for corn, wheat, cotton and sugar beets grown at selected sites in the western United States and alternative production functions fitted to these data. Technical report: Center for Agricultural and Rural Development, Iowa State University. <a href="https://babel.hathitrust.org/cgi/pt?id=wu.89031116783;view=1up;seq=3" class="uri">https://babel.hathitrust.org/cgi/pt?id=wu.89031116783;view=1up;seq=3</a></p>
 <p>The technical report provides data from experiments on corn, wheat, cotton &amp; sugar beets, each crop tested at several locations over two years, with a factorial structure on irrigation and nitrogen treatments, with replications. Three polynomial functions were fit to the data for each location (quadratic, square root, three-halves).</p>
 <p><strong>Kenward, Michael G.</strong> (1987). A Method for Comparing Profiles of Repeated Measurements. Applied Statistics, 36, 296-308.</p>
 <p>An ante-dependence model is fit to repeated measures of cattle weight.</p>
-<p><strong>Klumper &amp; Qaim</strong> (2015). A Meta-Analysis of the Impacts of Genetically Modified Crops. <a href="http://doi.org/10.1371/journal.pone.0111629" class="uri">http://doi.org/10.1371/journal.pone.0111629</a></p>
+<p><strong>Klumper &amp; Qaim</strong> (2015). A Meta-Analysis of the Impacts of Genetically Modified Crops. <a href="https://doi.org/10.1371/journal.pone.0111629" class="uri">https://doi.org/10.1371/journal.pone.0111629</a></p>
 <p>Nice meta-analysis dataset. Published data only include differences, not standard-errors. See the comments on PLOS article for some peculiarities in the data.</p>
-<p><strong>Lado, B. et al.</strong> (2013). <em>Increased Genomic Prediction Accuracy in Wheat Breeding Through Spatial Adjustment of Field Trial Data</em>. G3, 3, 2105-2114. <a href="http://doi.org/10.1534/g3.113.007807" class="uri">http://doi.org/10.1534/g3.113.007807</a></p>
+<p><strong>Lado, B. et al.</strong> (2013). <em>Increased Genomic Prediction Accuracy in Wheat Breeding Through Spatial Adjustment of Field Trial Data</em>. G3, 3, 2105-2114. <a href="https://doi.org/10.1534/g3.113.007807" class="uri">https://doi.org/10.1534/g3.113.007807</a></p>
 <p>Has a large haplotype dataset (83 MB) and two-year phenotype data with multiple traits.</p>
-<p><strong>Payne, Roger</strong> (2015). The Design and Analysis of Long-Term Rotation Experiments. Agronomy Journal, 107, 772-784. <a href="http://doi.org/10.2134/agronj2012.0411" class="uri">http://doi.org/10.2134/agronj2012.0411</a></p>
+<p><strong>Payne, Roger</strong> (2015). The Design and Analysis of Long-Term Rotation Experiments. Agronomy Journal, 107, 772-784. <a href="https://doi.org/10.2134/agronj2012.0411" class="uri">https://doi.org/10.2134/agronj2012.0411</a></p>
 <p>The data and R code appeared in the paper. Free access, but closed copyright.</p>
-<p><strong>Snedecor, George and E. S. Haber</strong> (1946). Statistical Methods For an Incomplete Experiment on a Perennial Crop. Biometrics Bulletin, 2, 61-67. <a href="http://doi.org/10.2307/3001959" class="uri">http://doi.org/10.2307/3001959</a></p>
+<p><strong>Snedecor, George and E. S. Haber</strong> (1946). Statistical Methods For an Incomplete Experiment on a Perennial Crop. Biometrics Bulletin, 2, 61-67. <a href="https://doi.org/10.2307/3001959" class="uri">https://doi.org/10.2307/3001959</a></p>
 <p>Harvest of asparagus over 10 years, three cutting dates per year, 6 blocks.</p>
-<p><strong>Technow, Frank, et al.</strong> (2014). Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize. August 1, 2014 vol. 197 no. 4 1343-1355. <a href="http://doi.org/10.1534/genetics.114.165860" class="uri">http://doi.org/10.1534/genetics.114.165860</a></p>
+<p><strong>Technow, Frank, et al.</strong> (2014). Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize. August 1, 2014 vol. 197 no. 4 1343-1355. <a href="https://doi.org/10.1534/genetics.114.165860" class="uri">https://doi.org/10.1534/genetics.114.165860</a></p>
 <p>Genotype and phenotype data appears in the sommer package.</p>
-<p><strong>Tian, Ting</strong> (2015). Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data. <a href="http://doi.org/10.1371/journal.pone.0144370" class="uri">http://doi.org/10.1371/journal.pone.0144370</a></p>
+<p><strong>Tian, Ting</strong> (2015). Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data. <a href="https://doi.org/10.1371/journal.pone.0144370" class="uri">https://doi.org/10.1371/journal.pone.0144370</a></p>
 <p>Uses <code>agridat::australia.soybean</code> data and one other real dataset with 4 traits that are not identified. All data and code available.</p>
-<p><strong>Randall J. Wisser et al.</strong> (2011). Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene. PNAS. <a href="http://doi.org/10.1073/pnas.1011739108" class="uri">http://doi.org/10.1073/pnas.1011739108</a></p>
+<p><strong>Randall J. Wisser et al.</strong> (2011). Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene. PNAS. <a href="https://doi.org/10.1073/pnas.1011739108" class="uri">https://doi.org/10.1073/pnas.1011739108</a></p>
 <p>The supplement contains genotype data, but no phenotype data.</p>
 <p><strong>Rife et al.</strong> (2018) Genomic analysis and prediction within a US public collaborative winter wheat regional testing nursery. <a href="https://doi.org/10.5061/dryad.q968v83" class="uri">https://doi.org/10.5061/dryad.q968v83</a></p>
 <p>Large phenotypic dataset with 691 wheat lines, 33 years, 670 environments, 3-4 reps, 120000 datapoints. No genotypic data is included.</p>
@@ -689,6 +707,10 @@
 <h3>gRbase</h3>
 <p>Data <code>gRbase::carcass</code>: thickness of meat and fat on slaughter pigs</p>
 </div>
+<div id="lmdiallel" class="section level3">
+<h3>lmDiallel</h3>
+<p><a href="https://github.com/OnofriAndreaPG/lmDiallel/tree/master/data" class="uri">https://github.com/OnofriAndreaPG/lmDiallel/tree/master/data</a></p>
+</div>
 <div id="lmtest" class="section level3">
 <h3>lmtest</h3>
 <p>Data <code>lmtest::ChickEgg</code> time series of annual chicken and egg production in the United States 1930-1983.</p>
@@ -699,8 +721,7 @@
 </div>
 <div id="nlraa" class="section level3">
 <h3>nlraa</h3>
-<p><a href="http://r-forge.r-project.org/R/?group_id=1599" class="uri">http://r-forge.r-project.org/R/?group_id=1599</a></p>
-<p>Miguez. Non-linear models in agriculture. <code>nlraa::sm</code> = <code>agridat::miguez.biomass</code></p>
+<p>Miguez. Non-linear models in agriculture. <code>nlraa::sm</code> = <code>agridat::miguez.biomass</code> Vignettes and functions for working with (non)linear mixed models</p>
 </div>
 <div id="nlme" class="section level3">
 <h3>nlme</h3>
@@ -733,10 +754,19 @@
 <div id="sommer---solving-mixed-model-equations-in-r" class="section level3">
 <h3>sommer - Solving mixed model equations in R</h3>
 <p>Data: h2. Modest-sized GxE experiment in potato Data: cornHybrid. Yield/PLTHT for 100 hybrids from 20 inbred * 20 inbred, 4 locs. Phenotype and relationship matrix.</p>
-<p>Data: wheatLines CIMMYT wheat data for 599 lines. Phenotype and relationship data.</p>
+<p>Data:</p>
+<pre><code>data(DT_wheat) #  CIMMYT wheat data
+DT_wheat # 599 varieties, yield in 4 envts
+GT_wheat # 599 varieties, 1279 markers coded -1,1</code></pre>
 <p>Data: RICE</p>
 <p>Data: FDdata taken from agridat::bond.diallel</p>
-<p>Data: Technow_data. AF=Additive Flint. AD=Additive Dent. MF=Marker Flint. MD=Marker Dent. pheno=phenotype data for 1254 hybrids (GY=yield, GM=moisture). This data is from Technow et al: <a href="http://www.genetics.org/content/197/4/1343.supplemental" class="uri">http://www.genetics.org/content/197/4/1343.supplemental</a></p>
+<p>Data:</p>
+<pre><code>data(DT_technow) # From http://www.genetics.org/content/197/4/1343.supplemental
+DT &lt;- DT_technow  # 1254 hybs, parents, GY=yield, GM=moisture
+Md &lt;- Md_technow  # 123 dent parents, 35478 markers
+Mf &lt;- Mf_technow  # 86 flint parents, 37478 markers
+Ad &lt;- Ad_technow  # 123 x 123 A matrix 
+Af &lt;- Af_technow  # 86 x 85 A matrix</code></pre>
 </div>
 <div id="soynam---soybean-nested-association-mapping" class="section level3">
 <h3>SoyNAM - Soybean nested association mapping</h3>
@@ -774,11 +804,7 @@
 <h1>Web sites</h1>
 <div id="ars-oat-trials" class="section level3">
 <h3>ARS oat trials</h3>
-<p><a href="http://www.ars.usda.gov/Main/docs.htm?docid=8419&amp;page=4" class="uri">http://www.ars.usda.gov/Main/docs.htm?docid=8419&amp;page=4</a></p>
-</div>
-<div id="betydb" class="section level3">
-<h3>BETYdb</h3>
-<p><a href="https://www.betydb.org/" class="uri">https://www.betydb.org/</a> Biofuel Ecophysiological Traits and Yields Database</p>
+<p><a href="https://www.ars.usda.gov/Main/docs.htm?docid=8419&amp;page=4" class="uri">https://www.ars.usda.gov/Main/docs.htm?docid=8419&amp;page=4</a></p>
 </div>
 <div id="cimmyt-research-data" class="section level3">
 <h3>CIMMYT Research Data</h3>
@@ -787,7 +813,7 @@
 <div id="genomes-to-fields-g2f" class="section level3">
 <h3>Genomes To Fields (G2F)</h3>
 <p><a href="https://www.genomes2fields.org/publications/" class="uri">https://www.genomes2fields.org/publications/</a></p>
-<p><a href="http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3" class="uri">http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3</a></p>
+<p><a href="https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3" class="uri">https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3</a></p>
 <p>Very large GxE data here for 2014 and 2015. Hybrid &amp; inbred phenotype data, weather data, genomic data. Very nice.</p>
 </div>
 <div id="glten-global-long-term-metadal-portal" class="section level3">
@@ -797,23 +823,19 @@
 </div>
 <div id="google-dataset-search" class="section level3">
 <h3>Google dataset search</h3>
-<p><a href="https://toolbox.google.com/datasetsearch" class="uri">https://toolbox.google.com/datasetsearch</a></p>
+<p><a href="https://datasetsearch.research.google.com/" class="uri">https://datasetsearch.research.google.com/</a></p>
 </div>
 <div id="grain-genes" class="section level3">
 <h3>Grain genes</h3>
 <ol style="list-style-type: decimal">
 <li><p><a href="https://wheat.pw.usda.gov/ggpages/HxT/" class="uri">https://wheat.pw.usda.gov/ggpages/HxT/</a> The Harrington x TR306 Barley Mapping Population. The genotype and phenotype data comes from Mapmaker, but seems to be in a slightly non-standard format; 145 DH lines, 217 markers, 25 env, 1 rep.</p></li>
-<li><p><a href="https://wheat.pw.usda.gov/ggpages/SxM" class="uri">https://wheat.pw.usda.gov/ggpages/SxM</a>. This data is agridat::steptoe.morex.</p></li>
+<li><p><a href="https://wheat.pw.usda.gov/ggpages/SxM/" class="uri">https://wheat.pw.usda.gov/ggpages/SxM/</a> . This data is agridat::steptoe.morex.</p></li>
 </ol>
 </div>
 <div id="ideals" class="section level3">
 <h3>Ideals</h3>
 <p><a href="https://www.ideals.illinois.edu/handle/2142/3528" class="uri">https://www.ideals.illinois.edu/handle/2142/3528</a> Data File : Raw data from each ear analyzed each year of the Illinois long-term selection experiment for oil and protein in corn (1896-2004)</p>
 </div>
-<div id="illinois-corn-hybrid-variety-trials" class="section level3">
-<h3>Illinois Corn Hybrid Variety Trials</h3>
-<p><a href="http://vt.cropsci.illinois.edu/corn.html" class="uri">http://vt.cropsci.illinois.edu/corn.html</a></p>
-</div>
 <div id="ilri-international-livestock-research-institute" class="section level3">
 <h3>ILRI International Livestock Research Institute</h3>
 <p>Case study 4 is a nice diallel example with sheep data. Available as agridat::ilri.sheep</p>
@@ -826,7 +848,6 @@
 <div id="rothamsted-electronic-archive" class="section level3">
 <h3>Rothamsted Electronic Archive</h3>
 <p><a href="http://www.era.rothamsted.ac.uk/index.php" class="uri">http://www.era.rothamsted.ac.uk/index.php</a> Data from Broadbalk and other long-term experiments.</p>
-<p>Twitter: <a href="https://twitter.com/eRA_Curator" class="uri">https://twitter.com/eRA_Curator</a></p>
 <p>Github draft data: <a href="https://github.com/Rothamsted-Ecoinformatics/YieldbookDatasetDrafts" class="uri">https://github.com/Rothamsted-Ecoinformatics/YieldbookDatasetDrafts</a></p>
 </div>
 <div id="rothamsted-documents-archive" class="section level3">
@@ -866,12 +887,12 @@
 </div>
 <div id="terra-ref" class="section level3">
 <h3>Terra-Ref</h3>
-<p><a href="http://terraref.org/" class="uri">http://terraref.org/</a></p>
+<p><a href="https://terraref.org/" class="uri">https://terraref.org/</a></p>
 <p>Sensor observations, plant phenotypes, derived traits, genetic and genomic data. Beta version until Nov 2018.</p>
 </div>
 <div id="usda-national-agricultural-statistics-service" class="section level3">
 <h3>USDA National Agricultural Statistics Service</h3>
-<p><a href="http://www.nass.usda.gov" class="uri">http://www.nass.usda.gov</a> <a href="http://quickstats.nass.usda.gov/" class="uri">http://quickstats.nass.usda.gov/</a></p>
+<p><a href="https://www.nass.usda.gov" class="uri">https://www.nass.usda.gov</a> <a href="https://quickstats.nass.usda.gov/" class="uri">https://quickstats.nass.usda.gov/</a></p>
 <p>Group: Field Crops Commodity: Corn Category: Area Harvested, Yield Data Item: Corn grain Acres Harvested, Yield Bu/Ac Domain: Total Geography: State See <code>agridat::nass.corn</code>, nass.wheat, etc.</p>
 </div>
 </div>
diff -Nru agridat-1.17/inst/doc/agridat_data.Rmd agridat-1.18/inst/doc/agridat_data.Rmd
--- agridat-1.17/inst/doc/agridat_data.Rmd	2020-07-30 20:40:22.000000000 +0000
+++ agridat-1.18/inst/doc/agridat_data.Rmd	2021-01-11 21:49:28.000000000 +0000
@@ -13,7 +13,7 @@
 # Books
 
 ### _Die Landwirtschaftlichen Versuchs-Stations_
-http://catalog.hathitrust.org/Record/000549685
+https://catalog.hathitrust.org/Record/000549685
 
 Full view of research station reports 1859-1920. In German.
 
@@ -30,8 +30,8 @@
 ```
 
 ### D. Bayisa (2010). _Application of Spatial Mixed Model in Agricultural Field Experiment_.
-Master thesis. Department of Statistics, Addis Ababa University.
 
+Master thesis. Department of Statistics, Addis Ababa University.
 One dataset from wheat, RCB, with field coordinates.
 
 
@@ -51,15 +51,16 @@
 279 maize covariate, yield & plant count, 4 rep, 32 obs
 ```
 
+
 ### Peter Diggle, Patrick Heagerty, Kung-Yee Liang, Scott Zeger. _Analysis of Longitudinal Data_.
-http://faculty.washington.edu/heagerty/Books/AnalysisLongitudinal/datasets.html
+https://faculty.washington.edu/heagerty/Books/AnalysisLongitudinal/datasets.html
 
 Pig weight data is found in `SemiPar::pig.weights`
 
 Sitka spruce data is found in: `geepack::spruce`
 
 Milk protein data is found in: `nlme::Milk`.  A thorough description of this data can be found in Molenberghs & Kenward, _Missing Data in Clinical Studies_, p. 377.
-Original source: A. P. Verbyla and B. R. Cullis, Modelling in Repeated Measures Experiments. http://www.jstor.org/stable/2347384 
+Original source: A. P. Verbyla and B. R. Cullis, Modelling in Repeated Measures Experiments. https://www.jstor.org/stable/2347384 
 
 
 ### Federer, Walt (1955). _Experimental Design_.
@@ -83,7 +84,7 @@
 
 
 ### Galwey, N.W. (2014). _Introduction to Mixed Modelling_, 2nd ed.
-http://www.wiley.com/WileyCDA/WileyTitle/productCd-1119945496.html
+https://www.wiley.com/WileyCDA/WileyTitle/productCd-1119945496.html
 
 ```
  2  83 variety x nitro split-plot - agridat::yates.oats
@@ -110,11 +111,11 @@
 
 ### Kwanchai A. Gomez & Gomez (1984). _Statistical Procedures for Agricultural Research_.
 
-Extensive collection of datasets from rice experiments.
+Extensive collection of datasets from rice experiments. Many added to agridat.
 
 
 ### Cyril H. Goulden, _Methods of Statistical Analysis_.
-First edition: http://archive.org/details/methodsofstatist031744mbp 
+First edition: https://archive.org/details/methodsofstatist031744mbp 
 
 ```
  18 Uniformity trial - agridat::goulden.barley.uniformity 
@@ -325,7 +326,7 @@
 
 ### Oliver Schabenberger and Francis J. Pierce. _Contemporary Statistical Models for the Plant and Soil Sciences_.
 
-Many datasets
+Many datasets. Some added to agridat.
 
 
 ### S. J. Welham et al. (2015). _Statistical Methods In Biology_.
@@ -334,6 +335,7 @@
 
 
 ### Pesticides in the Nation's Streams and Ground Water, 1992-2001
+
 Extensive data for detection of pesticides in water samples.  See Appendix 5 and Appendix 6 of the supporting info.
 https://water.usgs.gov/nawqa/pnsp/pubs/circ1291/supporting_info.php
 
@@ -345,16 +347,15 @@
 https://data.nal.usda.gov/about-ag-data-commons
 https://data.nal.usda.gov/search/type/dataset
 
-### CyVerse Data Commons
 
-http://datacommons.cyverse.org/
+### CyVerse Data Commons
 
-http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated
+https://datacommons.cyverse.org/
+https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated
 
 
 
 ### DataDryad
-http://datadryad.org/
 
 
 ### Harvard Dataverse
@@ -364,15 +365,14 @@
 https://dataverse.harvard.edu/dataverse/RiceResearch
 
 ### Nature Scientific Data
-http://www.nature.com/sdata/
+https://www.nature.com/sdata/
 
 
 ### Open Data Journal for Agricultural Research
-http://library.wur.nl/ojs/index.php/odjar/
+https://library.wur.nl/ojs/index.php/odjar/
 
 
 ### Plant Genomics and Phenomics Research Data Repository
-https://edal-pgp.ipk-gatersleben.de/
 
 
 ### Wolfram Data Repository 
@@ -382,7 +382,7 @@
 # Journals - Bulletins
 
 ### Iowa State Agricultural Research Bulletins
-http://lib.dr.iastate.edu/ag_researchbulletins/
+https://lib.dr.iastate.edu/ag_researchbulletins/
 ```
 Vol 26/ 281. Cox: Analysis of Lattice and Triple Lattice.
 Page 11: Lattice, 81 hybs, 4 reps 
@@ -430,14 +430,14 @@
 
 **Xavier, Alencar et al.**.
 Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population,
-http://www.g3journal.org/content/8/2/519
+https://www.g3journal.org/content/8/2/519
 
 Data are in the SoyNAM and NAM packages.
 
 **Barrero, Ivan D. et al**. (2013).
 A multi-environment trial analysis shows slight grain yield improvement in Texas commercial maize.
 Field Crops Research, 149, Pages 167-176.
-http://doi.org/10.1016/j.fcr.2013.04.017
+https://doi.org/10.1016/j.fcr.2013.04.017
 
 This is a large (14500 records), multi-year, multi-location, 10-trait data. Sent a note encouraging the authors to formally publish the data.
 Source: http://maizeandgenetics.tamu.edu/CTP/CTP.html
@@ -446,14 +446,14 @@
 **Cleveland, M.A. and John M. Hickey, Selma Forni** (2012).
 A Common Dataset for Genomic Analysis of Livestock Populations.
 G3, 2, 429-435.
-http://doi.org/10.1534/g3.111.001453
+https://doi.org/10.1534/g3.111.001453
 
 The supplemental information for this paper contains data for 3534 pigs with high-density genotypes (50000 SNPs), and a pedigree including parents and grandparents of the animals.
 
 
 **Daillant-Spinnler** (1996). Relationships between perceived sensory properties and major preference directions of 12 variaties of apples from the southern hemisphere.
 Food Quality and Preference, 7(2), 113-126.
-http://dx.doi.org/10.1016/0950-3293(95)00043-7
+https://dx.doi.org/10.1016/0950-3293(95)00043-7
 
 The data are in `ClustVarLV::apples_sh$pref` and `ClustVarLV::apples_sh$senso`
 12 apple varieties, 43 traits, 60 consumers
@@ -481,7 +481,7 @@
 **Monteverde et al**
 Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (Oryza sativa L.) Grown in Subtropical Areas
 https://doi.org/10.1534/g3.119.400064 
-https://gsajournals.figshare.com/articles/Supplemental_Material_for_Monteverde_et_al_2019/7685636
+https://gsajournals.figshare.com/articles/dataset/Supplemental_Material_for_Monteverde_et_al_2019/7685636
 
 Supplemental information contains phenotypic data and markers and environmental covariates for PLS analysis.
 
@@ -503,7 +503,7 @@
 
 **Klumper & Qaim** (2015).
 A Meta-Analysis of the Impacts of Genetically Modified Crops.
-http://doi.org/10.1371/journal.pone.0111629
+https://doi.org/10.1371/journal.pone.0111629
 
 Nice meta-analysis dataset.  Published data only include differences, not standard-errors.
 See the comments on PLOS article for some peculiarities in the data.
@@ -511,7 +511,7 @@
 
 **Lado, B. et al.** (2013). 
 *Increased Genomic Prediction Accuracy in Wheat Breeding Through Spatial Adjustment of Field Trial Data*.  G3, 3, 2105-2114.
-http://doi.org/10.1534/g3.113.007807
+https://doi.org/10.1534/g3.113.007807
 
 Has a large haplotype dataset (83 MB) and two-year phenotype data with multiple traits.
 
@@ -519,7 +519,7 @@
 **Payne, Roger** (2015). 
 The Design and Analysis of Long-Term Rotation Experiments.
 Agronomy Journal, 107, 772-784.
-http://doi.org/10.2134/agronj2012.0411
+https://doi.org/10.2134/agronj2012.0411
 
 The data and R code appeared in the paper. Free access, but closed copyright.
 
@@ -527,7 +527,7 @@
 **Snedecor, George and E. S. Haber** (1946).
 Statistical Methods For an Incomplete Experiment on a Perennial Crop.
 Biometrics Bulletin, 2, 61-67.
-http://doi.org/10.2307/3001959
+https://doi.org/10.2307/3001959
 
 Harvest of asparagus over 10 years, three cutting dates per year, 6 blocks.
 
@@ -535,20 +535,20 @@
 **Technow, Frank, et al.** (2014).
 Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize.
 August 1, 2014 vol. 197 no. 4 1343-1355.
-http://doi.org/10.1534/genetics.114.165860
+https://doi.org/10.1534/genetics.114.165860
 
 Genotype and phenotype data appears in the sommer package.
 
 
 **Tian, Ting** (2015).
 Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data.
-http://doi.org/10.1371/journal.pone.0144370
+https://doi.org/10.1371/journal.pone.0144370
 
 Uses `agridat::australia.soybean` data and one other real dataset with 4 traits that are not identified.  All data and code available.
 
 
 **Randall J. Wisser et al.** (2011). 
-Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene. PNAS.  http://doi.org/10.1073/pnas.1011739108
+Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene. PNAS.  https://doi.org/10.1073/pnas.1011739108
 
 The supplement contains genotype data, but no phenotype data.
 
@@ -656,6 +656,11 @@
 
 Data `gRbase::carcass`: thickness of meat and fat on slaughter pigs
 
+
+### lmDiallel
+https://github.com/OnofriAndreaPG/lmDiallel/tree/master/data
+
+
 ### lmtest
 
 Data `lmtest::ChickEgg` time series of annual chicken and egg production in the United States 1930-1983.
@@ -667,10 +672,10 @@
 
 
 ### nlraa
-http://r-forge.r-project.org/R/?group_id=1599
 
 Miguez. Non-linear models in agriculture.
 `nlraa::sm` = `agridat::miguez.biomass`
+Vignettes and functions for working with (non)linear mixed models
 
    
 ### nlme
@@ -712,14 +717,27 @@
 
 Data: h2. Modest-sized GxE experiment in potato Data: cornHybrid. Yield/PLTHT for 100 hybrids from 20 inbred * 20 inbred, 4 locs. Phenotype and relationship matrix.
 
-Data: wheatLines  CIMMYT wheat data for 599 lines. Phenotype and relationship data.
+Data: 
+```
+data(DT_wheat) #  CIMMYT wheat data
+DT_wheat # 599 varieties, yield in 4 envts
+GT_wheat # 599 varieties, 1279 markers coded -1,1
+```
 
 Data: RICE
 
 Data: FDdata taken from agridat::bond.diallel
 
-Data: Technow_data. AF=Additive Flint. AD=Additive Dent. MF=Marker Flint. MD=Marker Dent. pheno=phenotype data for 1254 hybrids (GY=yield, GM=moisture).  This data is from Technow et al:
-http://www.genetics.org/content/197/4/1343.supplemental
+Data:
+```
+data(DT_technow) # From http://www.genetics.org/content/197/4/1343.supplemental
+DT <- DT_technow  # 1254 hybs, parents, GY=yield, GM=moisture
+Md <- Md_technow  # 123 dent parents, 35478 markers
+Mf <- Mf_technow  # 86 flint parents, 37478 markers
+Ad <- Ad_technow  # 123 x 123 A matrix 
+Af <- Af_technow  # 86 x 85 A matrix
+```
+
 
 
 ### SoyNAM - Soybean nested association mapping 
@@ -766,12 +784,7 @@
 # Web sites
 
 ### ARS oat trials
-http://www.ars.usda.gov/Main/docs.htm?docid=8419&page=4
-
-
-### BETYdb
-https://www.betydb.org/
-Biofuel Ecophysiological Traits and Yields Database
+https://www.ars.usda.gov/Main/docs.htm?docid=8419&page=4
 
 
 ### CIMMYT Research Data
@@ -782,7 +795,7 @@
 ### Genomes To Fields (G2F) 
 https://www.genomes2fields.org/publications/
 
-http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3
+https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3
     
 Very large GxE data here for 2014 and 2015. Hybrid & inbred phenotype data, weather data, genomic data.  Very nice.
 
@@ -796,7 +809,7 @@
 
 
 ### Google dataset search
-https://toolbox.google.com/datasetsearch
+https://datasetsearch.research.google.com/
 
 
 ### Grain genes
@@ -804,7 +817,7 @@
 1. https://wheat.pw.usda.gov/ggpages/HxT/
 The Harrington x TR306 Barley Mapping Population. The genotype and phenotype data comes from Mapmaker, but seems to be in a slightly non-standard format; 145 DH lines, 217 markers, 25 env, 1 rep.
 
-2. https://wheat.pw.usda.gov/ggpages/SxM. This data is agridat::steptoe.morex.
+2. https://wheat.pw.usda.gov/ggpages/SxM/ . This data is agridat::steptoe.morex.
 
 
 
@@ -813,10 +826,6 @@
 Data File : Raw data from each ear analyzed each year of the Illinois long-term selection experiment for oil and protein in corn (1896-2004)
 
 
-### Illinois Corn Hybrid Variety Trials
-http://vt.cropsci.illinois.edu/corn.html
-
-
 ### ILRI International Livestock Research Institute
 
 Case study 4 is a nice diallel example with sheep data.
@@ -837,8 +846,6 @@
 http://www.era.rothamsted.ac.uk/index.php
 Data from Broadbalk and other long-term experiments.
 
-Twitter: https://twitter.com/eRA_Curator
-
 Github draft data: https://github.com/Rothamsted-Ecoinformatics/YieldbookDatasetDrafts
 
 
@@ -885,15 +892,15 @@
 
 
 ### Terra-Ref
-http://terraref.org/
+https://terraref.org/
 
 Sensor observations, plant phenotypes, derived traits, genetic and genomic data.
 Beta version until Nov 2018.
 
 
 ### USDA National Agricultural Statistics Service
-http://www.nass.usda.gov
-http://quickstats.nass.usda.gov/
+https://www.nass.usda.gov
+https://quickstats.nass.usda.gov/
 
 Group:       Field Crops
 Commodity:   Corn
diff -Nru agridat-1.17/inst/doc/agridat_examples.html agridat-1.18/inst/doc/agridat_examples.html
--- agridat-1.17/inst/doc/agridat_examples.html	2020-07-30 20:44:05.000000000 +0000
+++ agridat-1.18/inst/doc/agridat_examples.html	2021-01-11 23:23:05.000000000 +0000
@@ -12,10 +12,23 @@
 
 <meta name="author" content="Kevin Wright" />
 
-<meta name="date" content="2020-07-30" />
+<meta name="date" content="2021-01-11" />
 
 <title>Graphical Gems in the agridat Package</title>
 
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
 <script>// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
 // v0.0.1
 // Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
@@ -33,95 +46,16 @@
 });
 </script>
 
+<style type="text/css">
+  code{white-space: pre-wrap;}
+  span.smallcaps{font-variant: small-caps;}
+  span.underline{text-decoration: underline;}
+  div.column{display: inline-block; vertical-align: top; width: 50%;}
+  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+  ul.task-list{list-style: none;}
+      </style>
 
-<style type="text/css">code{white-space: pre;}</style>
-<style type="text/css" data-origin="pandoc">
-code.sourceCode > span { display: inline-block; line-height: 1.25; }
-code.sourceCode > span { color: inherit; text-decoration: inherit; }
-code.sourceCode > span:empty { height: 1.2em; }
-.sourceCode { overflow: visible; }
-code.sourceCode { white-space: pre; position: relative; }
-div.sourceCode { margin: 1em 0; }
-pre.sourceCode { margin: 0; }
-@media screen {
-div.sourceCode { overflow: auto; }
-}
-@media print {
-code.sourceCode { white-space: pre-wrap; }
-code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
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@@ -319,7 +253,7 @@
 
 <h1 class="title toc-ignore">Graphical Gems in the agridat Package</h1>
 <h4 class="author">Kevin Wright</h4>
-<h4 class="date">2020-07-30</h4>
+<h4 class="date">2021-01-11</h4>
 
 
 
@@ -327,23 +261,16 @@
 <p>This vignette presents graphical views of a few of the datasets in this package.</p>
 <div id="setup" class="section level1">
 <h1>Setup</h1>
-<p>This exhibit of agricultural data uses the following packages.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">library</span>(<span class="st">&quot;agridat&quot;</span>)</span>
-<span id="cb1-2"><a href="#cb1-2"></a><span class="kw">library</span>(<span class="st">&quot;desplot&quot;</span>)</span>
-<span id="cb1-3"><a href="#cb1-3"></a><span class="kw">library</span>(<span class="st">&quot;gge&quot;</span>)</span>
-<span id="cb1-4"><a href="#cb1-4"></a><span class="kw">library</span>(<span class="st">&quot;HH&quot;</span>)</span>
-<span id="cb1-5"><a href="#cb1-5"></a><span class="kw">library</span>(<span class="st">&quot;lattice&quot;</span>)</span>
-<span id="cb1-6"><a href="#cb1-6"></a><span class="kw">library</span>(<span class="st">&quot;latticeExtra&quot;</span>)</span>
-<span id="cb1-7"><a href="#cb1-7"></a><span class="kw">library</span>(<span class="st">&quot;mapproj&quot;</span>)</span>
-<span id="cb1-8"><a href="#cb1-8"></a><span class="kw">library</span>(<span class="st">&quot;maps&quot;</span>)</span>
-<span id="cb1-9"><a href="#cb1-9"></a><span class="kw">library</span>(<span class="st">&quot;reshape2&quot;</span>)</span></code></pre></div>
+<p>This exhibit of agricultural data uses the following packages: <code>agridat</code>, <code>desplot</code>, <code>gge</code>, <code>HH</code>, <code>lattice</code>, <code>latticeExtra</code>, <code>mapproj</code>, <code>maps</code>, <code>reshape2</code>.</p>
 </div>
 <div id="potato-blight-incidence-over-space-and-time" class="section level1">
 <h1>Potato blight incidence over space and time</h1>
-<pre><code>## Please use desplot(data,form) instead of desplot(form,data)</code></pre>
+<pre><code>## Loading required package: desplot</code></pre>
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" /><!-- --></p>
 <p><span class="citation">Lee (2009)</span> analyzed a large dataset to evaluate the resistance of potato varieties to blight. This data contains evaluations of a changing set of varieties every two years, evaluated in 5 blocks, repeatedly throughout the growing season to track the progress of the disease. Each panel shows a field map on the given date, with a separate row of panels for each year.</p>
 <p>Would you include field spatial trends in a model for these data?</p>
+<pre><code>## Loading required package: latticeExtra</code></pre>
+<pre><code>## Loading required package: lattice</code></pre>
 <p><img 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" /><!-- --></p>
 <p>In 1983, 20 varieties were evaluated in 5 blocks (shown by colored numbers) throughout the growing season for disease resistance. Resistance scores start at 9 for all varieties (shown in panels). As the growing season progresses, the ‘I.HARDY’ variety succumbs quickly to blight, while ‘IWA’ succumbs steadily, and ‘064.1’ resists blight until near the end of the season.</p>
 <p>Does this view show differences between blocks?</p>
@@ -363,13 +290,25 @@
 </div>
 <div id="verification-of-experiment-layout" class="section level1">
 <h1>Verification of experiment layout</h1>
-<pre><code>## Please use desplot(data,form) instead of desplot(form,data)</code></pre>
 <p><img 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" /><!-- --></p>
 <p><span class="citation">Gomez and Gomez (1984)</span> provide data for an experiment with 3 reps, 6 genotypes, 3 levels of nitrogen and 2 planting dates. The experiment layout is putatively a ‘’split strip-plot’’. To verify the design, the <code>desplot</code> package is used for plotting the design of field experiments.</p>
 <p>How is the design different from a ‘’split-split-plot’’ design?</p>
 </div>
 <div id="visualizing-main-effects-two-way-interactions" class="section level1">
 <h1>Visualizing main effects, two-way interactions</h1>
+<pre><code>## Loading required package: HH</code></pre>
+<pre><code>## Loading required package: grid</code></pre>
+<pre><code>## Loading required package: multcomp</code></pre>
+<pre><code>## Loading required package: mvtnorm</code></pre>
+<pre><code>## Loading required package: survival</code></pre>
+<pre><code>## Loading required package: TH.data</code></pre>
+<pre><code>## Loading required package: MASS</code></pre>
+<pre><code>## 
+## Attaching package: &#39;TH.data&#39;</code></pre>
+<pre><code>## The following object is masked from &#39;package:MASS&#39;:
+## 
+##     geyser</code></pre>
+<pre><code>## Loading required package: gridExtra</code></pre>
 <p><img 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" /><!-- --></p>
 <p><span class="citation">Heiberger and Holland (2004)</span> provide an interesting way to use lattice graphics to visualize the main effects (using boxplots) and interactions (using interaction plots) in data. Rice yield is plotted versus replication, nitrogen, management type, and genotype variety. Box plots show minor differences between reps, increaing yield due to nitrogen, high yield from intensive management, and large differences between varieties.</p>
 <p>Do you think interaction plots show interaction (lack of parallelism)?</p>
@@ -404,11 +343,13 @@
 </div>
 <div id="nebraska-farming-income-choropleth" class="section level1">
 <h1>Nebraska farming income choropleth</h1>
+<pre><code>## Loading required package: maps</code></pre>
+<pre><code>## Loading required package: mapproj</code></pre>
 <p><img 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" /><!-- --></p>
 <p>The Red-Blue palette in the <code>RColorBrewer</code> package is a divergent palette with light colors near the middle of the scale. This can cause problems when there are missing values, which appear as white (technically, the background). In order to increase the visibility of missing values, the <code>agridat</code> package uses a Red-Gray-Blue palette, with a gray color that is dark enough to clearly distinguish missing values.</p>
 <p>How does the outlier county (Butler) in northeast Nebraska limit interpration of spatial patterns in the data?</p>
 <p><img 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" /><!-- --></p>
-<p>Because counties are different sizes, the second graphic uses an income rate per square mile. Because of the outlier, it might be smart to use percentile break points, but doing so hides the outlier. Instead, the break points are calulated using a method called Fisher-Jenks. These break points show both the outlier and the spatial patterns. It is now easy to see that northwest (Sandhills) Nebraska has low farming income, especially for crops. Counties with missing data are white, which is easily distinguished from gray.</p>
+<p>Because counties are different sizes, the second graphic uses an income rate per square mile. Because of the outlier, it might be smart to use percentile break points, but doing so hides the outlier. Instead, the break points are calculated using a method called Fisher-Jenks. These break points show both the outlier and the spatial patterns. It is now easy to see that northwest (Sandhills) Nebraska has low farming income, especially for crops. Counties with missing data are white, which is easily distinguished from gray.</p>
 <p>Where are farm incomes highest? Why?</p>
 </div>
 <div id="las-rosas-yield-monitor" class="section level1">
@@ -425,8 +366,8 @@
 <p>Which states were in the corn belt in 2000?</p>
 </div>
 <div id="references" class="section level1 unnumbered">
-<h1>References</h1>
-<div id="refs" class="references">
+<h1 class="unnumbered">References</h1>
+<div id="refs" class="references hanging-indent">
 <div id="ref-anselin2004spatial">
 <p>Anselin, Luc, Rodolfo Bongiovanni, and Jess Lowenberg-DeBoer. 2004. “A Spatial Econometric Approach to the Economics of Site-Specific Nitrogen Management in Corn Production.” <em>American Journal of Agricultural Economics</em> 86 (3): 675–87. <a href="https://doi.org/10.1111/j.0002-9092.2004.00610.x">https://doi.org/10.1111/j.0002-9092.2004.00610.x</a>.</p>
 </div>
diff -Nru agridat-1.17/inst/doc/agridat_examples.R agridat-1.18/inst/doc/agridat_examples.R
--- agridat-1.17/inst/doc/agridat_examples.R	2020-07-30 20:44:04.000000000 +0000
+++ agridat-1.18/inst/doc/agridat_examples.R	2021-01-11 23:23:04.000000000 +0000
@@ -2,18 +2,8 @@
 knitr::opts_chunk$set(echo=FALSE, fig.height = 5, fig.width = 5)
 options(width=90)
 
-## ----packs, eval=TRUE, message=FALSE, echo=TRUE-----------------------------------------
-library("agridat")
-library("desplot")
-library("gge")
-library("HH")
-library("lattice")
-library("latticeExtra")
-library("mapproj")
-library("maps")
-library("reshape2")
-
 ## ----lee1, eval=TRUE, fig.height=7.5, fig.width=5---------------------------------------
+library(agridat)
 data(lee.potatoblight)
 dat <- lee.potatoblight
 # Note the progression to lower scores as time passes in each year
@@ -27,27 +17,32 @@
          rep(0,5),1,1,1,1,1,
          rep(0,5),1,1,1,1,1,
          rep(0,5),1,1,1,1,1)
-require(desplot)
-desplot(y ~ col*row|date, dat,
-        main="lee.potatoblight", #col.regions=RedGrayBlue,
-        between=list(y=.3), strip.cex =.6,
-        layout=c(10,11), skip=as.logical(skp))
+if(require("desplot")){
+  desplot(dat, y ~ col*row|date,
+          main="lee.potatoblight", #col.regions=RedGrayBlue,
+          between=list(y=.3), strip.cex =.6,
+          layout=c(10,11), skip=as.logical(skp))
+}
 
 ## ----lee2, eval=TRUE--------------------------------------------------------------------
+library(agridat)
 # 1983 only.  I.Hardy succumbs quickly
 dat <- lee.potatoblight
 dat$dd <- as.Date(dat$date)
 d83 <- droplevels(subset(dat, year==1983))
-foo <- xyplot(y ~ dd|gen, d83, group=rep,
-       xlab="Date", ylab="Blight resistance score",
-       main="lee.potatoblight 1983", as.table=TRUE,
-       par.settings=list(
-         superpose.symbol=list(col=c("black","red","royalblue","#009900","dark orange"),
-           pch=c("1","2","3","4","5"))),
-       scales=list(alternating=FALSE, x=list(rot=90, cex=.7)))
-foo + xyplot(y ~ dd|gen, d83, subset=year==1983, type='smooth', col='gray80')
+if(require("latticeExtra")){
+  foo <- xyplot(y ~ dd|gen, d83, group=rep,
+         xlab="Date", ylab="Blight resistance score",
+         main="lee.potatoblight 1983", as.table=TRUE,
+         par.settings=list(
+           superpose.symbol=list(col=c("black","red","royalblue","#009900","dark orange"),
+             pch=c("1","2","3","4","5"))),
+         scales=list(alternating=FALSE, x=list(rot=90, cex=.7)))
+  foo + xyplot(y ~ dd|gen, d83, subset=year==1983, type='smooth', col='gray80')
+}
 
 ## ----harrison, eval=TRUE, fig.height=6--------------------------------------------------
+library(agridat)
 data(harrison.priors)
 d1 <- subset(harrison.priors, substance=="daidzein")
 d1 <- d1[ , c("source","number","min","max")]
@@ -62,34 +57,36 @@
   out <- rbind(out, data.frame(source=d1[ii,'source'], vals=vals))
 }
 out <- droplevels(out) # Extra levels exist in d1
-foo0 <- dotplot(source ~ vals, out, 
-                main="harrison.priors", xlab="Daidzein level",
+if(require("latticeExtra")) {
+  foo0 <- dotplot(source ~ vals, out, 
+                  main="harrison.priors", xlab="Daidzein level",
+                  panel=function(x,y,...){
+                    panel.dotplot(x,y,...)
+                    #browser()
+                    # Minimum for each row
+                    x2l <- tapply(x, y, min)
+                    x2r <- tapply(x, y, max)
+                    y2 <- tapply(y, y, "[", 1)
+                    panel.xyplot(x2l, y2, pch=16, cex=1.5, col="navy")
+                    panel.xyplot(x2r, y2, pch=16, cex=1.5, col="navy")
+                  },
+                  # Hack.  Add blanks for extra space on graph
+                  ylim=c(levels(out$source),"","","","prior","Constructed","",""))
+  
+  # Now calculate parameters for a common lognormal distribution
+  mu0 <- mean(log(out$vals))
+  sd0 <- sd(log(out$vals))
+  xvals <- seq(0,2000, length=100)
+  library("latticeExtra")
+  foo0 + xyplot((19+4000*dlnorm(xvals, mu0, sd0))~xvals, type='l',
                 panel=function(x,y,...){
-                  panel.dotplot(x,y,...)
-                  #browser()
-                  # Minimum for each row
-                  x2l <- tapply(x, y, min)
-                  x2r <- tapply(x, y, max)
-                  y2 <- tapply(y, y, "[", 1)
-                  panel.xyplot(x2l, y2, pch=16, cex=1.5, col="navy")
-                  panel.xyplot(x2r, y2, pch=16, cex=1.5, col="navy")
-                },
-                # Hack.  Add blanks for extra space on graph
-                ylim=c(levels(out$source),"","","","prior","Constructed","",""))
-
-# Now calculate parameters for a common lognormal distribution
-mu0 <- mean(log(out$vals))
-sd0 <- sd(log(out$vals))
-xvals <- seq(0,2000, length=100)
-library("latticeExtra")
-foo0 + xyplot((19+4000*dlnorm(xvals, mu0, sd0))~xvals, type='l',
-              panel=function(x,y,...){
-                panel.xyplot(x,y,...)
-                panel.abline(h=19, col="gray90")
-              })
-
+                  panel.xyplot(x,y,...)
+                  panel.abline(h=19, col="gray90")
+                })
+}
 
 ## ----mead, eval=TRUE--------------------------------------------------------------------
+library(agridat)
 data(mead.germination)
 dat <- mead.germination
 # dat <- transform(dat, concf=factor(conc))
@@ -108,67 +105,71 @@
 newb <- expand.grid(temp=c('T1','T2','T3','T4'), logconc=log(c(0,.1,1,10)+.01))
 newb$pct <- predict(m6, new=newb, type='response')
 # Binomial density
-foob <- xyplot(pct~logconc |temp, newb,
-               xlim=c(-5.5, 4.5), ylim=c(-2, 53), as.table=TRUE,
-               xlab="Log concentration",
-               ylab="Seeds germinating (out of 50).  Binomial density.",
-               main="mead.germination", #layout=c(4,1),
-               panel=function(x,y,...){
-                 for(ix in 1:4){
-                   quan <- qbinom(c(.025, .975), size=50, prob=y[ix])
-                   yval <- seq(min(quan), max(quan), by=1)
-                   off <- x[ix]
-                   xl <- off + rep(0, length(yval))
-                   # Constant multiuplier of 8 chosen by trial and error
-                   xr <- off + 8 * dbinom(yval, size=50, prob=y[ix])
-                   panel.segments(xl,yval,xr, yval, cex=.35, lwd=3, col="gray70")
-                 }
-               })
-
-
-# Add mean response line with equally-spaced points on the log scale
-newl <- expand.grid(temp=c('T1','T2','T3','T4'),
-                   logconc=seq(log(.01), log(10.01), length=50))
-newl$pct <- predict(m6, new=newl, type='response')
-# Logistic curve
-fool <- xyplot(pct~logconc|temp, newl,
-               panel=function(x,y,...){
-                 panel.points(x, 50*y, type='l', col='blue')
-               })
-
-
-# Data points last, on top of everything
-food <- xyplot(germ~logconc|temp, dat, layout=c(4,1),
-       ylab="Seeds germinating (out of 50)", cex=1.5, pch=20, col='black')
-foob + fool + food
-
+if(require("latticeExtra")){
+  foob <- xyplot(pct~logconc |temp, newb,
+                 xlim=c(-5.5, 4.5), ylim=c(-2, 53), as.table=TRUE,
+                 xlab="Log concentration",
+                 ylab="Seeds germinating (out of 50).  Binomial density.",
+                 main="mead.germination", #layout=c(4,1),
+                 panel=function(x,y,...){
+                   for(ix in 1:4){
+                     quan <- qbinom(c(.025, .975), size=50, prob=y[ix])
+                     yval <- seq(min(quan), max(quan), by=1)
+                     off <- x[ix]
+                     xl <- off + rep(0, length(yval))
+                     # Constant multiuplier of 8 chosen by trial and error
+                     xr <- off + 8 * dbinom(yval, size=50, prob=y[ix])
+                     panel.segments(xl,yval,xr, yval, cex=.35, lwd=3, col="gray70")
+                   }
+                 })
+  
+  # Add mean response line with equally-spaced points on the log scale
+  newl <- expand.grid(temp=c('T1','T2','T3','T4'),
+                     logconc=seq(log(.01), log(10.01), length=50))
+  newl$pct <- predict(m6, new=newl, type='response')
+  # Logistic curve
+  fool <- xyplot(pct~logconc|temp, newl,
+                 panel=function(x,y,...){
+                   panel.points(x, 50*y, type='l', col='blue')
+                 })
+  
+  # Data points last, on top of everything
+  food <- xyplot(germ~logconc|temp, dat, layout=c(4,1),
+         ylab="Seeds germinating (out of 50)", cex=1.5, pch=20, col='black')
+  foob + fool + food
+}
 
 ## ----gomez, eval=TRUE-------------------------------------------------------------------
+library(agridat)
 data(gomez.stripsplitplot)
 dat <- gomez.stripsplitplot
 
 # Layout
-require(desplot)
-desplot(gen~col*row, dat,
-        out1=rep, col=nitro, text=planting, cex=1,
-        main="gomez.stripsplitplot")
+if(require("desplot")){
+  desplot(dat, gen~col*row,
+          out1=rep, col=nitro, text=planting, cex=1,
+          main="gomez.stripsplitplot")
+}
 
 ## ----gomez2, eval=TRUE------------------------------------------------------------------
+library(agridat)
 data(gomez.splitsplit)
 dat <- gomez.splitsplit
 dat$nitrogen <- factor(dat$nitro)
-require(HH)
-#position(dat$rep) <- position(dat$management) <-
-#  position(dat$gen) <- c(10,70,130)
-#position(dat$nitrogen) <- c(0,50,80,110,140)
-interaction2wt(yield~rep+nitrogen+management+gen, data=dat,
-               main="gomez.splitsplit",
-               x.between=0, y.between=0,
-               relation=list(x="free", y="same"),
-               rot=c(90,0), xlab="",
-               par.strip.text.input=list(cex=.8))
+if(require("HH")){
+  #position(dat$rep) <- position(dat$management) <-
+  #  position(dat$gen) <- c(10,70,130)
+  #position(dat$nitrogen) <- c(0,50,80,110,140)
+  interaction2wt(yield~rep+nitrogen+management+gen, data=dat,
+                 main="gomez.splitsplit",
+                 x.between=0, y.between=0,
+                 relation=list(x="free", y="same"),
+                 rot=c(90,0), xlab="",
+                 par.strip.text.input=list(cex=.8))
+}
 
 ## ----keen, eval=TRUE, fig.width=7, fig.height=7.5---------------------------------------
+library(agridat)
 data(keen.potatodamage)
 dat <- keen.potatodamage
 
@@ -182,81 +183,85 @@
 
 
 ## ----wright, eval=TRUE------------------------------------------------------------------
+library(agridat)
 data(minnesota.barley.yield)
 data(minnesota.barley.weather)
 dat <- minnesota.barley.yield
 datw <- minnesota.barley.weather
 
 # Weather trends over time
-library("latticeExtra")
-#useOuterStrips(xyplot(cdd~mo|year*site, datw, groups=year,
-#main="minnesota.barley", xlab="month", ylab="Cooling degree days",
-#subset=(mo > 3 & mo < 10), scales=list(alternating=FALSE),
-#type='l', auto.key=list(columns=5)))
-
-# Total cooling/heating/precip in Apr-Aug for each site/yr
-ww <- subset(datw, mo>=4 & mo<=8)
-ww <- aggregate(cbind(cdd,hdd,precip)~site+year, data=ww, sum)
-
-# Average yield per each site/env
-yy <- aggregate(yield~site+year, dat, mean)
-
-minn <- merge(ww, yy)
-
-
-# Higher yields generally associated with cooler temps, more precip
-library("reshape2")
-me <- melt(minn, id.var=c('site','year'))
-mey <- subset(me, variable=="yield")
-mey <- mey[,c('site','year','value')]
-names(mey) <- c('site','year','y')
-mec <- subset(me, variable!="yield")
-names(mec) <- c('site','year','covar','x')
-mecy <- merge(mec, mey)
-mecy$yr <- factor(mecy$year)
-oldpar <- tpg <- trellis.par.get()
-tpg$superpose.symbol$pch <- substring(levels(mecy$yr),4) # Last digit of year
-trellis.par.set(tpg)
-foo <- xyplot(y~x|covar*site, data=mecy, groups=yr, cex=1, ylim=c(5,65),
-              xlab="Weather covariate", ylab="Barley yield",
-              main="minnesota.barley",
-              panel=function(x,y,...) {
-                panel.lmline(x,y,..., col="gray")
-                panel.superpose(x,y,...)
-              },
-              scales=list(x=list(relation="free")))
-foo <- useOuterStrips(foo, strip.left = strip.custom(par.strip.text=list(cex=.7)))
-combineLimits(foo, margin.x=2L)
+if(require("latticeExtra")){
+  #useOuterStrips(xyplot(cdd~mo|year*site, datw, groups=year,
+  #main="minnesota.barley", xlab="month", ylab="Cooling degree days",
+  #subset=(mo > 3 & mo < 10), scales=list(alternating=FALSE),
+  #type='l', auto.key=list(columns=5)))
+    
+  # Total cooling/heating/precip in Apr-Aug for each site/yr
+  ww <- subset(datw, mo>=4 & mo<=8)
+  ww <- aggregate(cbind(cdd,hdd,precip)~site+year, data=ww, sum)
+    
+  # Average yield per each site/env
+  yy <- aggregate(yield~site+year, dat, mean)
+    
+  minn <- merge(ww, yy)
+    
+  
+  # Higher yields generally associated with cooler temps, more precip
+  library("reshape2")
+  me <- melt(minn, id.var=c('site','year'))
+  mey <- subset(me, variable=="yield")
+  mey <- mey[,c('site','year','value')]
+  names(mey) <- c('site','year','y')
+  mec <- subset(me, variable!="yield")
+  names(mec) <- c('site','year','covar','x')
+  mecy <- merge(mec, mey)
+  mecy$yr <- factor(mecy$year)
+  oldpar <- tpg <- trellis.par.get()
+  tpg$superpose.symbol$pch <- substring(levels(mecy$yr),4) # Last digit of year
+  trellis.par.set(tpg)
+  foo <- xyplot(y~x|covar*site, data=mecy, groups=yr, cex=1, ylim=c(5,65),
+                xlab="Weather covariate", ylab="Barley yield",
+                main="minnesota.barley",
+                panel=function(x,y,...) {
+                  panel.lmline(x,y,..., col="gray")
+                  panel.superpose(x,y,...)
+                },
+                scales=list(x=list(relation="free")))
+  foo <- useOuterStrips(foo, strip.left = strip.custom(par.strip.text=list(cex=.7)))
+  combineLimits(foo, margin.x=2L)
+}
 
 ## ----crossa, eval=FALSE, message=FALSE--------------------------------------------------
-#  
+#  library(agridat)
 #  # Specify env.group as column in data frame
 #  data(crossa.wheat)
 #  dat2 <- crossa.wheat
-#  require(gge)
-#  m4 <- gge(yield~gen*loc, dat2, env.group=locgroup, scale=FALSE)
-#  # plot(m4)
-#  biplot(m4, lab.env=TRUE, main="crossa.wheat")
+#  if(require("gge")){
+#    m4 <- gge(yield~gen*loc, dat2, env.group=locgroup, scale=FALSE)
+#    # plot(m4)
+#    biplot(m4, lab.env=TRUE, main="crossa.wheat")
+#  }
 
 ## ----nebr1, eval=TRUE-------------------------------------------------------------------
-library("maps")
-library("mapproj")
-library("latticeExtra")
 
+library(agridat)
 data(nebraska.farmincome)
 dat <- nebraska.farmincome
 dat$stco <- paste0('nebraska,', dat$county)
 dat <- transform(dat, crop=crop/1000, animal=animal/1000)
 
-# Raw, county-wide incomes.  Note the outlier Cuming county
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-mapplot(stco ~ crop + animal, data = dat,
-        scales = list(draw = FALSE),
-        main="nebraska.farmincome",
-        xlab="", ylab="Income ($1000) per county",
-        colramp=redblue,
-        map = map('county', 'nebraska', plot = FALSE, fill = TRUE,
-                  projection = "mercator"))
+if(require("maps") & require("mapproj") & require("latticeExtra")){
+
+  # Raw, county-wide incomes.  Note the outlier Cuming county
+  redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
+  mapplot(stco ~ crop + animal, data = dat,
+          scales = list(draw = FALSE),
+          main="nebraska.farmincome",
+          xlab="", ylab="Income ($1000) per county",
+          colramp=redblue,
+          map = map('county', 'nebraska', plot = FALSE, fill = TRUE,
+                    projection = "mercator"))
+}
 
 
 ## ----nebr2, eval=TRUE-------------------------------------------------------------------
@@ -264,6 +269,7 @@
 # Now scale to income/mile^2
 dat <- transform(dat, crop.rate=crop/area, animal.rate=animal/area)
 # And use manual breakpoints.
+if(require("maps") & require("mapproj") & require("latticeExtra")){
 mapplot(stco ~ crop.rate + animal.rate, data = dat,
         scales = list(draw = FALSE),
         main="nebraska.farmincome",
@@ -277,76 +283,78 @@
         #breaks=classIntervals(na.omit(c(dat$crop.rate, dat$animal.rate)), n=7, style='fisher')$brks
         breaks=c(0,.049, .108, .178, .230, .519, .958, 1.31)
         )
+}
 
 ## ----lasrosas, eval=TRUE, fig.height=7.5------------------------------------------------
-
+library(agridat)
 data(lasrosas.corn)
 dat <- lasrosas.corn
-library("latticeExtra")
 
 # yield map
 redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-foo1 <- levelplot(yield ~ long*lat|factor(year), data=dat,
-          aspect=1, layout=c(2,1),
-          main="lasrosas.corn grain yield (qu/ha)", xlab="Longitude", ylab="Latitude",
-          scales=list(alternating=FALSE),
-          prepanel = prepanel.default.xyplot,
-          panel = panel.levelplot.points,
-          type = c("p", "g"), col.regions=redblue)
-
-# Experiment design...shows problems in 2001
-dat <- lasrosas.corn
-
-xl <- range(dat$long)
-yl <- range(dat$lat)
-
-sseq=matrix(c(
-  35, 0.9, 0.5,  # brown
-  35, 0.8, 0.6,
-  35, 0.7, 0.7,
-  35, 0.6, 0.8,
-  35, 0.5, .9,
-  35, 0.4, 1,
-  80, 0.9, 0.5,  # green
-  80, 0.8, 0.6,
-  80, 0.7, 0.7,
-  80, 0.6, 0.8,
-  80, 0.5, 0.9,
-  80, 0.4, 1,
-  190, 0.9, 0.5,  # blue
-  190, 0.8, 0.6,
-  190, 0.7, 0.7,
-  190, 0.6, 0.8,
-  190, 0.5, 0.9,
-  190, 0.4, 1
-  ), ncol=3, byrow=TRUE)
-sseq <- hsv(sseq[,1]/360, sseq[,2], sseq[,3])
-
-dat$repnf <- factor(paste(dat$rep,dat$nf))
-# levels(dat$repnf) # check the order
-#dat <- transform(dat, col=as.character(sseq[as.numeric(factor(paste(dat$rep,dat$nf)))]))
-
-# By default, manual specification of col/pch does not work with multiple panels.
-# Define a custom panel function to make it work
-mypanel <- function(x,y,...,subscripts,col,pch) {
-  panel.xyplot(x,y,col=col[subscripts],pch=pch[subscripts], ...)
-}
-
-foo2 <- xyplot(lat~long|factor(year), data=dat,
-               aspect=1, layout=c(2,1),
-               xlim=xl, ylim=yl, cex=0.9,
-               main="lasrosas.corn experiment design", xlab="", ylab="",
-               scales=list(alternating=FALSE),
-               col=sseq[dat$repnf],
-               #pch=levels(dat$topo)[dat$topo],
-               pch=c('-','+','/','\\')[dat$topo],
-               panel=mypanel)
-
-plot(foo1, split = c(1, 1, 1, 2))
-plot(foo2, split = c(1, 2, 1, 2), newpage = FALSE)
-
+if(require("latticeExtra")){
+  foo1 <- levelplot(yield ~ long*lat|factor(year), data=dat,
+            aspect=1, layout=c(2,1),
+            main="lasrosas.corn grain yield (qu/ha)", xlab="Longitude", ylab="Latitude",
+            scales=list(alternating=FALSE),
+            prepanel = prepanel.default.xyplot,
+            panel = panel.levelplot.points,
+            type = c("p", "g"), col.regions=redblue)
+  
+  # Experiment design...shows problems in 2001
+  dat <- lasrosas.corn
+  
+  xl <- range(dat$long)
+  yl <- range(dat$lat)
+
+  sseq=matrix(c(
+    35, 0.9, 0.5,  # brown
+    35, 0.8, 0.6,
+    35, 0.7, 0.7,
+    35, 0.6, 0.8,
+    35, 0.5, .9,
+    35, 0.4, 1,
+    80, 0.9, 0.5,  # green
+    80, 0.8, 0.6,
+    80, 0.7, 0.7,
+    80, 0.6, 0.8,
+    80, 0.5, 0.9,
+    80, 0.4, 1,
+    190, 0.9, 0.5,  # blue
+    190, 0.8, 0.6,
+    190, 0.7, 0.7,
+    190, 0.6, 0.8,
+    190, 0.5, 0.9,
+    190, 0.4, 1
+    ), ncol=3, byrow=TRUE)
+  sseq <- hsv(sseq[,1]/360, sseq[,2], sseq[,3])
+  
+  dat$repnf <- factor(paste(dat$rep,dat$nf))
+  # levels(dat$repnf) # check the order
+  #dat <- transform(dat, col=as.character(sseq[as.numeric(factor(paste(dat$rep,dat$nf)))]))
+  
+  # By default, manual specification of col/pch does not work with multiple panels.
+  # Define a custom panel function to make it work
+  mypanel <- function(x,y,...,subscripts,col,pch) {
+    panel.xyplot(x,y,col=col[subscripts],pch=pch[subscripts], ...)
+  }
+  
+  foo2 <- xyplot(lat~long|factor(year), data=dat,
+                 aspect=1, layout=c(2,1),
+                 xlim=xl, ylim=yl, cex=0.9,
+                 main="lasrosas.corn experiment design", xlab="", ylab="",
+                 scales=list(alternating=FALSE),
+                 col=sseq[dat$repnf],
+                 #pch=levels(dat$topo)[dat$topo],
+                 pch=c('-','+','/','\\')[dat$topo],
+                 panel=mypanel)
+  
+  plot(foo1, split = c(1, 1, 1, 2))
+  plot(foo2, split = c(1, 2, 1, 2), newpage = FALSE)
+}
 
 ## ----nass, eval=TRUE, fig.height=8------------------------------------------------------
+library(agridat)
 data(nass.corn)
 dat <- nass.corn
 dat$acres <- dat$acres/1000000
@@ -359,6 +367,7 @@
 dat <- subset(dat, state != "California")
 dat <- droplevels(subset(dat, is.element(state, keep)))
 # Acres of corn grown each year
+require("lattice")
 xyplot(acres ~ year|state, dat, type='l', as.table=TRUE,
        layout=c(6,5),
        strip=strip.custom(par.strip.text=list(cex=.5)),
diff -Nru agridat-1.17/inst/doc/agridat_examples.Rmd agridat-1.18/inst/doc/agridat_examples.Rmd
--- agridat-1.17/inst/doc/agridat_examples.Rmd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/inst/doc/agridat_examples.Rmd	2020-12-11 20:13:19.000000000 +0000
@@ -22,23 +22,21 @@
 knitr::opts_chunk$set(echo=FALSE, fig.height = 5, fig.width = 5)
 options(width=90)
 ```
-This exhibit of agricultural data uses the following packages.
-
-```{r packs, eval=TRUE, message=FALSE, echo=TRUE}
-library("agridat")
-library("desplot")
-library("gge")
-library("HH")
-library("lattice")
-library("latticeExtra")
-library("mapproj")
-library("maps")
-library("reshape2")
-```
+This exhibit of agricultural data uses the following packages: 
+`agridat`,
+`desplot`,
+`gge`,
+`HH`,
+`lattice`,
+`latticeExtra`,
+`mapproj`,
+`maps`,
+`reshape2`.
 
 # Potato blight incidence over space and time
 
 ```{r lee1, eval=TRUE, fig.height=7.5, fig.width=5}
+library(agridat)
 data(lee.potatoblight)
 dat <- lee.potatoblight
 # Note the progression to lower scores as time passes in each year
@@ -52,11 +50,12 @@
          rep(0,5),1,1,1,1,1,
          rep(0,5),1,1,1,1,1,
          rep(0,5),1,1,1,1,1)
-require(desplot)
-desplot(y ~ col*row|date, dat,
-        main="lee.potatoblight", #col.regions=RedGrayBlue,
-        between=list(y=.3), strip.cex =.6,
-        layout=c(10,11), skip=as.logical(skp))
+if(require("desplot")){
+  desplot(dat, y ~ col*row|date,
+          main="lee.potatoblight", #col.regions=RedGrayBlue,
+          between=list(y=.3), strip.cex =.6,
+          layout=c(10,11), skip=as.logical(skp))
+}
 ```
 
 @lee2009random analyzed a large dataset to evaluate the resistance of potato varieties to blight.  This data contains evaluations of a changing set of varieties every two years, evaluated in 5 blocks, repeatedly throughout the growing season to track the progress of the disease.  Each panel shows a field map on the given date, with a separate row of panels for each year.
@@ -65,18 +64,21 @@
 
 
 ```{r lee2, eval=TRUE}
+library(agridat)
 # 1983 only.  I.Hardy succumbs quickly
 dat <- lee.potatoblight
 dat$dd <- as.Date(dat$date)
 d83 <- droplevels(subset(dat, year==1983))
-foo <- xyplot(y ~ dd|gen, d83, group=rep,
-       xlab="Date", ylab="Blight resistance score",
-       main="lee.potatoblight 1983", as.table=TRUE,
-       par.settings=list(
-         superpose.symbol=list(col=c("black","red","royalblue","#009900","dark orange"),
-           pch=c("1","2","3","4","5"))),
-       scales=list(alternating=FALSE, x=list(rot=90, cex=.7)))
-foo + xyplot(y ~ dd|gen, d83, subset=year==1983, type='smooth', col='gray80')
+if(require("latticeExtra")){
+  foo <- xyplot(y ~ dd|gen, d83, group=rep,
+         xlab="Date", ylab="Blight resistance score",
+         main="lee.potatoblight 1983", as.table=TRUE,
+         par.settings=list(
+           superpose.symbol=list(col=c("black","red","royalblue","#009900","dark orange"),
+             pch=c("1","2","3","4","5"))),
+         scales=list(alternating=FALSE, x=list(rot=90, cex=.7)))
+  foo + xyplot(y ~ dd|gen, d83, subset=year==1983, type='smooth', col='gray80')
+}
 ```
 
 In 1983, 20 varieties were evaluated in 5 blocks (shown by colored numbers) throughout the growing season for disease resistance. Resistance scores start at 9 for all varieties (shown in panels).  As the growing season progresses, the 'I.HARDY' variety succumbs quickly to blight, while 'IWA' succumbs steadily, and '064.1' resists blight until near the end of the season.
@@ -88,6 +90,7 @@
 # An informative prior
 
 ```{r harrison, eval=TRUE, fig.height=6}
+library(agridat)
 data(harrison.priors)
 d1 <- subset(harrison.priors, substance=="daidzein")
 d1 <- d1[ , c("source","number","min","max")]
@@ -102,32 +105,33 @@
   out <- rbind(out, data.frame(source=d1[ii,'source'], vals=vals))
 }
 out <- droplevels(out) # Extra levels exist in d1
-foo0 <- dotplot(source ~ vals, out, 
-                main="harrison.priors", xlab="Daidzein level",
+if(require("latticeExtra")) {
+  foo0 <- dotplot(source ~ vals, out, 
+                  main="harrison.priors", xlab="Daidzein level",
+                  panel=function(x,y,...){
+                    panel.dotplot(x,y,...)
+                    #browser()
+                    # Minimum for each row
+                    x2l <- tapply(x, y, min)
+                    x2r <- tapply(x, y, max)
+                    y2 <- tapply(y, y, "[", 1)
+                    panel.xyplot(x2l, y2, pch=16, cex=1.5, col="navy")
+                    panel.xyplot(x2r, y2, pch=16, cex=1.5, col="navy")
+                  },
+                  # Hack.  Add blanks for extra space on graph
+                  ylim=c(levels(out$source),"","","","prior","Constructed","",""))
+  
+  # Now calculate parameters for a common lognormal distribution
+  mu0 <- mean(log(out$vals))
+  sd0 <- sd(log(out$vals))
+  xvals <- seq(0,2000, length=100)
+  library("latticeExtra")
+  foo0 + xyplot((19+4000*dlnorm(xvals, mu0, sd0))~xvals, type='l',
                 panel=function(x,y,...){
-                  panel.dotplot(x,y,...)
-                  #browser()
-                  # Minimum for each row
-                  x2l <- tapply(x, y, min)
-                  x2r <- tapply(x, y, max)
-                  y2 <- tapply(y, y, "[", 1)
-                  panel.xyplot(x2l, y2, pch=16, cex=1.5, col="navy")
-                  panel.xyplot(x2r, y2, pch=16, cex=1.5, col="navy")
-                },
-                # Hack.  Add blanks for extra space on graph
-                ylim=c(levels(out$source),"","","","prior","Constructed","",""))
-
-# Now calculate parameters for a common lognormal distribution
-mu0 <- mean(log(out$vals))
-sd0 <- sd(log(out$vals))
-xvals <- seq(0,2000, length=100)
-library("latticeExtra")
-foo0 + xyplot((19+4000*dlnorm(xvals, mu0, sd0))~xvals, type='l',
-              panel=function(x,y,...){
-                panel.xyplot(x,y,...)
-                panel.abline(h=19, col="gray90")
-              })
-
+                  panel.xyplot(x,y,...)
+                  panel.abline(h=19, col="gray90")
+                })
+}
 ```
 
 @harrison2012bayesian used a Bayesian approach to model daidzein levels in soybean samples.  From 18 previous publications, they extracted the published minimum and maximum daidzein levels, and the number of samples tested.  Each line in the dotplot shows large, dark dots for one published minimum and maximum.  The small dots are imputed using a lognormal distribution.
@@ -142,6 +146,7 @@
 # Data densities for a binomial GLM
 
 ```{r mead, eval=TRUE}
+library(agridat)
 data(mead.germination)
 dat <- mead.germination
 # dat <- transform(dat, concf=factor(conc))
@@ -160,40 +165,39 @@
 newb <- expand.grid(temp=c('T1','T2','T3','T4'), logconc=log(c(0,.1,1,10)+.01))
 newb$pct <- predict(m6, new=newb, type='response')
 # Binomial density
-foob <- xyplot(pct~logconc |temp, newb,
-               xlim=c(-5.5, 4.5), ylim=c(-2, 53), as.table=TRUE,
-               xlab="Log concentration",
-               ylab="Seeds germinating (out of 50).  Binomial density.",
-               main="mead.germination", #layout=c(4,1),
-               panel=function(x,y,...){
-                 for(ix in 1:4){
-                   quan <- qbinom(c(.025, .975), size=50, prob=y[ix])
-                   yval <- seq(min(quan), max(quan), by=1)
-                   off <- x[ix]
-                   xl <- off + rep(0, length(yval))
-                   # Constant multiuplier of 8 chosen by trial and error
-                   xr <- off + 8 * dbinom(yval, size=50, prob=y[ix])
-                   panel.segments(xl,yval,xr, yval, cex=.35, lwd=3, col="gray70")
-                 }
-               })
-
-
-# Add mean response line with equally-spaced points on the log scale
-newl <- expand.grid(temp=c('T1','T2','T3','T4'),
-                   logconc=seq(log(.01), log(10.01), length=50))
-newl$pct <- predict(m6, new=newl, type='response')
-# Logistic curve
-fool <- xyplot(pct~logconc|temp, newl,
-               panel=function(x,y,...){
-                 panel.points(x, 50*y, type='l', col='blue')
-               })
-
-
-# Data points last, on top of everything
-food <- xyplot(germ~logconc|temp, dat, layout=c(4,1),
-       ylab="Seeds germinating (out of 50)", cex=1.5, pch=20, col='black')
-foob + fool + food
-
+if(require("latticeExtra")){
+  foob <- xyplot(pct~logconc |temp, newb,
+                 xlim=c(-5.5, 4.5), ylim=c(-2, 53), as.table=TRUE,
+                 xlab="Log concentration",
+                 ylab="Seeds germinating (out of 50).  Binomial density.",
+                 main="mead.germination", #layout=c(4,1),
+                 panel=function(x,y,...){
+                   for(ix in 1:4){
+                     quan <- qbinom(c(.025, .975), size=50, prob=y[ix])
+                     yval <- seq(min(quan), max(quan), by=1)
+                     off <- x[ix]
+                     xl <- off + rep(0, length(yval))
+                     # Constant multiuplier of 8 chosen by trial and error
+                     xr <- off + 8 * dbinom(yval, size=50, prob=y[ix])
+                     panel.segments(xl,yval,xr, yval, cex=.35, lwd=3, col="gray70")
+                   }
+                 })
+  
+  # Add mean response line with equally-spaced points on the log scale
+  newl <- expand.grid(temp=c('T1','T2','T3','T4'),
+                     logconc=seq(log(.01), log(10.01), length=50))
+  newl$pct <- predict(m6, new=newl, type='response')
+  # Logistic curve
+  fool <- xyplot(pct~logconc|temp, newl,
+                 panel=function(x,y,...){
+                   panel.points(x, 50*y, type='l', col='blue')
+                 })
+  
+  # Data points last, on top of everything
+  food <- xyplot(germ~logconc|temp, dat, layout=c(4,1),
+         ylab="Seeds germinating (out of 50)", cex=1.5, pch=20, col='black')
+  foob + fool + food
+}
 ```
 
 @mead2002statistical present data for germination of seeds under four temperatures (T1-T4) and four chemical concentrations.  For each of the 4*4=16 treatments, 50 seeds were tested in each of four reps.  In the graphic, each point is one rep. The blue line is a fitted curve from a GLM with Temperature as a factor and log concentration as a covariate.  The gray lines show the central 95 percent of the binomial density at that position.
@@ -205,14 +209,16 @@
 # Verification of experiment layout
 
 ```{r gomez, eval=TRUE}
+library(agridat)
 data(gomez.stripsplitplot)
 dat <- gomez.stripsplitplot
 
 # Layout
-require(desplot)
-desplot(gen~col*row, dat,
-        out1=rep, col=nitro, text=planting, cex=1,
-        main="gomez.stripsplitplot")
+if(require("desplot")){
+  desplot(dat, gen~col*row,
+          out1=rep, col=nitro, text=planting, cex=1,
+          main="gomez.stripsplitplot")
+}
 ```
 
 @gomez1984statistical provide data for an experiment with 3 reps, 6 genotypes, 3 levels of nitrogen and 2 planting dates.  The experiment layout is putatively a ''split strip-plot''.  To verify the design, the `desplot` package is used for plotting the design of field experiments.
@@ -224,19 +230,21 @@
 # Visualizing main effects, two-way interactions
 
 ```{r gomez2, eval=TRUE}
+library(agridat)
 data(gomez.splitsplit)
 dat <- gomez.splitsplit
 dat$nitrogen <- factor(dat$nitro)
-require(HH)
-#position(dat$rep) <- position(dat$management) <-
-#  position(dat$gen) <- c(10,70,130)
-#position(dat$nitrogen) <- c(0,50,80,110,140)
-interaction2wt(yield~rep+nitrogen+management+gen, data=dat,
-               main="gomez.splitsplit",
-               x.between=0, y.between=0,
-               relation=list(x="free", y="same"),
-               rot=c(90,0), xlab="",
-               par.strip.text.input=list(cex=.8))
+if(require("HH")){
+  #position(dat$rep) <- position(dat$management) <-
+  #  position(dat$gen) <- c(10,70,130)
+  #position(dat$nitrogen) <- c(0,50,80,110,140)
+  interaction2wt(yield~rep+nitrogen+management+gen, data=dat,
+                 main="gomez.splitsplit",
+                 x.between=0, y.between=0,
+                 relation=list(x="free", y="same"),
+                 rot=c(90,0), xlab="",
+                 par.strip.text.input=list(cex=.8))
+}
 ```
 
 @heiberger2004statistical provide an interesting way to use lattice graphics to visualize the main effects (using boxplots) and interactions (using interaction plots) in data.  Rice yield is plotted versus replication, nitrogen, management type, and genotype variety.  Box plots show minor differences between reps, increaing yield due to nitrogen, high yield from intensive management, and large differences between varieties.
@@ -266,6 +274,7 @@
 # Mosaic plot of potato damage from harvesting
 
 ```{r keen, eval=TRUE, fig.width=7, fig.height=7.5}
+library(agridat)
 data(keen.potatodamage)
 dat <- keen.potatodamage
 
@@ -288,51 +297,53 @@
 # Yield vs covariate for lattice::barley
 
 ```{r wright, eval=TRUE}
+library(agridat)
 data(minnesota.barley.yield)
 data(minnesota.barley.weather)
 dat <- minnesota.barley.yield
 datw <- minnesota.barley.weather
 
 # Weather trends over time
-library("latticeExtra")
-#useOuterStrips(xyplot(cdd~mo|year*site, datw, groups=year,
-#main="minnesota.barley", xlab="month", ylab="Cooling degree days",
-#subset=(mo > 3 & mo < 10), scales=list(alternating=FALSE),
-#type='l', auto.key=list(columns=5)))
-
-# Total cooling/heating/precip in Apr-Aug for each site/yr
-ww <- subset(datw, mo>=4 & mo<=8)
-ww <- aggregate(cbind(cdd,hdd,precip)~site+year, data=ww, sum)
-
-# Average yield per each site/env
-yy <- aggregate(yield~site+year, dat, mean)
-
-minn <- merge(ww, yy)
-
-
-# Higher yields generally associated with cooler temps, more precip
-library("reshape2")
-me <- melt(minn, id.var=c('site','year'))
-mey <- subset(me, variable=="yield")
-mey <- mey[,c('site','year','value')]
-names(mey) <- c('site','year','y')
-mec <- subset(me, variable!="yield")
-names(mec) <- c('site','year','covar','x')
-mecy <- merge(mec, mey)
-mecy$yr <- factor(mecy$year)
-oldpar <- tpg <- trellis.par.get()
-tpg$superpose.symbol$pch <- substring(levels(mecy$yr),4) # Last digit of year
-trellis.par.set(tpg)
-foo <- xyplot(y~x|covar*site, data=mecy, groups=yr, cex=1, ylim=c(5,65),
-              xlab="Weather covariate", ylab="Barley yield",
-              main="minnesota.barley",
-              panel=function(x,y,...) {
-                panel.lmline(x,y,..., col="gray")
-                panel.superpose(x,y,...)
-              },
-              scales=list(x=list(relation="free")))
-foo <- useOuterStrips(foo, strip.left = strip.custom(par.strip.text=list(cex=.7)))
-combineLimits(foo, margin.x=2L)
+if(require("latticeExtra")){
+  #useOuterStrips(xyplot(cdd~mo|year*site, datw, groups=year,
+  #main="minnesota.barley", xlab="month", ylab="Cooling degree days",
+  #subset=(mo > 3 & mo < 10), scales=list(alternating=FALSE),
+  #type='l', auto.key=list(columns=5)))
+    
+  # Total cooling/heating/precip in Apr-Aug for each site/yr
+  ww <- subset(datw, mo>=4 & mo<=8)
+  ww <- aggregate(cbind(cdd,hdd,precip)~site+year, data=ww, sum)
+    
+  # Average yield per each site/env
+  yy <- aggregate(yield~site+year, dat, mean)
+    
+  minn <- merge(ww, yy)
+    
+  
+  # Higher yields generally associated with cooler temps, more precip
+  library("reshape2")
+  me <- melt(minn, id.var=c('site','year'))
+  mey <- subset(me, variable=="yield")
+  mey <- mey[,c('site','year','value')]
+  names(mey) <- c('site','year','y')
+  mec <- subset(me, variable!="yield")
+  names(mec) <- c('site','year','covar','x')
+  mecy <- merge(mec, mey)
+  mecy$yr <- factor(mecy$year)
+  oldpar <- tpg <- trellis.par.get()
+  tpg$superpose.symbol$pch <- substring(levels(mecy$yr),4) # Last digit of year
+  trellis.par.set(tpg)
+  foo <- xyplot(y~x|covar*site, data=mecy, groups=yr, cex=1, ylim=c(5,65),
+                xlab="Weather covariate", ylab="Barley yield",
+                main="minnesota.barley",
+                panel=function(x,y,...) {
+                  panel.lmline(x,y,..., col="gray")
+                  panel.superpose(x,y,...)
+                },
+                scales=list(x=list(relation="free")))
+  foo <- useOuterStrips(foo, strip.left = strip.custom(par.strip.text=list(cex=.7)))
+  combineLimits(foo, margin.x=2L)
+}
 ```
 
 @wright2013revisiting investigated the `lattice::barley` data.  The original two years of data were extended to 10 years (from original source documents), and supplemented with weather covariates for the 6 locations and 10 years.  Each panel shows a scatterplot and regression for average location yield verses the weather covariate.  Horizontal strips are for locations, vertical strips are for covariates: cdd = Cooling Degree Days, hdd = Heating Degree Days, precip = Precipitation).  Higher values of heating imply cooler weather.  Each plotting symbol is the last digit of the year (1927-1936) for that location.
@@ -344,14 +355,15 @@
 # GGE biplot
 
 ```{r crossa, eval=FALSE, message=FALSE}
-
+library(agridat)
 # Specify env.group as column in data frame
 data(crossa.wheat)
 dat2 <- crossa.wheat
-require(gge)
-m4 <- gge(yield~gen*loc, dat2, env.group=locgroup, scale=FALSE)
-# plot(m4)
-biplot(m4, lab.env=TRUE, main="crossa.wheat")
+if(require("gge")){
+  m4 <- gge(yield~gen*loc, dat2, env.group=locgroup, scale=FALSE)
+  # plot(m4)
+  biplot(m4, lab.env=TRUE, main="crossa.wheat")
+}
 ```
 
 @laffont2013genotype developed a variation of the GGE (genotype plus genotype-by-environment) biplot to include auxiliary information about a block/group of environments.  Each location is classified into one of two mega-environments (colored).  The mosaic plots partition variation simultaneously by principal component axis and source (genotype, genotype-by-block, residual).
@@ -363,24 +375,25 @@
 # Nebraska farming income choropleth
 
 ```{r nebr1, eval=TRUE}
-library("maps")
-library("mapproj")
-library("latticeExtra")
 
+library(agridat)
 data(nebraska.farmincome)
 dat <- nebraska.farmincome
 dat$stco <- paste0('nebraska,', dat$county)
 dat <- transform(dat, crop=crop/1000, animal=animal/1000)
 
-# Raw, county-wide incomes.  Note the outlier Cuming county
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-mapplot(stco ~ crop + animal, data = dat,
-        scales = list(draw = FALSE),
-        main="nebraska.farmincome",
-        xlab="", ylab="Income ($1000) per county",
-        colramp=redblue,
-        map = map('county', 'nebraska', plot = FALSE, fill = TRUE,
-                  projection = "mercator"))
+if(require("maps") & require("mapproj") & require("latticeExtra")){
+
+  # Raw, county-wide incomes.  Note the outlier Cuming county
+  redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
+  mapplot(stco ~ crop + animal, data = dat,
+          scales = list(draw = FALSE),
+          main="nebraska.farmincome",
+          xlab="", ylab="Income ($1000) per county",
+          colramp=redblue,
+          map = map('county', 'nebraska', plot = FALSE, fill = TRUE,
+                    projection = "mercator"))
+}
 
 ```
 
@@ -396,6 +409,7 @@
 # Now scale to income/mile^2
 dat <- transform(dat, crop.rate=crop/area, animal.rate=animal/area)
 # And use manual breakpoints.
+if(require("maps") & require("mapproj") & require("latticeExtra")){
 mapplot(stco ~ crop.rate + animal.rate, data = dat,
         scales = list(draw = FALSE),
         main="nebraska.farmincome",
@@ -409,9 +423,10 @@
         #breaks=classIntervals(na.omit(c(dat$crop.rate, dat$animal.rate)), n=7, style='fisher')$brks
         breaks=c(0,.049, .108, .178, .230, .519, .958, 1.31)
         )
+}
 ```
 
-Because counties are different sizes, the second graphic uses an income rate per square mile.  Because of the outlier, it might be smart to use percentile break points, but doing so hides the outlier.  Instead, the break points are calulated using a method called Fisher-Jenks.  These break points show both the outlier and the spatial patterns.  It is now easy to see that northwest (Sandhills) Nebraska has low farming income, especially for crops.  Counties with missing data are white, which is easily distinguished from gray.
+Because counties are different sizes, the second graphic uses an income rate per square mile.  Because of the outlier, it might be smart to use percentile break points, but doing so hides the outlier.  Instead, the break points are calculated using a method called Fisher-Jenks.  These break points show both the outlier and the spatial patterns.  It is now easy to see that northwest (Sandhills) Nebraska has low farming income, especially for crops.  Counties with missing data are white, which is easily distinguished from gray.
 
 Where are farm incomes highest?  Why?
 
@@ -420,72 +435,72 @@
 # Las Rosas yield monitor
 
 ```{r lasrosas, eval=TRUE, fig.height=7.5}
-
+library(agridat)
 data(lasrosas.corn)
 dat <- lasrosas.corn
-library("latticeExtra")
 
 # yield map
 redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-foo1 <- levelplot(yield ~ long*lat|factor(year), data=dat,
-          aspect=1, layout=c(2,1),
-          main="lasrosas.corn grain yield (qu/ha)", xlab="Longitude", ylab="Latitude",
-          scales=list(alternating=FALSE),
-          prepanel = prepanel.default.xyplot,
-          panel = panel.levelplot.points,
-          type = c("p", "g"), col.regions=redblue)
-
-# Experiment design...shows problems in 2001
-dat <- lasrosas.corn
-
-xl <- range(dat$long)
-yl <- range(dat$lat)
-
-sseq=matrix(c(
-  35, 0.9, 0.5,  # brown
-  35, 0.8, 0.6,
-  35, 0.7, 0.7,
-  35, 0.6, 0.8,
-  35, 0.5, .9,
-  35, 0.4, 1,
-  80, 0.9, 0.5,  # green
-  80, 0.8, 0.6,
-  80, 0.7, 0.7,
-  80, 0.6, 0.8,
-  80, 0.5, 0.9,
-  80, 0.4, 1,
-  190, 0.9, 0.5,  # blue
-  190, 0.8, 0.6,
-  190, 0.7, 0.7,
-  190, 0.6, 0.8,
-  190, 0.5, 0.9,
-  190, 0.4, 1
-  ), ncol=3, byrow=TRUE)
-sseq <- hsv(sseq[,1]/360, sseq[,2], sseq[,3])
-
-dat$repnf <- factor(paste(dat$rep,dat$nf))
-# levels(dat$repnf) # check the order
-#dat <- transform(dat, col=as.character(sseq[as.numeric(factor(paste(dat$rep,dat$nf)))]))
-
-# By default, manual specification of col/pch does not work with multiple panels.
-# Define a custom panel function to make it work
-mypanel <- function(x,y,...,subscripts,col,pch) {
-  panel.xyplot(x,y,col=col[subscripts],pch=pch[subscripts], ...)
-}
-
-foo2 <- xyplot(lat~long|factor(year), data=dat,
-               aspect=1, layout=c(2,1),
-               xlim=xl, ylim=yl, cex=0.9,
-               main="lasrosas.corn experiment design", xlab="", ylab="",
-               scales=list(alternating=FALSE),
-               col=sseq[dat$repnf],
-               #pch=levels(dat$topo)[dat$topo],
-               pch=c('-','+','/','\\')[dat$topo],
-               panel=mypanel)
-
-plot(foo1, split = c(1, 1, 1, 2))
-plot(foo2, split = c(1, 2, 1, 2), newpage = FALSE)
-
+if(require("latticeExtra")){
+  foo1 <- levelplot(yield ~ long*lat|factor(year), data=dat,
+            aspect=1, layout=c(2,1),
+            main="lasrosas.corn grain yield (qu/ha)", xlab="Longitude", ylab="Latitude",
+            scales=list(alternating=FALSE),
+            prepanel = prepanel.default.xyplot,
+            panel = panel.levelplot.points,
+            type = c("p", "g"), col.regions=redblue)
+  
+  # Experiment design...shows problems in 2001
+  dat <- lasrosas.corn
+  
+  xl <- range(dat$long)
+  yl <- range(dat$lat)
+
+  sseq=matrix(c(
+    35, 0.9, 0.5,  # brown
+    35, 0.8, 0.6,
+    35, 0.7, 0.7,
+    35, 0.6, 0.8,
+    35, 0.5, .9,
+    35, 0.4, 1,
+    80, 0.9, 0.5,  # green
+    80, 0.8, 0.6,
+    80, 0.7, 0.7,
+    80, 0.6, 0.8,
+    80, 0.5, 0.9,
+    80, 0.4, 1,
+    190, 0.9, 0.5,  # blue
+    190, 0.8, 0.6,
+    190, 0.7, 0.7,
+    190, 0.6, 0.8,
+    190, 0.5, 0.9,
+    190, 0.4, 1
+    ), ncol=3, byrow=TRUE)
+  sseq <- hsv(sseq[,1]/360, sseq[,2], sseq[,3])
+  
+  dat$repnf <- factor(paste(dat$rep,dat$nf))
+  # levels(dat$repnf) # check the order
+  #dat <- transform(dat, col=as.character(sseq[as.numeric(factor(paste(dat$rep,dat$nf)))]))
+  
+  # By default, manual specification of col/pch does not work with multiple panels.
+  # Define a custom panel function to make it work
+  mypanel <- function(x,y,...,subscripts,col,pch) {
+    panel.xyplot(x,y,col=col[subscripts],pch=pch[subscripts], ...)
+  }
+  
+  foo2 <- xyplot(lat~long|factor(year), data=dat,
+                 aspect=1, layout=c(2,1),
+                 xlim=xl, ylim=yl, cex=0.9,
+                 main="lasrosas.corn experiment design", xlab="", ylab="",
+                 scales=list(alternating=FALSE),
+                 col=sseq[dat$repnf],
+                 #pch=levels(dat$topo)[dat$topo],
+                 pch=c('-','+','/','\\')[dat$topo],
+                 panel=mypanel)
+  
+  plot(foo1, split = c(1, 1, 1, 2))
+  plot(foo2, split = c(1, 2, 1, 2), newpage = FALSE)
+}
 ```
 
 @anselin2004spatial and @lambert2004comparison looked at yield monitor data collected from a corn field in Argentina in 1999 and 2001, to see how yield was affected by field topography and nitrogen fertilizer.  The figures here show heatmaps for the yield each year, and also the experiment design (colors are reps, shades of color are nitrogen level, plotting character is topography).
@@ -497,6 +512,7 @@
 # Time series of corn yields by state
 
 ```{r nass, eval=TRUE, fig.height=8}
+library(agridat)
 data(nass.corn)
 dat <- nass.corn
 dat$acres <- dat$acres/1000000
@@ -509,6 +525,7 @@
 dat <- subset(dat, state != "California")
 dat <- droplevels(subset(dat, is.element(state, keep)))
 # Acres of corn grown each year
+require("lattice")
 xyplot(acres ~ year|state, dat, type='l', as.table=TRUE,
        layout=c(6,5),
        strip=strip.custom(par.strip.text=list(cex=.5)),
diff -Nru agridat-1.17/inst/doc/agridat_intro.html agridat-1.18/inst/doc/agridat_intro.html
--- agridat-1.17/inst/doc/agridat_intro.html	2020-07-30 20:44:06.000000000 +0000
+++ agridat-1.18/inst/doc/agridat_intro.html	2021-01-11 23:23:05.000000000 +0000
@@ -12,10 +12,23 @@
 
 <meta name="author" content="Kevin Wright" />
 
-<meta name="date" content="2020-07-30" />
+<meta name="date" content="2021-01-11" />
 
 <title>Introduction to agridat</title>
 
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
 <script>// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
 // v0.0.1
 // Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
@@ -33,6 +46,15 @@
 });
 </script>
 
+<style type="text/css">
+  code{white-space: pre-wrap;}
+  span.smallcaps{font-variant: small-caps;}
+  span.underline{text-decoration: underline;}
+  div.column{display: inline-block; vertical-align: top; width: 50%;}
+  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+  ul.task-list{list-style: none;}
+      </style>
+
 
 
 
@@ -231,7 +253,7 @@
 
 <h1 class="title toc-ignore">Introduction to <code>agridat</code></h1>
 <h4 class="author">Kevin Wright</h4>
-<h4 class="date">2020-07-30</h4>
+<h4 class="date">2021-01-11</h4>
 
 
 
@@ -242,7 +264,7 @@
 <p>Box (1957) said, “I had hoped that we had seen the end of the obscene tribal habit practiced by statisticians of continually exhuming and massaging dead data sets after their purpose in life has long since been forgotten and there was no possibility of doing anything useful as a result of this treatment.”</p>
 <p>Massaging these dead data sets will not lead to any of the genetics being released for commercial use. The value of this package is: 1. Validating published analyses. 2. Providing data for testing new analysis methods. 3. Illustrating (and validating) the use of R packages.</p>
 <p>White and van Evert (2008) present some guidelines for publication of data.</p>
-<p>Some of the examples use the <code>asreml</code> package since it is the <em>only</em> R tool for fitting mixed models with complex variance structures to large datasets, and the best option for modelling AR1xAR1 residual variance structures. Commercial use of <code>asreml</code> requires a license: <a href="http://www.vsni.co.uk/downloads/asreml" class="uri">http://www.vsni.co.uk/downloads/asreml</a>.</p>
+<p>Some of the examples use the <code>asreml</code> package since it is the <em>only</em> R tool for fitting mixed models with complex variance structures to large datasets, and the best option for modelling AR1xAR1 residual variance structures. Commercial use of <code>asreml</code> requires a license: <a href="https://www.vsni.co.uk/downloads/asreml" class="uri">https://www.vsni.co.uk/downloads/asreml</a>.</p>
 </div>
 <div id="comments-on-the-package-structure" class="section level1">
 <h1>Comments on the package structure</h1>
@@ -257,8 +279,8 @@
 <div id="references" class="section level1">
 <h1>References</h1>
 <p>G. E. P. Box (1957). Integration of Techniques in Process Development, Transactions of the American Society for Quality Control.</p>
-<p>J. White and Frits van Evert. (2008). Publishing Agronomic Data. Agronomy Journal, 100, 1396-1400. <a href="http://doi.org/10.2134/agronj2008.0080F" class="uri">http://doi.org/10.2134/agronj2008.0080F</a></p>
-<p>Stephen Wolfram (2017). Launching the Wolfram Data Repository: Data Publishing that Really Works. <a href="http://blog.stephenwolfram.com/2017/04/launching-the-wolfram-data-repository-data-publishing-that-really-works/" class="uri">http://blog.stephenwolfram.com/2017/04/launching-the-wolfram-data-repository-data-publishing-that-really-works/</a></p>
+<p>J. White and Frits van Evert. (2008). Publishing Agronomic Data. Agronomy Journal, 100, 1396-1400. <a href="https://doi.org/10.2134/agronj2008.0080F" class="uri">https://doi.org/10.2134/agronj2008.0080F</a></p>
+<p>Stephen Wolfram (2017). Launching the Wolfram Data Repository: Data Publishing that Really Works. <a href="https://writings.stephenwolfram.com/2017/04/launching-the-wolfram-data-repository-data-publishing-that-really-works/" class="uri">https://writings.stephenwolfram.com/2017/04/launching-the-wolfram-data-repository-data-publishing-that-really-works/</a></p>
 </div>
 
 
diff -Nru agridat-1.17/inst/doc/agridat_intro.Rmd agridat-1.18/inst/doc/agridat_intro.Rmd
--- agridat-1.17/inst/doc/agridat_intro.Rmd	2019-10-30 12:25:11.000000000 +0000
+++ agridat-1.18/inst/doc/agridat_intro.Rmd	2020-12-11 21:32:39.000000000 +0000
@@ -28,7 +28,7 @@
 
 White and van Evert (2008) present some guidelines for publication of data.
 
-Some of the examples use the `asreml` package since it is the _only_ R tool for fitting mixed models with complex variance structures to large datasets, and the best option for modelling AR1xAR1 residual variance structures.  Commercial use of `asreml` requires a license: http://www.vsni.co.uk/downloads/asreml.
+Some of the examples use the `asreml` package since it is the _only_ R tool for fitting mixed models with complex variance structures to large datasets, and the best option for modelling AR1xAR1 residual variance structures.  Commercial use of `asreml` requires a license: https://www.vsni.co.uk/downloads/asreml.
 
 # Comments on the package structure
 
@@ -54,8 +54,8 @@
 J. White and Frits van Evert. (2008).
 Publishing Agronomic Data.
 Agronomy Journal, 100, 1396-1400.
-http://doi.org/10.2134/agronj2008.0080F
+https://doi.org/10.2134/agronj2008.0080F
 
 Stephen Wolfram (2017).
 Launching the Wolfram Data Repository: Data Publishing that Really Works.
-http://blog.stephenwolfram.com/2017/04/launching-the-wolfram-data-repository-data-publishing-that-really-works/
+https://writings.stephenwolfram.com/2017/04/launching-the-wolfram-data-repository-data-publishing-that-really-works/
diff -Nru agridat-1.17/man/aastveit.barley.Rd agridat-1.18/man/aastveit.barley.Rd
--- agridat-1.17/man/aastveit.barley.Rd	2019-11-22 16:44:01.000000000 +0000
+++ agridat-1.18/man/aastveit.barley.Rd	2020-12-11 20:45:37.000000000 +0000
@@ -71,13 +71,13 @@
   Aastveit, A. H. and Martens, H. (1986).
   ANOVA interactions interpreted by partial least squares regression.
   Biometrics, 42, 829--844.
-  http://doi.org/10.2307/2530697
+  https://doi.org/10.2307/2530697
 }
 \references{
   J. Chadoeuf and J. B. Denis (1991).
   Asymptotic variances for the multiplicative interaction model.
   J. App. Stat., 18, 331-353.
-  http://doi.org/10.1080/02664769100000032
+  https://doi.org/10.1080/02664769100000032
 }
 \examples{
 \dontrun{
diff -Nru agridat-1.17/man/acorsi.grayleafspot.Rd agridat-1.18/man/acorsi.grayleafspot.Rd
--- agridat-1.17/man/acorsi.grayleafspot.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/acorsi.grayleafspot.Rd	2020-12-11 20:46:29.000000000 +0000
@@ -33,7 +33,7 @@
   Applying the generalized additive main effects and multiplicative
   interaction model to analysis of maize genotypes resistant to grey leaf spot.
   \emph{Journal of Agricultural Science}.
-  http://doi.org/10.1017/S0021859616001015
+  https://doi.org/10.1017/S0021859616001015
 
   Electronic data and R code kindly provided by Marlon Coan.
 }
@@ -49,6 +49,7 @@
 
 # Acorsi figure 2. Note: Acorsi used cell means
 op <- par(mfrow=c(2,1), mar=c(5,4,3,2))
+libs(lattice)
 boxplot(y ~ env, dat, las=2,
         xlab="environment", ylab="GLS severity")
 title("acorsi.grayleafspot")
diff -Nru agridat-1.17/man/adugna.sorghum.Rd agridat-1.18/man/adugna.sorghum.Rd
--- agridat-1.17/man/adugna.sorghum.Rd	2019-11-22 16:45:09.000000000 +0000
+++ agridat-1.18/man/adugna.sorghum.Rd	2020-12-11 20:46:31.000000000 +0000
@@ -41,7 +41,7 @@
   Assessment of yield stability in sorghum using
   univariate and multivariate statistical approaches.
   Hereditas, 145, 28--37.  
-  http://doi.org/10.1111/j.0018-0661.2008.2023.x
+  https://doi.org/10.1111/j.0018-0661.2008.2023.x
 }
 
 \examples{
diff -Nru agridat-1.17/man/agridat.Rd agridat-1.18/man/agridat.Rd
--- agridat-1.17/man/agridat.Rd	2020-07-15 03:24:19.000000000 +0000
+++ agridat-1.18/man/agridat.Rd	2020-12-11 20:46:33.000000000 +0000
@@ -87,6 +87,8 @@
 \link{moore.springcauliflower.uniformity} \tab 12 x 20 \tab xy \tab \cr
 \link{moore.fallcauliflower.uniformity}   \tab 12 x 20 \tab xy \tab \cr
 \link{nagai.strawberry.uniformity}        \tab 18 x 24 \tab xy \tab \cr
+\link{nagai.strawberry.uniformity}        \tab 18 x 24 \tab xy \tab \cr
+\link{nair.turmeric.uniformity}           \tab 72 x 12 \tab xy \tab \cr
 \link{narain.sorghum.uniformity}          \tab 10 x 16 \tab xy \tab \cr
 \link{nonnecke.peas.uniformity}           \tab 15 x 18 \tab xy, 2 traits \tab \cr
 \link{nonnecke.sweetcorn.uniformity}      \tab 32 x 18 \tab xy, 3 loc \tab \cr
@@ -101,6 +103,7 @@
 \link{robinson.peanut.uniformity}         \tab 16 x 36 \tab xy \tab \cr
 \link{sawyer.multi.uniformity}            \tab 8 x 6   \tab xy, 3 year \tab \cr
 \link{sayer.sugarcane.uniformity}         \tab 8 x 136, 8 x 121 \tab xy, 2 year \tab \cr
+\link{shafi.tomato.uniformity}            \tab 10 x 20 \tab xy \tab \cr
 \link{smith.beans.uniformity}             \tab 18 x 12, 16 x 15 \tab xy, 2 yr, 2 crops \tab \cr
 \link{smith.corn.uniformity}              \tab 6 x 20 \tab xy, 3 years \tab rgl \cr
 \link{stephens.sorghum.uniformity}        \tab 100 x 20 \tab xy \tab \cr
@@ -224,6 +227,7 @@
 \link{crowder.seeds}               \tab 2   \tab    \tab 21  \tab    \tab 2     \tab \tab glm,INLA,jags \cr
 \link{cox.stripsplit}              \tab     \tab    \tab  4  \tab    \tab 3,4,2 \tab split-block \tab aov \cr
 \link{cullis.earlygen}             \tab 532 \tab    \tab     \tab    \tab       \tab xy \tab asreml \cr
+\link{damesa.maize}                \tab 22  \tab 4  \tab 3   \tab    \tab       \tab xy,incblock,twostage \tab asreml \cr
 \link{dasilva.maize}               \tab 55  \tab 9  \tab 3   \tab    \tab       \tab \tab \cr
 \link{darwin.maize}                \tab     \tab    \tab 12  \tab    \tab 2     \tab \tab t.test \cr
 \link{davidian.soybean}            \tab 2   \tab    \tab     \tab 3  \tab       \tab \tab nlme \cr
@@ -283,6 +287,7 @@
 \link{jansen.apple}                \tab 3   \tab    \tab 4   \tab    \tab 3     \tab binomial \tab glmer\cr
 \link{jansen.carrot}               \tab 16  \tab    \tab 3   \tab    \tab 2     \tab binomial \tab glmer\cr
 \link{jansen.strawberry}           \tab 12  \tab    \tab 4   \tab    \tab       \tab ordinal \tab mosaicplot\cr
+\link{jayaraman.bamboo}            \tab 6   \tab 2  \tab 3   \tab    \tab       \tab heritability \tab lmer \cr
 \link{jenkyn.mildew}               \tab     \tab    \tab 9   \tab    \tab 4     \tab \tab lm\cr
 \link{john.alpha}                  \tab 24  \tab    \tab 3   \tab    \tab       \tab alpha \tab lm, lmer \cr
 \link{johnson.blight}              \tab     \tab    \tab     \tab 2  \tab       \tab \tab logistic \cr
@@ -424,6 +429,6 @@
   J. White and Frits van Evert. (2008).
   Publishing Agronomic Data.
   \emph{Agron J.} 100, 1396-1400.
-  http://doi.org/10.2134/agronj2008.0080F
+  https://doi.org/10.2134/agronj2008.0080F
 }
 
diff -Nru agridat-1.17/man/allcroft.lodging.Rd agridat-1.18/man/allcroft.lodging.Rd
--- agridat-1.17/man/allcroft.lodging.Rd	2019-11-22 16:45:45.000000000 +0000
+++ agridat-1.18/man/allcroft.lodging.Rd	2020-12-11 20:46:34.000000000 +0000
@@ -25,7 +25,7 @@
   D. J. Allcroft and C. A. Glasbey, 2003.
   Analysis of crop lodging using a latent variable model.
   Journal of Agricultural Science, 140, 383--393.
-  http://doi.org/10.1017/S0021859603003332
+  https://doi.org/10.1017/S0021859603003332
 }
 
 \examples{
diff -Nru agridat-1.17/man/alwan.lamb.Rd agridat-1.18/man/alwan.lamb.Rd
--- agridat-1.17/man/alwan.lamb.Rd	2019-12-06 21:46:05.000000000 +0000
+++ agridat-1.18/man/alwan.lamb.Rd	2020-12-11 20:46:38.000000000 +0000
@@ -38,7 +38,7 @@
 \source{
   Mohammed Alwan (1983).
   Studies of the flock mating performance of Booroola merino crossbred ram lambs, and the foot conditions in Booroola merino crossbreds and Perendale sheep grazed on hill country.
-  Thesis, Massey University. http://hdl.handle.net/10179/5900
+  Thesis, Massey University. https://hdl.handle.net/10179/5900
   Appendix I, II.
 }
 \references{
@@ -84,7 +84,7 @@
   m1 <- clmm(shape ~ yr + b1 + b2 + b3 + (1|sire), data=dat,
              weights=count, link="probit", Hess=TRUE)
   summary(m1) # Very similar to Gilmour results
-  ranef(m1) # sign is opposite of SAS
+  ordinal::ranef(m1) # sign is opposite of SAS
 
   ## SAS var of sires .04849
   ## Effect 	Shape 	Estimate 	Standard Error 	DF 	t Value 	Pr > |t|
@@ -105,7 +105,7 @@
 
   # Plot random sire effects with intervals, similar to SAS example
   plot.random <- function(model, random.effect, ylim=NULL, xlab="", main="") {
-    tab <- ranef(model)[[random.effect]]
+    tab <- ordinal::ranef(model)[[random.effect]]
     tab <- data.frame(lab=rownames(tab), est=tab$"(Intercept)")
     tab <- transform(tab,
                      lo = est - 1.96 * sqrt(model$condVar),
diff -Nru agridat-1.17/man/ars.earlywhitecorn96.Rd agridat-1.18/man/ars.earlywhitecorn96.Rd
--- agridat-1.17/man/ars.earlywhitecorn96.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/ars.earlywhitecorn96.Rd	2020-12-11 02:45:26.000000000 +0000
@@ -46,25 +46,27 @@
   Agricultural Research Service Special Report 502.
 }
 \examples{
+\dontrun{
+  
+  library(agridat)
+  
+  data(ars.earlywhitecorn96)
+  dat <- ars.earlywhitecorn96
 
-library(agridat)
-
-data(ars.earlywhitecorn96)
-dat <- ars.earlywhitecorn96
-
-libs(lattice)
-# These views emphasize differences between locations
-dotplot(gen~yield, dat, group=loc, auto.key=list(columns=3),
+  libs(lattice)
+  # These views emphasize differences between locations
+  dotplot(gen~yield, dat, group=loc, auto.key=list(columns=3),
+          main="ars.earlywhitecorn96")
+  ## dotplot(gen~stalklodge, dat, group=loc, auto.key=list(columns=3),
+  ##         main="ars.earlywhitecorn96")
+  splom(~dat[,3:9], group=dat$loc, auto.key=list(columns=3),
         main="ars.earlywhitecorn96")
-## dotplot(gen~stalklodge, dat, group=loc, auto.key=list(columns=3),
-##         main="ars.earlywhitecorn96")
-splom(~dat[,3:9], group=dat$loc, auto.key=list(columns=3),
-      main="ars.earlywhitecorn96")
-
-# MANOVA
-m1 <- manova(cbind(yield,earht,moisture) ~ gen + loc, dat)
-m1
-summary(m1)
-
+  
+  # MANOVA
+  m1 <- manova(cbind(yield,earht,moisture) ~ gen + loc, dat)
+  m1
+  summary(m1)
+  
+}
 }
 \keyword{datasets}
diff -Nru agridat-1.17/man/australia.soybean.Rd agridat-1.18/man/australia.soybean.Rd
--- agridat-1.17/man/australia.soybean.Rd	2019-11-22 16:47:15.000000000 +0000
+++ agridat-1.18/man/australia.soybean.Rd	2020-12-11 20:56:41.000000000 +0000
@@ -77,7 +77,7 @@
   Chapman and Hall/CRC.
 
   Retrieved from:
-  http://three-mode.leidenuniv.nl/data/soybeaninf.htm
+  https://three-mode.leidenuniv.nl/data/soybeaninf.htm
 }
 \references{  
   K E Basford (1982). The Use of Multidimensional Scaling in Analysing
diff -Nru agridat-1.17/man/baker.barley.uniformity.Rd agridat-1.18/man/baker.barley.uniformity.Rd
--- agridat-1.17/man/baker.barley.uniformity.Rd	2020-07-04 20:26:43.000000000 +0000
+++ agridat-1.18/man/baker.barley.uniformity.Rd	2020-12-11 20:46:40.000000000 +0000
@@ -33,7 +33,7 @@
   Baker, GA and Huberty, MR and Veihmeyer, FJ.  (1952)
   A uniformity trial on unirrigated barley of ten years' duration.
   \emph{Agronomy Journal}, 44, 267-270.
-  http://doi.org/10.2134/agronj1952.00021962004400050011x
+  https://doi.org/10.2134/agronj1952.00021962004400050011x
 }
 
 \examples{
diff -Nru agridat-1.17/man/baker.strawberry.uniformity.Rd agridat-1.18/man/baker.strawberry.uniformity.Rd
--- agridat-1.17/man/baker.strawberry.uniformity.Rd	2020-07-04 20:27:32.000000000 +0000
+++ agridat-1.18/man/baker.strawberry.uniformity.Rd	2020-12-11 20:46:42.000000000 +0000
@@ -39,7 +39,7 @@
   G. A. Baker and R. E. Baker (1953).
   Strawberry Uniformity Yield Trials.
   \emph{Biometrics}, 9, 412-421.
-  http://doi.org/10.2307/3001713
+  https://doi.org/10.2307/3001713
 }
 \references{
   None
diff -Nru agridat-1.17/man/baker.wheat.uniformity.Rd agridat-1.18/man/baker.wheat.uniformity.Rd
--- agridat-1.17/man/baker.wheat.uniformity.Rd	2020-07-04 20:27:52.000000000 +0000
+++ agridat-1.18/man/baker.wheat.uniformity.Rd	2020-12-11 20:56:43.000000000 +0000
@@ -39,21 +39,23 @@
   G. A. Baker, E. B. Roessler (1957).
   Implications of a uniformity trial with small plots of wheat.
   Hilgardia, 27, 183-188.
-  http://hilgardia.ucanr.edu/Abstract/?a=hilg.v27n05p183
+  https://hilgardia.ucanr.edu/Abstract/?a=hilg.v27n05p183
 }
 \references{
   None
 }
 \examples{
+\dontrun{
+  
+  library(agridat)
+  data(baker.wheat.uniformity)
+  dat <- baker.wheat.uniformity
 
-library(agridat)
-data(baker.wheat.uniformity)
-dat <- baker.wheat.uniformity
-
-libs(desplot)
-desplot(dat, yield ~ col*row,
-        flip=TRUE, aspect=1,
-        main="baker.wheat.uniformity")
+  libs(desplot)
+  desplot(dat, yield ~ col*row,
+          flip=TRUE, aspect=1,
+          main="baker.wheat.uniformity")
 
 }
+}
 \keyword{datasets}
diff -Nru agridat-1.17/man/batchelor.uniformity.Rd agridat-1.18/man/batchelor.uniformity.Rd
--- agridat-1.17/man/batchelor.uniformity.Rd	2020-07-04 20:29:47.000000000 +0000
+++ agridat-1.18/man/batchelor.uniformity.Rd	2020-12-11 20:46:45.000000000 +0000
@@ -125,14 +125,14 @@
   L. D. Batchelor and H. S. Reed. (1918).
   Relation of the variability of yields of fruit trees to the accuracy
   of field trials. \emph{J. Agric. Res}, 12, 245--283.
-  http://books.google.com/books?id=Lil6AAAAMAAJ&lr&pg=PA245
+  https://books.google.com/books?id=Lil6AAAAMAAJ&lr&pg=PA245
 }
 \references{
   McCullagh, P. and Clifford, D., (2006).
   Evidence for conformal invariance of crop yields,
   \emph{Proceedings of the Royal Society A: Mathematical, Physical and
     Engineering Science}, 462, 2119--2143.
-  http://doi.org/10.1098/rspa.2006.1667
+  https://doi.org/10.1098/rspa.2006.1667
 }
 \examples{
 \dontrun{
diff -Nru agridat-1.17/man/battese.survey.Rd agridat-1.18/man/battese.survey.Rd
--- agridat-1.17/man/battese.survey.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/battese.survey.Rd	2020-12-11 20:46:47.000000000 +0000
@@ -40,10 +40,10 @@
   An error-components model for prediction of county crop areas using
   survey and satellite data.
   emph{Journal of the American Statistical Association}, 83, 28-36.
-  http://doi.org/10.2307/2288915
+  https://doi.org/10.2307/2288915
 
   Battese (1982) preprint version.
-  http://www.une.edu.au/__data/assets/pdf_file/0017/15542/emetwp15.pdf
+  https://www.une.edu.au/__data/assets/pdf_file/0017/15542/emetwp15.pdf
   
 }
 \references{
diff -Nru agridat-1.17/man/beall.webworms.Rd agridat-1.18/man/beall.webworms.Rd
--- agridat-1.17/man/beall.webworms.Rd	2020-07-04 20:29:59.000000000 +0000
+++ agridat-1.18/man/beall.webworms.Rd	2020-12-11 20:46:49.000000000 +0000
@@ -42,14 +42,14 @@
   The fit and significance of contagious distributions when applied to
   observations on larval insects.
   \emph{Ecology}, 21, 460-474. Table 6.
-  http://doi.org/10.2307/1930285
+  https://doi.org/10.2307/1930285
 }
 
 \references{
   Michal Kosma et al. (2019).
   Over-dispersed count data in crop and agronomy research.
   \emph{Journal of Agronomy and Crop Science}.
-  http://doi.org/10.1111/jac.12333
+  https://doi.org/10.1111/jac.12333
 }
 
 \examples{
diff -Nru agridat-1.17/man/beaven.barley.Rd agridat-1.18/man/beaven.barley.Rd
--- agridat-1.17/man/beaven.barley.Rd	2020-07-04 20:30:16.000000000 +0000
+++ agridat-1.18/man/beaven.barley.Rd	2020-12-11 20:46:51.000000000 +0000
@@ -37,7 +37,7 @@
   On testing varieties of cereals.
   \emph{Biometrika}, 271-293.
   
-  http://doi.org/10.1093/biomet/15.3-4.271
+  https://doi.org/10.1093/biomet/15.3-4.271
 
 }
 \references{
diff -Nru agridat-1.17/man/besag.bayesian.Rd agridat-1.18/man/besag.bayesian.Rd
--- agridat-1.17/man/besag.bayesian.Rd	2019-12-09 21:01:37.000000000 +0000
+++ agridat-1.18/man/besag.bayesian.Rd	2020-12-19 04:07:43.000000000 +0000
@@ -25,7 +25,7 @@
   Besag, J. E., Green, P. J., Higdon, D. and Mengersen, K. (1995).
   Bayesian computation and stochastic systems.
   Statistical Science, 10, 3-66.
-  http://www.jstor.org/stable/2246224
+  https://www.jstor.org/stable/2246224
 }
 \references{
   Davison, A. C. 2003.
@@ -50,7 +50,6 @@
          xlab="row", ylab="yield", main="besag.bayesian")
 
   libs(asreml)
-  # same code for asreml3 and asreml4
 
   # Use asreml to fit a model with AR1 gradient in rows  
   dat <- transform(dat, cf=factor(col), rf=factor(rrow))
diff -Nru agridat-1.17/man/besag.beans.Rd agridat-1.18/man/besag.beans.Rd
--- agridat-1.17/man/besag.beans.Rd	2020-07-04 20:30:38.000000000 +0000
+++ agridat-1.18/man/besag.beans.Rd	2020-12-11 20:46:52.000000000 +0000
@@ -42,7 +42,7 @@
   Julian Besag and Rob Kempton (1986).
   Statistical Analysis of Field Experiments Using Neighbouring Plots.
   \emph{Biometrics}, 42, 231-251.  Table 6.
-  http://doi.org/10.2307/2531047
+  https://doi.org/10.2307/2531047
 }
 \references{
   Kempton, RA and Lockwood, G. (1984).
diff -Nru agridat-1.17/man/besag.checks.Rd agridat-1.18/man/besag.checks.Rd
--- agridat-1.17/man/besag.checks.Rd	2020-07-04 20:31:11.000000000 +0000
+++ agridat-1.18/man/besag.checks.Rd	2020-12-11 20:46:53.000000000 +0000
@@ -39,7 +39,7 @@
   Besag, J.E. & Kempton R.A. (1986).
   Statistical analysis of field experiments using neighbouring plots.
   Biometrics, 42, 231-251.
-  http://doi.org/10.2307/2531047 
+  https://doi.org/10.2307/2531047 
 }
 \references{
   Kempton, Statistical Methods for Plant Variety Evaluation, page 91--92
diff -Nru agridat-1.17/man/besag.elbatan.Rd agridat-1.18/man/besag.elbatan.Rd
--- agridat-1.17/man/besag.elbatan.Rd	2020-07-04 20:31:25.000000000 +0000
+++ agridat-1.18/man/besag.elbatan.Rd	2020-12-11 20:46:55.000000000 +0000
@@ -26,7 +26,7 @@
   Plot dimensions are not given by Besag.
   
   Data retrieved from
-  http://web.archive.org/web/19991008143232/www.stat.duke.edu/~higdon/trials/elbatan.dat
+  https://web.archive.org/web/19991008143232/www.stat.duke.edu/~higdon/trials/elbatan.dat
 
   Used with permission of David Higdon.
 }
@@ -35,7 +35,7 @@
   Bayesian Analysis of Agricultural Field Experiments,
   Journal of the Royal Statistical Society: Series B,61, 691--746.
   Table 1.
-  http://doi.org/10.1111/1467-9868.00201
+  https://doi.org/10.1111/1467-9868.00201
 }
 
 \references{
diff -Nru agridat-1.17/man/besag.endive.Rd agridat-1.18/man/besag.endive.Rd
--- agridat-1.17/man/besag.endive.Rd	2020-07-21 16:26:36.000000000 +0000
+++ agridat-1.18/man/besag.endive.Rd	2020-12-19 04:08:25.000000000 +0000
@@ -39,7 +39,7 @@
   N Friel & A. N Pettitt (2004). Likelihood Estimation and Inference
   for the Autologistic Model.
   \emph{Journal of Computational and Graphical Statistics}, 13:1, 232-246.
-  http://doi.org/10.1198/1061860043029
+  https://doi.org/10.1198/1061860043029
 }
 \examples{
 \dontrun{
@@ -76,30 +76,27 @@
 
 
   libs(asreml)
-  if( utils::packageVersion("asreml") >  "4" ) {
-    # asreml4
     
-    # Now try an AR1xAR1 model.
-    dat2 <- transform(dat, xf=factor(col), yf=factor(row),
-                      pres=as.numeric(disease=="Y"))
-    
-    m2 <- asreml(pres ~ 1, data=dat2,
-                 resid = ~ar1(xf):ar1(yf))
-    # The 0/1 response is arbitrary, but there is some suggestion
-    # of auto-correlation in the x (.17) and y (.10) directions,
-    # suggesting the pattern is more 'patchy' than just random noise,
-    # but is it meaningful?
-    
-    libs(lucid)
-    vc(m2)
-    ##       effect component std.error z.ratio bound %ch
-    ##     xf:yf(R)   0.1301   0.003798    34       P   0
-    ## xf:yf!xf!cor   0.1699   0.01942      8.7     U   0
-    ## xf:yf!yf!cor   0.09842  0.02038      4.8     U   0
-    
-  }
-}
+  # Now try an AR1xAR1 model.
+  dat2 <- transform(dat, xf=factor(col), yf=factor(row),
+                    pres=as.numeric(disease=="Y"))
+  
+  m2 <- asreml(pres ~ 1, data=dat2,
+               resid = ~ar1(xf):ar1(yf))
+  # The 0/1 response is arbitrary, but there is some suggestion
+  # of auto-correlation in the x (.17) and y (.10) directions,
+  # suggesting the pattern is more 'patchy' than just random noise,
+  # but is it meaningful?
+  
+  libs(lucid)
+  vc(m2)
+  ##       effect component std.error z.ratio bound %ch
+  ##     xf:yf(R)   0.1301   0.003798    34       P   0
+  ## xf:yf!xf!cor   0.1699   0.01942      8.7     U   0
+  ## xf:yf!yf!cor   0.09842  0.02038      4.8     U   0
+  
 
 }
+}
 
 \keyword{datasets}
diff -Nru agridat-1.17/man/besag.met.Rd agridat-1.18/man/besag.met.Rd
--- agridat-1.17/man/besag.met.Rd	2020-07-28 17:46:45.000000000 +0000
+++ agridat-1.18/man/besag.met.Rd	2020-12-13 02:59:27.000000000 +0000
@@ -42,7 +42,7 @@
 
   
   Retrieved from
-  http://web.archive.org/web/19990505223413/www.stat.duke.edu/~higdon/trials/nc.dat
+  https://web.archive.org/web/19990505223413/www.stat.duke.edu/~higdon/trials/nc.dat
 
   Used with permission of David Higdon.
 }
@@ -51,7 +51,7 @@
   Bayesian Analysis of Agricultural Field Experiments,
   Journal of the Royal Statistical Society: Series B, 61, 691--746.
   Table 1.
-  http://doi.org/10.1111/1467-9868.00201
+  https://doi.org/10.1111/1467-9868.00201
 }
 
 \examples{
@@ -91,156 +91,75 @@
   # almost identical.
   
   libs(asreml)
-  if( utils::packageVersion("asreml") < "4") {
-    # asreml3
+  # asreml4
 
-    # asreml Using 'rcov' ALWAYS requires sorting the data
-    datm <- datm[order(datm$gen),]
-    m1a <- asreml(yield ~ gen, data=datm,
-                  random = ~ county,
-                  rcov = ~ at(gen):units)
-    
-    libs(lucid)
-    vc(m1a)[1:7,]
-    ##             effect component std.error z.ratio constr
-    ##  county!county.var   1324       838.2      1.6    pos
-    ##   gen_G01!variance     91.93     58.82     1.6    pos
-    ##   gen_G02!variance    210.7     133.9      1.6    pos
-    ##   gen_G03!variance     63.03     40.53     1.6    pos
-    ##   gen_G04!variance    112.1      71.53     1.6    pos
-    ##   gen_G05!variance     28.39     18.63     1.5    pos
-    ##   gen_G06!variance    237.4     150.8      1.6    pos
-    
-    
-    # We get the same results from asreml & lme
-    # plot(m1a$gammas[-1],
-    #      m1l$sigma^2 * c(1, coef(m1l$modelStruct$varStruct, unc = FALSE))^2)
-    
-    
-    # The following example shows how to construct a GxE biplot
-    # from the FA2 model.
-    
-    dat <- besag.met
-    dat <- transform(dat, xf=factor(col), yf=factor(row))
-    dat <- dat[order(dat$county, dat$xf, dat$yf), ]
-    
-    # First, AR1xAR1
-    m1 <- asreml(yield ~ county, data=dat,
-                 random = ~ gen:county,
-                 rcov = ~ at(county):ar1(xf):ar1(yf))
-    # Add FA1.
-    # For ASExtras:::summary.fa, use fa(county,1):gen, NOT gen:fa(county,1)
-    m2 <- update(m1, random=~ gen:fa(county,1))
-    # FA2
-    m3 <- update(m2, random=~ gen:fa(county,2))
-    m3 <- update(m3)
-    
-    # Use the loadings to make a biplot
-    vars <- vc(m3)
-    psi <- vars[grepl(".var$", vars$effect), "component"]
-    la1 <- vars[grepl(".fa1$", vars$effect), "component"]
-    la2 <- vars[grepl(".fa2$", vars$effect), "component"]
-    mat <- as.matrix(data.frame(psi, la1, la2))
-    rot <- svd(mat[,-1])$v # rotation matrix
-    lam <- mat[,-1] %*% rot # Rotate the loadings
-    colnames(lam) <- c("load1", "load2")
-  
-    co3 <- coef(m3)$random # Scores are the GxE coefficients
-    ix1 <- grepl("_Comp1$", rownames(co3))
-    ix2 <- grepl("_Comp2$", rownames(co3))
-    sco <- matrix(c(co3[ix1], co3[ix2]), ncol=2, byrow=FALSE)
-    sco <- sco %*% rot # Rotate the scores
-    dimnames(sco) <- list(levels(dat$gen) , c('load1','load2'))
-    rownames(lam) <- levels(dat$county)
-    sco[,1] <- -1 * sco[,1]
-    lam[,1] <- -1 * lam[,1]
-    biplot(sco, lam, cex=.5, main="FA2 coefficient biplot (asreml3)")
-    # G variance matrix
-    gvar <- lam %*% t(lam) + diag(mat[,1])
-  
-    # Now get predictions and make an ordinary biplot
-    p3 <- predict(m3, data=dat, classify="county:gen")
-    p3 <- p3$pred$pval
-    libs("gge")  
-    bi3 <- gge(p3, predicted.value ~ gen*county, scale=FALSE)
-    if(interactive()) dev.new()
-    # Very similar to the coefficient biplot
-    biplot(bi3, stand=FALSE, # what does 'stand' do?
-         main="SVD biplot of FA2 predictions")
-
-  }
+  # Average reps
+  datm <- aggregate(yield ~ county + gen, data=dat, FUN=mean)
+  # asreml Using 'rcov' ALWAYS requires sorting the data
+  datm <- datm[order(datm$gen),]
+  
+  m1 <- asreml(yield ~ gen, data=datm,
+               random = ~ county,
+               residual = ~ dsum( ~ units|gen))
+  libs(lucid)
+  vc(m1)[1:7,]
+  ##      effect component std.error z.ratio bound %ch
+  ##    county   1324       836.1      1.6     P 0.2
+  ## gen_G01!R     91.98     58.91     1.6     P 0.1
+  ## gen_G02!R    210.6     133.6      1.6     P 0.1
+  ## gen_G03!R     63.06     40.58     1.6     P 0.1
+  ## gen_G04!R    112.1      71.59     1.6     P 0.1
+  ## gen_G05!R     28.35     18.57     1.5     P 0.2
+  ## gen_G06!R    237.4     150.8      1.6     P 0  
+  
+  # We get the same results from asreml & lme
+  # plot(m1$vparameters[-1],
+  #      m1l$sigma^2 * c(1, coef(m1l$modelStruct$varStruct, unc = FALSE))^2)
+  
+  # The following example shows how to construct a GxE biplot
+  # from the FA2 model.
 
-  libs(asreml)
-  if( utils::packageVersion("asreml") >  "4") {
-    # asreml4
+  
+  dat <- besag.met
+  dat <- transform(dat, xf=factor(col), yf=factor(row))
+  dat <- dat[order(dat$county, dat$xf, dat$yf), ]
 
-    # Average reps
-    datm <- aggregate(yield ~ county + gen, data=dat, FUN=mean)
-    # asreml Using 'rcov' ALWAYS requires sorting the data
-    datm <- datm[order(datm$gen),]
-    
-    m1 <- asreml(yield ~ gen, data=datm,
-                 random = ~ county,
-                 residual = ~ dsum( ~ units|gen))
-    libs(lucid)
-    vc(m1)[1:7,]
-    ##      effect component std.error z.ratio bound %ch
-    ##    county   1324       836.1      1.6     P 0.2
-    ## gen_G01!R     91.98     58.91     1.6     P 0.1
-    ## gen_G02!R    210.6     133.6      1.6     P 0.1
-    ## gen_G03!R     63.06     40.58     1.6     P 0.1
-    ## gen_G04!R    112.1      71.59     1.6     P 0.1
-    ## gen_G05!R     28.35     18.57     1.5     P 0.2
-    ## gen_G06!R    237.4     150.8      1.6     P 0  
-
-    # We get the same results from asreml & lme
-    # plot(m1$vparameters[-1],
-    #      m1l$sigma^2 * c(1, coef(m1l$modelStruct$varStruct, unc = FALSE))^2)
-    
-    # The following example shows how to construct a GxE biplot
-    # from the FA2 model.
-
-    
-    dat <- besag.met
-    dat <- transform(dat, xf=factor(col), yf=factor(row))
-    dat <- dat[order(dat$county, dat$xf, dat$yf), ]
-
-    # First, AR1xAR1
-    m1 <- asreml(yield ~ county, data=dat,
-                 random = ~ gen:county,
-                 residual = ~ dsum( ~ ar1(xf):ar1(yf)|county))
-    # Add FA1
-    m2 <- update(m1, random=~gen:fa(county,1)) # rotate.FA=FALSE
-    # FA2
-    m3 <- update(m2, random=~gen:fa(county,2))
-    asreml.options(extra=50)
-    m3 <- update(m3, maxit=50)
-    asreml.options(extra=0)
-
-    # Use the loadings to make a biplot
-    vars <- vc(m3)
-    psi <- vars[grepl("!var$", vars$effect), "component"]
-    la1 <- vars[grepl("!fa1$", vars$effect), "component"]
-    la2 <- vars[grepl("!fa2$", vars$effect), "component"]
-    mat <- as.matrix(data.frame(psi, la1, la2))
-    # I tried using rotate.fa=FALSE, but it did not seem to
-    # give orthogonal vectors.  Rotate by hand.
-    rot <- svd(mat[,-1])$v # rotation matrix
-    lam <- mat[,-1] %*% rot # Rotate the loadings
-    colnames(lam) <- c("load1", "load2")
-
-    co3 <- coef(m3)$random # Scores are the GxE coefficients
-    ix1 <- grepl("_Comp1$", rownames(co3))
-    ix2 <- grepl("_Comp2$", rownames(co3))
-    sco <- matrix(c(co3[ix1], co3[ix2]), ncol=2, byrow=FALSE)
-    sco <- sco %*% rot # Rotate the scores
-    dimnames(sco) <- list(levels(dat$gen) , c('load1','load2'))
-    rownames(lam) <- levels(dat$county)
-    sco[,1:2] <- -1 * sco[,1:2]
-    lam[,1:2] <- -1 * lam[,1:2]
-    biplot(sco, lam, cex=.5, main="FA2 coefficient biplot (asreml4)")
-    # G variance matrix
-    gvar <- lam %*% t(lam) + diag(mat[,1])
+  # First, AR1xAR1
+  m1 <- asreml(yield ~ county, data=dat,
+               random = ~ gen:county,
+               residual = ~ dsum( ~ ar1(xf):ar1(yf)|county))
+  # Add FA1
+  m2 <- update(m1, random=~gen:fa(county,1)) # rotate.FA=FALSE
+  # FA2
+  m3 <- update(m2, random=~gen:fa(county,2))
+  asreml.options(extra=50)
+  m3 <- update(m3, maxit=50)
+  asreml.options(extra=0)
+
+  # Use the loadings to make a biplot
+  vars <- vc(m3)
+  psi <- vars[grepl("!var$", vars$effect), "component"]
+  la1 <- vars[grepl("!fa1$", vars$effect), "component"]
+  la2 <- vars[grepl("!fa2$", vars$effect), "component"]
+  mat <- as.matrix(data.frame(psi, la1, la2))
+  # I tried using rotate.fa=FALSE, but it did not seem to
+  # give orthogonal vectors.  Rotate by hand.
+  rot <- svd(mat[,-1])$v # rotation matrix
+  lam <- mat[,-1] %*% rot # Rotate the loadings
+  colnames(lam) <- c("load1", "load2")
+
+  co3 <- coef(m3)$random # Scores are the GxE coefficients
+  ix1 <- grepl("_Comp1$", rownames(co3))
+  ix2 <- grepl("_Comp2$", rownames(co3))
+  sco <- matrix(c(co3[ix1], co3[ix2]), ncol=2, byrow=FALSE)
+  sco <- sco %*% rot # Rotate the scores
+  dimnames(sco) <- list(levels(dat$gen) , c('load1','load2'))
+  rownames(lam) <- levels(dat$county)
+  sco[,1:2] <- -1 * sco[,1:2]
+  lam[,1:2] <- -1 * lam[,1:2]
+  biplot(sco, lam, cex=.5, main="FA2 coefficient biplot (asreml4)")
+  # G variance matrix
+  gvar <- lam %*% t(lam) + diag(mat[,1])
   
   # Now get predictions and make an ordinary biplot
   p3 <- predict(m3, data=dat, classify="county:gen")
@@ -250,8 +169,7 @@
   if(interactive()) dev.new()
   # Very similar to the coefficient biplot
   biplot(bi3, stand=FALSE, main="SVD biplot of FA2 predictions")
-
-}
+  
 }
 }
 
diff -Nru agridat-1.17/man/besag.triticale.Rd agridat-1.18/man/besag.triticale.Rd
--- agridat-1.17/man/besag.triticale.Rd	2020-07-21 16:27:01.000000000 +0000
+++ agridat-1.18/man/besag.triticale.Rd	2020-12-19 04:09:09.000000000 +0000
@@ -31,7 +31,7 @@
   Julian Besag and Rob Kempton (1986).
   Statistical Analysis of Field Experiments Using Neighbouring Plots.
   \emph{Biometrics}, 42, 231-251. Table 2.
-  http://doi.org/10.2307/2531047
+  https://doi.org/10.2307/2531047
 }
 \references{
   None.
@@ -66,36 +66,33 @@
          as.table=TRUE, type="s", layout=c(1,3),
          main="besag.triticale")
   
-  libs(asreml)
-  if( utils::packageVersion("asreml") >  "4") {
-    # asreml4
+  libs(asreml) # asreml4
 
-    # Besag uses an adjustment based on neighboring plots.
-    # This analysis fits the standard AR1xAR1 residual model
-
-    dat <- dat[order(dat$xf, dat$yf), ]
-    m3 <- asreml(yield ~ gen + rate + nitro + regulator +
-                   gen:rate + gen:nitro + gen:regulator +
-                   rate:nitro + rate:regulator +
-                   nitro:regulator + yf, data=dat,
-                 resid = ~ ar1(xf):ar1(yf))
-    wald(m3) # Strongly significant gen, rate, regulator
-    ##                 Df Sum of Sq Wald statistic Pr(Chisq)    
-    ## (Intercept)      1   1288255        103.971 < 2.2e-16 ***
-    ## gen              2    903262         72.899 < 2.2e-16 ***
-    ## rate             1    104774          8.456  0.003638 ** 
-    ## nitro            1       282          0.023  0.880139    
-    ## regulator        2    231403         18.676 8.802e-05 ***
-    ## yf               2      3788          0.306  0.858263    
-    ## gen:rate         2      1364          0.110  0.946461    
-    ## gen:nitro        2     30822          2.488  0.288289    
-    ## gen:regulator    4     37269          3.008  0.556507    
-    ## rate:nitro       1      1488          0.120  0.728954    
-    ## rate:regulator   2     49296          3.979  0.136795    
-    ## nitro:regulator  2     41019          3.311  0.191042    
-    ## residual (MS)          12391                             
-
-}
+  # Besag uses an adjustment based on neighboring plots.
+  # This analysis fits the standard AR1xAR1 residual model
+  
+  dat <- dat[order(dat$xf, dat$yf), ]
+  m3 <- asreml(yield ~ gen + rate + nitro + regulator +
+                 gen:rate + gen:nitro + gen:regulator +
+                 rate:nitro + rate:regulator +
+                 nitro:regulator + yf, data=dat,
+               resid = ~ ar1(xf):ar1(yf))
+  wald(m3) # Strongly significant gen, rate, regulator
+  ##                 Df Sum of Sq Wald statistic Pr(Chisq)    
+  ## (Intercept)      1   1288255        103.971 < 2.2e-16 ***
+  ## gen              2    903262         72.899 < 2.2e-16 ***
+  ## rate             1    104774          8.456  0.003638 ** 
+  ## nitro            1       282          0.023  0.880139    
+  ## regulator        2    231403         18.676 8.802e-05 ***
+  ## yf               2      3788          0.306  0.858263    
+  ## gen:rate         2      1364          0.110  0.946461    
+  ## gen:nitro        2     30822          2.488  0.288289    
+  ## gen:regulator    4     37269          3.008  0.556507    
+  ## rate:nitro       1      1488          0.120  0.728954    
+  ## rate:regulator   2     49296          3.979  0.136795    
+  ## nitro:regulator  2     41019          3.311  0.191042    
+  ## residual (MS)          12391                             
+  
 }
 }
 \keyword{datasets}
diff -Nru agridat-1.17/man/blackman.wheat.Rd agridat-1.18/man/blackman.wheat.Rd
--- agridat-1.17/man/blackman.wheat.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/blackman.wheat.Rd	2020-12-11 20:47:02.000000000 +0000
@@ -36,7 +36,7 @@
   Response of semi-dwarf and conventional winter wheat varieties to the
   application of nitrogen fertilizer.
   \emph{The Journal of Agricultural Science}, 90, 543--550.
-  http://doi.org/10.1017/S0021859600056070
+  https://doi.org/10.1017/S0021859600056070
 }
 \references{
   Gower, J. and Lubbe, S.G. and Gardner, S. and Le Roux, N. (2011).
diff -Nru agridat-1.17/man/bliss.borers.Rd agridat-1.18/man/bliss.borers.Rd
--- agridat-1.17/man/bliss.borers.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/bliss.borers.Rd	2020-12-11 20:47:05.000000000 +0000
@@ -30,12 +30,12 @@
   C. Bliss and R. A. Fisher. (1953).
   Fitting the Negative Binomial Distribution to Biological Data.
   \emph{Biometrics}, 9, 176--200.  Table 3.
-  http://doi.org/10.2307/3001850
+  https://doi.org/10.2307/3001850
 
   Geoffrey Beall. 1940. The Fit and Significance of Contagious
   Distributions when Applied to Observations on Larval Insects.
   \emph{Ecology}, 21, 460-474.  Page 463.
-  http://doi.org/10.2307/1930285
+  https://doi.org/10.2307/1930285
 }
 
 \examples{
diff -Nru agridat-1.17/man/bond.diallel.Rd agridat-1.18/man/bond.diallel.Rd
--- agridat-1.17/man/bond.diallel.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/bond.diallel.Rd	2020-12-11 20:47:07.000000000 +0000
@@ -25,30 +25,47 @@
   }
 }
 \details{
-  Yield in grams/plot for diallel crosses between inbred lines of winter
-  beans.  Values are means over two years.
+  Yield in grams/plot for full diallel cross between 6 inbred lines
+  of winter beans.  Values are means over two years.
 }
 \source{
   D. A. Bond (1966). Yield and components of yield in diallel crosses
   between inbred lines of winter beans (Viciafaba).
   \emph{The Journal of Agricultural Science}, 67, 325--336.
-  http://doi.org/10.1017/S0021859600017329
+  https://doi.org/10.1017/S0021859600017329
 }
 \references{
   Peter John, \emph{Statistical Design and Analysis of Experiments}, p. 85.
 }
 \examples{
+\dontrun{
+  
+  library(agridat)
+  data(bond.diallel)
+  dat <- bond.diallel
+  
+  # Because these data are means, we will not be able to reproduce
+  # the anova table in Bond. More useful as a multivariate example.
 
-data(bond.diallel)
-dat <- bond.diallel
+  libs(corrgram)
+  corrgram(dat[ , 3:11], main="bond.diallel",
+           lower=panel.pts)
 
-libs(lattice)
-splom(dat[,3:11], main="bond.diallel")
+  # Multivariate example from sommer package
+  corrgram(dat[,c("stems","pods","seeds")],
+           lower=panel.pts, upper=panel.conf, main="bond.diallel")
+  libs(sommer)           
+  
+  m1 <- mmer(cbind(stems,pods,seeds) ~ 1,
+             random= ~ vs(female)+vs(male),
+             rcov= ~ vs(units),
+             dat)
 
-# Needs an example.  Bond says yield heterosis of F1 hybrids over parent
-# means is 22.56, but I cannot match.
-
-# See man page for FDdata in R package sommer
+  #### genetic variance covariance
+  cov2cor(m1$sigma$`u:female`)
+  cov2cor(m1$sigma$`u:male`)
+  cov2cor(m1$sigma$`u:units`)
 
 }
+}
 \keyword{datasets}
diff -Nru agridat-1.17/man/bose.multi.uniformity.Rd agridat-1.18/man/bose.multi.uniformity.Rd
--- agridat-1.17/man/bose.multi.uniformity.Rd	2020-07-04 20:32:52.000000000 +0000
+++ agridat-1.18/man/bose.multi.uniformity.Rd	2020-12-11 20:56:45.000000000 +0000
@@ -48,7 +48,7 @@
 \references{
   Shaw (1935).
   Handbook of Statistics for Use in Plant-Breeding and Agricultural Problems, p. 149-170.
-  http://krishikosh.egranth.ac.in/handle/1/21153
+  https://krishikosh.egranth.ac.in/handle/1/21153
 }
 \examples{
 \dontrun{
diff -Nru agridat-1.17/man/box.cork.Rd agridat-1.18/man/box.cork.Rd
--- agridat-1.17/man/box.cork.Rd	2020-07-29 18:28:54.000000000 +0000
+++ agridat-1.18/man/box.cork.Rd	2020-12-11 20:47:10.000000000 +0000
@@ -22,7 +22,7 @@
   C.R. Rao (1948).
   Tests of significance in multivariate analysis. 
   \emph{Biometrika}, 35, 58-79.
-  http://doi.org/10.2307/2332629
+  https://doi.org/10.2307/2332629
   
 }
 
diff -Nru agridat-1.17/man/bradley.multi.uniformity.Rd agridat-1.18/man/bradley.multi.uniformity.Rd
--- agridat-1.17/man/bradley.multi.uniformity.Rd	2020-07-04 20:34:31.000000000 +0000
+++ agridat-1.18/man/bradley.multi.uniformity.Rd	2020-12-11 20:56:45.000000000 +0000
@@ -72,7 +72,7 @@
   P. L. Bradley (1941).
   A study of the variation in productivity over a number of fixed plots in field 2.
   Dissertation: The University of the West Indies. Appendix 1a, 1b, 1c, 1d.
-  http://hdl.handle.net/2139/41264
+  https://hdl.handle.net/2139/41264
 
   The data are repeated in:
   C. E. Wilson.
@@ -80,7 +80,7 @@
   plot-fertility data for use in future experiments on these plots,
   season 1940-41.
   Dissertation: The University of the West Indies.  Page 36-39.
-  http://uwispace.sta.uwi.edu/dspace/handle/2139/43658
+  https://uwispace.sta.uwi.edu/dspace/handle/2139/43658
 }
 \references{
   None
diff -Nru agridat-1.17/man/brandt.switchback.Rd agridat-1.18/man/brandt.switchback.Rd
--- agridat-1.17/man/brandt.switchback.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/brandt.switchback.Rd	2020-12-11 20:56:46.000000000 +0000
@@ -45,10 +45,11 @@
 \source{
   A.E. Brandt (1938). Tests of Significance in Reversal or Switchback Trials
   Iowa State College, Agricultural Research Bulletins. Bulletin 234. Book 22.
-  http://lib.dr.iastate.edu/ag_researchbulletins/22/
+  https://lib.dr.iastate.edu/ag_researchbulletins/22/
 }
 \examples{
-
+\dontrun{
+  
 library(agridat)
 
 data(brandt.switchback)
@@ -72,4 +73,5 @@
 anova(m1)
 
 }
+}
 \keyword{datasets}
diff -Nru agridat-1.17/man/bridges.cucumber.Rd agridat-1.18/man/bridges.cucumber.Rd
--- agridat-1.17/man/bridges.cucumber.Rd	2020-07-21 16:33:39.000000000 +0000
+++ agridat-1.18/man/bridges.cucumber.Rd	2020-12-19 04:10:01.000000000 +0000
@@ -66,61 +66,12 @@
   bwplot(yield ~ loc|factor(.sample), dat20,
          main="bridges.cucumber - graphical inference")
 
-
-  ## libs(asreml)
-  ## if( utils::packageVersion("asreml") < "4") {
-  ##   # asreml3
-  
-  ##   ## Random row/col/resid. Same as Bridges 1989, p. 147
-  ##   m1 <- asreml(yield ~ 1 + gen + loc + loc:gen,
-  ##                random = ~ rowf:loc + colf:loc, data=dat)
-
-  ##   libs(lucid)
-  ##   vc(m1)
-  ##   ##             effect component std.error z.ratio constr
-  ##   ##  rowf:loc!rowf.var     31.62     23.02     1.4    pos
-  ##   ##  colf:loc!colf.var     18.08     15.32     1.2    pos
-  ##   ##         R!variance     31.48     12.85     2.4    pos
-  
-  ##   ## Random row/col/resid at each loc. Matches p. 147
-  ##   m2 <- asreml(yield ~ 1 + gen + loc + loc:gen,
-  ##                random = ~ at(loc):rowf + at(loc):colf, data=dat,
-  ##                rcov = ~at(loc):units)
-  ##   vc(m2)
-  ##   ##                          effect component std.error z.ratio constr
-  ##   ##  at(loc, Clemson):rowf!rowf.var     32.32    36.58     0.88    pos
-  ##   ##   at(loc, Tifton):rowf!rowf.var     30.92    28.63     1.1     pos
-  ##   ##  at(loc, Clemson):colf!colf.var     22.55    28.78     0.78    pos
-  ##   ##   at(loc, Tifton):colf!colf.var     13.62    14.59     0.93    pos
-  ##   ##            loc_Clemson!variance     46.85    27.05     1.7     pos
-  ##   ##             loc_Tifton!variance     16.11     9.299    1.7     pos
-  
-  ##   predict(m2, data=dat, classify='loc:gen')$predictions$pvals
-  ##   ##     loc      gen Predicted Std Err    Status
-  ##   ## Clemson   Dasher     45.55   5.043 Estimable
-  ##   ## Clemson Guardian     31.62   5.043 Estimable
-  ##   ## Clemson Poinsett     21.42   5.043 Estimable
-  ##   ## Clemson   Sprint     25.95   5.043 Estimable
-  ##   ##  Tifton   Dasher     50.48   3.894 Estimable
-  ##   ##  Tifton Guardian     38.72   3.894 Estimable
-  ##   ##  Tifton Poinsett     33.01   3.894 Estimable
-  ##   ##  Tifton   Sprint     39.18   3.894 Estimable
+  libs(asreml) # asreml4
     
-  ##   # Is a heterogeneous model justified? Maybe not.
-  ##   # m1$loglik
-  ##   ## -67.35585
-  ##   # m2$loglik
-  ##   ## -66.35621
-  ## }
-
-  libs(asreml)
-  if( utils::packageVersion("asreml") >  "4") {
-    # asreml4
-    
-    ## Random row/col/resid. Same as Bridges 1989, p. 147
-    m1 <- asreml(yield ~ 1 + gen + loc + loc:gen,
-                 random = ~ rowf:loc + colf:loc, data=dat)
-
+  ## Random row/col/resid. Same as Bridges 1989, p. 147
+  m1 <- asreml(yield ~ 1 + gen + loc + loc:gen,
+               random = ~ rowf:loc + colf:loc, data=dat)
+  
   libs(lucid)
   lucid::vc(m1)
   ##   effect component std.error z.ratio bound %ch
@@ -151,14 +102,13 @@
   ## 6  Tifton Guardian            38.7      3.89 Estimable
   ## 7  Tifton Poinsett            33        3.89 Estimable
   ## 8  Tifton   Sprint            39.2      3.89 Estimable
-
+  
   # Is a heterogeneous model justified? Maybe not.
   # m1$loglik
   ## -67.35585
   # m2$loglik
   ## -66.35621
 
-  }
 }
 }
 
diff -Nru agridat-1.17/man/broadbalk.wheat.Rd agridat-1.18/man/broadbalk.wheat.Rd
--- agridat-1.17/man/broadbalk.wheat.Rd	2019-10-29 00:41:31.000000000 +0000
+++ agridat-1.18/man/broadbalk.wheat.Rd	2020-12-11 20:56:47.000000000 +0000
@@ -21,7 +21,7 @@
 
   Note: This data is only 1852-1925.  You can find recent data for these
   experiments at the Electronic Rothamsted Archive:
-  http://www.era.rothamsted.ac.uk/
+  https://www.era.rothamsted.ac.uk/
 
   
   Rothamsted Experiment station conducted wheat experiments on the
@@ -59,14 +59,15 @@
 	\emph{Data: A Collection of Problems from Many Fields for the Student
 	  and Research Worker}.  Springer.
 
-  Retrieved from http://lib.stat.cmu.edu/datasets/Andrews/
+  Retrieved from https://lib.stat.cmu.edu/datasets/Andrews/
 }
 \references{
   Broadbalk Winter Wheat Experiment.
-  http://www.era.rothamsted.ac.uk/index.php?area=home&page=index&dataset=4
+  https://www.era.rothamsted.ac.uk/index.php?area=home&page=index&dataset=4
 }
 \examples{
-
+\dontrun{
+  
 library(agridat)
 data(broadbalk.wheat)
 dat <- broadbalk.wheat
@@ -82,4 +83,5 @@
 levelplot(grain~year*plot, dat, main="broadbalk.wheat: Grain", col.regions=redblue)
 
 }
+}
 \keyword{datasets}
diff -Nru agridat-1.17/man/burgueno.alpha.Rd agridat-1.18/man/burgueno.alpha.Rd
--- agridat-1.17/man/burgueno.alpha.Rd	2020-07-21 16:34:05.000000000 +0000
+++ agridat-1.18/man/burgueno.alpha.Rd	2020-12-19 04:10:34.000000000 +0000
@@ -62,42 +62,40 @@
   ##   Residual        <NA> <NA> 133200 365  
 
 
-  libs(asreml)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
-    
-    dat <- transform(dat, xf=factor(col), yf=factor(row))
-    dat <- dat[order(dat$xf, dat$yf),]                 
-    
-    # Sequence of models on page 36
-    
-    m1 <- asreml(yield ~  gen, data=dat)
-    m1$loglik # -232.13
-    
-    m2 <- asreml(yield ~  gen, data=dat,
-                 random = ~ rep)
-    m2$loglik # -223.48
-    
-    # Inc Block model
-    m3 <- asreml(yield ~  gen, data=dat,
-                 random = ~ rep/block)
-    m3$loglik # -221.42
-    m3$coef$fixed # Matches solution on p. 27
-    
-    # AR1xAR1 model
-    m4 <- asreml(yield ~ 1 + gen, data=dat,
-                 resid = ~ar1(xf):ar1(yf))
-    m4$loglik # -221.47
-    plot(varioGram(m4), main="burgueno.alpha") # Figure 1
-    
-    m5 <- asreml(yield ~ 1 + gen, data=dat,
-                 random= ~ yf, resid = ~ar1(xf):ar1(yf))
-    m5$loglik # -220.07
-    
-    m6 <- asreml(yield ~ 1 + gen + pol(yf,-2), data=dat,
-                 resid = ~ar1(xf):ar1(yf))
-    m6$loglik # -204.64
+  libs(asreml) # asreml4
     
+  dat <- transform(dat, xf=factor(col), yf=factor(row))
+  dat <- dat[order(dat$xf, dat$yf),]                 
+  
+  # Sequence of models on page 36
+  
+  m1 <- asreml(yield ~  gen, data=dat)
+  m1$loglik # -232.13
+  
+  m2 <- asreml(yield ~  gen, data=dat,
+               random = ~ rep)
+  m2$loglik # -223.48
+  
+  # Inc Block model
+  m3 <- asreml(yield ~  gen, data=dat,
+               random = ~ rep/block)
+  m3$loglik # -221.42
+  m3$coef$fixed # Matches solution on p. 27
+  
+  # AR1xAR1 model
+  m4 <- asreml(yield ~ 1 + gen, data=dat,
+               resid = ~ar1(xf):ar1(yf))
+  m4$loglik # -221.47
+  plot(varioGram(m4), main="burgueno.alpha") # Figure 1
+  
+  m5 <- asreml(yield ~ 1 + gen, data=dat,
+               random= ~ yf, resid = ~ar1(xf):ar1(yf))
+  m5$loglik # -220.07
+  
+  m6 <- asreml(yield ~ 1 + gen + pol(yf,-2), data=dat,
+               resid = ~ar1(xf):ar1(yf))
+  m6$loglik # -204.64
+  
   m7 <- asreml(yield ~ 1 + gen + lin(yf), data=dat,
                random= ~ spl(yf), resid = ~ar1(xf):ar1(yf))
   m7$loglik # -212.51
@@ -105,7 +103,7 @@
   m8 <- asreml(yield ~ 1 + gen + lin(yf), data=dat,
                random= ~ spl(yf))
   m8$loglik # -213.91
-
+  
   # Polynomial model with predictions
   m9 <- asreml(yield ~ 1 + gen + pol(yf,-2) + pol(xf,-2), data=dat,
                random= ~ spl(yf),
@@ -133,7 +131,6 @@
                 random= ~ spl(yf)+spl(xf))
   m13$loglik # -207.52
 
-  }
 }
 }
 
diff -Nru agridat-1.17/man/burgueno.rowcol.Rd agridat-1.18/man/burgueno.rowcol.Rd
--- agridat-1.17/man/burgueno.rowcol.Rd	2020-07-21 16:34:24.000000000 +0000
+++ agridat-1.18/man/burgueno.rowcol.Rd	2020-12-19 04:10:55.000000000 +0000
@@ -61,29 +61,27 @@
   ##      rep (Intercept) <NA> 0.1916 0.4378
   ## Residual        <NA> <NA> 0.1796 0.4238
   
-  libs(asreml)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
+  libs(asreml) # asreml4
 
-    # AR1 x AR1 with linear row/col effects, random spline row/col
-    dat <- transform(dat, xf=factor(col), yf=factor(row))
-    dat <- dat[order(dat$xf,dat$yf),]
-    m2 <- asreml(yield ~ gen + lin(yf) + lin(xf), data=dat,
-                 random = ~ spl(yf) + spl(xf),
-                 resid = ~ ar1(xf):ar1(yf))
-    m2 <- update(m2) # More iterations
-
-    # Scaling of spl components has changed in asreml from old versions
-    libs(lucid)
-    vc(m2) # Match Burgueno p. 42
-    ##       effect component std.error z.ratio bound %ch
-    ##      spl(yf)  0.09077    0.08252   1.1       P 0
-    ##      spl(xf)  0.08107    0.08209   0.99      P 0
-    ##     xf:yf(R)  0.1482     0.03119   4.8       P 0
-    ## xf:yf!xf!cor  0.1152     0.2269    0.51      U 0.1
-    ## xf:yf!yf!cor  0.009467   0.2414    0.039     U 0.9
+  # AR1 x AR1 with linear row/col effects, random spline row/col
+  dat <- transform(dat, xf=factor(col), yf=factor(row))
+  dat <- dat[order(dat$xf,dat$yf),]
+  m2 <- asreml(yield ~ gen + lin(yf) + lin(xf), data=dat,
+               random = ~ spl(yf) + spl(xf),
+               resid = ~ ar1(xf):ar1(yf))
+  m2 <- update(m2) # More iterations
+  
+  # Scaling of spl components has changed in asreml from old versions
+  libs(lucid)
+  vc(m2) # Match Burgueno p. 42
+  ##       effect component std.error z.ratio bound %ch
+  ##      spl(yf)  0.09077    0.08252   1.1       P 0
+  ##      spl(xf)  0.08107    0.08209   0.99      P 0
+  ##     xf:yf(R)  0.1482     0.03119   4.8       P 0
+  ## xf:yf!xf!cor  0.1152     0.2269    0.51      U 0.1
+  ## xf:yf!yf!cor  0.009467   0.2414    0.039     U 0.9
+  
+  plot(varioGram(m2), main="burgueno.rowcol")
 
-    plot(varioGram(m2), main="burgueno.rowcol")
-  }
 }
 }
diff -Nru agridat-1.17/man/burgueno.unreplicated.Rd agridat-1.18/man/burgueno.unreplicated.Rd
--- agridat-1.17/man/burgueno.unreplicated.Rd	2020-07-21 16:34:47.000000000 +0000
+++ agridat-1.18/man/burgueno.unreplicated.Rd	2020-12-19 04:11:25.000000000 +0000
@@ -54,42 +54,39 @@
           col=check, num=gen, #text=gen, cex=.3, # aspect unknown
           main="burgueno.unreplicated")
 
-  libs(asreml,lucid)
-    if( utils::packageVersion("asreml") > "4") {
-    # asreml4
+  libs(asreml,lucid) # asreml4
 
-    # AR1 x AR1 with random genotypes
-    dat <- transform(dat, xf=factor(col), yf=factor(row))
-    dat <- dat[order(dat$xf,dat$yf),]
-    m2 <- asreml(yield ~ 1, data=dat, random = ~ gen,
-                 resid = ~ ar1(xf):ar1(yf))
-    vc(m2)
-    ##       effect component std.error z.ratio bound %ch
-    ##          gen    0.9122   0.127       7.2     P 0  
-    ##     xf:yf(R)    0.4993   0.05601     8.9     P 0  
-    ## xf:yf!xf!cor   -0.2431   0.09156    -2.7     U 0  
-    ## xf:yf!yf!cor    0.1255   0.07057     1.8     U 0.1
-    
-    # Note the strong saw-tooth pattern in the variogram.  Seems to
-    # be column effects.
-    plot(varioGram(m2), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
-         main="burgueno.unreplicated - AR1xAR1")
-    # libs(lattice) # Show how odd columns are high
-    # bwplot(resid(m2) ~ col, data=dat, horizontal=FALSE)
-    
-    # Define an even/odd column factor as fixed effect
-    # dat$oddcol <- factor(dat$col %% 2)
-    # The modulus operator throws a bug, so do it the hard way.
-    dat$oddcol <- factor(dat$col - floor(dat$col / 2) *2 )
-    
-    m3 <- update(m2, yield ~ 1 + oddcol)
-    m3$loglik # Matches Burgueno table 3, line 3
-    
-    plot(varioGram(m3), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
-         main="burgueno.unreplicated - AR1xAR1 + Even/Odd")
-    # Much better-looking variogram
-    
-}
+  # AR1 x AR1 with random genotypes
+  dat <- transform(dat, xf=factor(col), yf=factor(row))
+  dat <- dat[order(dat$xf,dat$yf),]
+  m2 <- asreml(yield ~ 1, data=dat, random = ~ gen,
+               resid = ~ ar1(xf):ar1(yf))
+  vc(m2)
+  ##       effect component std.error z.ratio bound %ch
+  ##          gen    0.9122   0.127       7.2     P 0  
+  ##     xf:yf(R)    0.4993   0.05601     8.9     P 0  
+  ## xf:yf!xf!cor   -0.2431   0.09156    -2.7     U 0  
+  ## xf:yf!yf!cor    0.1255   0.07057     1.8     U 0.1
+  
+  # Note the strong saw-tooth pattern in the variogram.  Seems to
+  # be column effects.
+  plot(varioGram(m2), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
+       main="burgueno.unreplicated - AR1xAR1")
+  # libs(lattice) # Show how odd columns are high
+  # bwplot(resid(m2) ~ col, data=dat, horizontal=FALSE)
+  
+  # Define an even/odd column factor as fixed effect
+  # dat$oddcol <- factor(dat$col %% 2)
+  # The modulus operator throws a bug, so do it the hard way.
+  dat$oddcol <- factor(dat$col - floor(dat$col / 2) *2 )
+  
+  m3 <- update(m2, yield ~ 1 + oddcol)
+  m3$loglik # Matches Burgueno table 3, line 3
+  
+  plot(varioGram(m3), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
+       main="burgueno.unreplicated - AR1xAR1 + Even/Odd")
+  # Much better-looking variogram
+  
 }
 }
 
diff -Nru agridat-1.17/man/butron.maize.Rd agridat-1.18/man/butron.maize.Rd
--- agridat-1.17/man/butron.maize.Rd	2020-07-28 22:25:21.000000000 +0000
+++ agridat-1.18/man/butron.maize.Rd	2020-12-11 20:47:11.000000000 +0000
@@ -50,7 +50,7 @@
   Yield evaluation of maize cultivars across environments with different
   levels of pink stem borer infestation.
   Crop Science, 44, 741-747.
-  http://doi.org/10.2135/cropsci2004.7410
+  https://doi.org/10.2135/cropsci2004.7410
 }
 
 \examples{
@@ -97,88 +97,45 @@
   }
 
 
-  # asreml 3
+  # asreml 4
   if(0){
     libs(asreml)
-    if( utils::packageVersion("asreml") < "4") {
-      # asreml3
+    ped.ainv <- ainverse(ped)
       
-      ped.ainv <- asreml.Ainverse(ped)$ginv
-
-      m0 <- asreml(yield ~ 1+env, random = ~ gen, data=dat)
-      m1 <- asreml(yield ~ 1+env, random = ~ ped(gen),
-                   ginverse=list(gen=ped.ainv), data=dat)
-      m2 <- update(m1, random = ~ id(env):ped(gen))
-      m3 <- update(m2, random = ~ diag(env):ped(gen))
-      m4 <- update(m3, random = ~ fa(env,1):ped(gen))
-      ## AIC(m0,m1,m2,m3,m4)
-      ##    df      AIC
-      ## m0  2 229.4037
-      ## m1  2 213.2487
-      ## m2  2 290.6156
-      ## m3  6 296.8061
-      ## m4 11 218.1568
-      
-      p0 <- predict(m0, data=dat, classify="gen")$pred$pvals
-      p4 <- predict(m4, data=dat, classify="gen")$pred$pvals
-      p4par <- p4[1:12,]   # parents
-      p4 <- p4[-c(1:12),]  # hybrids
-      # Careful!  Need to manually sort the predictions
-      p0 <- p0[order(as.character(p0$gen)),]
-      p4 <- p4[order(as.character(p4$gen)),]
-      
-      # lims <- range(c(p0$pred, p4$pred)) * c(.95,1.05)
-      lims <- c(6,8.25) # zoom in on the higher-yielding hybrids
-      plot(p0$predicted.value, p4$predicted.value,
-           pch="", xlim=lims, ylim=lims, main="butron.maize",
-           xlab="BLUP w/o pedigree", ylab="BLUP with pedigree")
-      abline(0,1,col="lightgray")
-      text(x=p0$predicted.value, y=p4$predicted.value, p0$gen, cex=.5, srt=-45)
-      text(x=min(lims), y=p4par$predicted.value, p4par$gen, cex=.5)
-    }
-
-    # asreml 4
-    libs(asreml)
-    if( utils::packageVersion("asreml") > "4") {
-      # asreml4
-      ped.ainv <- ainverse(ped)
-      
-      m0 <- asreml(yield ~ 1+env, data=dat, random = ~ gen)
-      m1 <- asreml(yield ~ 1+env, random = ~ vm(gen, ped.ainv), data=dat)
-      m2 <- update(m1, random = ~ idv(env):vm(gen, ped.ainv))
-      m3 <- update(m2, random = ~ diag(env):vm(gen, ped.ainv))
-      m4 <- update(m3, random = ~ fa(env,1):vm(gen, ped.ainv))
-      #summary(m0)$aic
-      #summary(m4)$aic
-      ##    df      AIC
-      ## m0  2 229.4037
-      ## m1  2 213.2487
-      ## m2  2 290.6156
-      ## m3  6 296.8061
-      ## m4 11 218.1568
-      
-      p0 <- predict(m0, data=dat, classify="gen")$pvals
-      p1 <- predict(m1, data=dat, classify="gen")$pvals
-      p1par <- p1[1:12,]   # parents
-      p1 <- p1[-c(1:12),]  # remove parents
-      # Careful!  Need to manually sort the predictions
-      p0 <- p0[order(as.character(p0$gen)),]
-      p1 <- p1[order(as.character(p1$gen)),]
-      
-      # lims <- range(c(p0$pred, p1$pred)) * c(.95,1.05)
-      lims <- c(6,8.25) # zoom in on the higher-yielding hybrids
-      plot(p0$predicted.value, p1$predicted.value,
-           pch="", xlim=lims, ylim=lims, main="butron.maize",
-           xlab="BLUP w/o pedigree", ylab="BLUP with pedigree")
-      abline(0,1,col="lightgray")
-      text(x=p0$predicted.value, y=p1$predicted.value,
-           p0$gen, cex=.5, srt=-45)
-      text(x=min(lims), y=p1par$predicted.value, p1par$gen, cex=.5, col="red")
-      round( cor(p0$predicted.value, p1$predicted.value), 3)
-      # Including the pedigree provided very little change
+    m0 <- asreml(yield ~ 1+env, data=dat, random = ~ gen)
+    m1 <- asreml(yield ~ 1+env, random = ~ vm(gen, ped.ainv), data=dat)
+    m2 <- update(m1, random = ~ idv(env):vm(gen, ped.ainv))
+    m3 <- update(m2, random = ~ diag(env):vm(gen, ped.ainv))
+    m4 <- update(m3, random = ~ fa(env,1):vm(gen, ped.ainv))
+    #summary(m0)$aic
+    #summary(m4)$aic
+    ##    df      AIC
+    ## m0  2 229.4037
+    ## m1  2 213.2487
+    ## m2  2 290.6156
+    ## m3  6 296.8061
+    ## m4 11 218.1568
+    
+    p0 <- predict(m0, data=dat, classify="gen")$pvals
+    p1 <- predict(m1, data=dat, classify="gen")$pvals
+    p1par <- p1[1:12,]   # parents
+    p1 <- p1[-c(1:12),]  # remove parents
+    # Careful!  Need to manually sort the predictions
+    p0 <- p0[order(as.character(p0$gen)),]
+    p1 <- p1[order(as.character(p1$gen)),]
+    
+    # lims <- range(c(p0$pred, p1$pred)) * c(.95,1.05)
+    lims <- c(6,8.25) # zoom in on the higher-yielding hybrids
+    plot(p0$predicted.value, p1$predicted.value,
+         pch="", xlim=lims, ylim=lims, main="butron.maize",
+         xlab="BLUP w/o pedigree", ylab="BLUP with pedigree")
+    abline(0,1,col="lightgray")
+    text(x=p0$predicted.value, y=p1$predicted.value,
+         p0$gen, cex=.5, srt=-45)
+    text(x=min(lims), y=p1par$predicted.value, p1par$gen, cex=.5, col="red")
+    round( cor(p0$predicted.value, p1$predicted.value), 3)
+    # Including the pedigree provided very little change
     
-    }
-  
   }
 }
 }
diff -Nru agridat-1.17/man/byers.apple.Rd agridat-1.18/man/byers.apple.Rd
--- agridat-1.17/man/byers.apple.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/byers.apple.Rd	2020-12-11 03:22:03.000000000 +0000
@@ -37,7 +37,7 @@
 }
 
 \examples{
-
+  
 library(agridat)
 
 data(byers.apple)
diff -Nru agridat-1.17/man/caribbean.maize.Rd agridat-1.18/man/caribbean.maize.Rd
--- agridat-1.17/man/caribbean.maize.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/caribbean.maize.Rd	2020-12-11 20:56:47.000000000 +0000
@@ -41,7 +41,7 @@
 	\emph{Data: A Collection of Problems from Many Fields for the Student
 	  and Research Worker}.
 
-  Retrieved from http://lib.stat.cmu.edu/datasets/Andrews/
+  Retrieved from https://lib.stat.cmu.edu/datasets/Andrews/
 }
 \references{
   Also in the DAAG package as data sets antigua, stVincent.
diff -Nru agridat-1.17/man/carlson.germination.Rd agridat-1.18/man/carlson.germination.Rd
--- agridat-1.17/man/carlson.germination.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/carlson.germination.Rd	2020-12-11 20:47:15.000000000 +0000
@@ -26,7 +26,7 @@
   RE. (1983).
   Alfalfa Seed Germination in Antibiotic Agar Containing NaCl.
   \emph{Crop science}, 23, 882-885.
-  http://doi.org/10.2135/cropsci1983.0011183X002300050016x
+  https://doi.org/10.2135/cropsci1983.0011183X002300050016x
 }
 
 \examples{
diff -Nru agridat-1.17/man/carmer.density.Rd agridat-1.18/man/carmer.density.Rd
--- agridat-1.17/man/carmer.density.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/carmer.density.Rd	2020-12-11 20:47:17.000000000 +0000
@@ -29,7 +29,7 @@
   S G Carmer and J A Jackobs (1965).
   An Exponential Model for Predicting Optimum Plant Density and Maximum
   Corn Yield. \emph{Agronomy Journal}, 57, 241--244.
-  http://doi.org/10.2134/agronj1965.00021962005700030003x
+  https://doi.org/10.2134/agronj1965.00021962005700030003x
 }
 
 \examples{
diff -Nru agridat-1.17/man/cate.potassium.Rd agridat-1.18/man/cate.potassium.Rd
--- agridat-1.17/man/cate.potassium.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/cate.potassium.Rd	2020-12-11 20:47:18.000000000 +0000
@@ -30,7 +30,7 @@
   A simple statistical procedure for partitioning soil test correlation
   data into two classes.
   \emph{Soil Science Society of America Journal}, 35, 658--660.
-  http://doi.org/10.2136/sssaj1971.03615995003500040048x
+  https://doi.org/10.2136/sssaj1971.03615995003500040048x
 }
 
 \examples{
diff -Nru agridat-1.17/man/christidis.cotton.uniformity.Rd agridat-1.18/man/christidis.cotton.uniformity.Rd
--- agridat-1.17/man/christidis.cotton.uniformity.Rd	2020-07-04 20:37:24.000000000 +0000
+++ agridat-1.18/man/christidis.cotton.uniformity.Rd	2020-12-11 17:30:49.000000000 +0000
@@ -22,7 +22,7 @@
 
   Each block consisted of 20 rows, 1 meter apart and 66 meters long. Two
   rows on each side and 1 meter on each end were removed for
-  borderr. Each row was divided into 4 meter-lengths and harvested
+  borderr Each row was divided into 4 meter-lengths and harvested
   separately.  There were 4 blocks, oriented at 0, 30, 60, 90 degrees.
 
   Each block contained 16 rows, each 64 meters long.
diff -Nru agridat-1.17/man/christidis.wheat.uniformity.Rd agridat-1.18/man/christidis.wheat.uniformity.Rd
--- agridat-1.17/man/christidis.wheat.uniformity.Rd	2020-07-04 20:37:37.000000000 +0000
+++ agridat-1.18/man/christidis.wheat.uniformity.Rd	2020-12-11 20:47:21.000000000 +0000
@@ -31,7 +31,7 @@
   Christidis, Basil G (1931).
   The importance of the shape of plots in field experimentation.
   \emph{The Journal of Agricultural Science}, 21, 14-37. Table VI, p. 28.
-  http://dx.doi.org/10.1017/S0021859600007942
+  https://dx.doi.org/10.1017/S0021859600007942
 }
 \references{
   None
diff -Nru agridat-1.17/man/cleveland.soil.Rd agridat-1.18/man/cleveland.soil.Rd
--- agridat-1.17/man/cleveland.soil.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/cleveland.soil.Rd	2020-12-11 20:56:48.000000000 +0000
@@ -23,10 +23,10 @@
 }
 \source{
   William Cleveland, (1993), \emph{Visualizing Data}.
-  Electronic version from StatLib: http://lib.stat.cmu.edu/datasets/
+  Electronic version from StatLib: https://lib.stat.cmu.edu/datasets/
 
   Cleaned version from Luke Tierney
-  http://homepage.stat.uiowa.edu/~luke/classes/248/examples/soil
+  https://homepage.stat.uiowa.edu/~luke/classes/248/examples/soil
 }
 
 \examples{
diff -Nru agridat-1.17/man/cochran.beets.Rd agridat-1.18/man/cochran.beets.Rd
--- agridat-1.17/man/cochran.beets.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/cochran.beets.Rd	2020-12-11 20:47:23.000000000 +0000
@@ -31,7 +31,7 @@
   Interpretation of Adjusted Treatment Means and Regressions in Analysis
   of Covariance.
   \emph{Biometrics}, 13, 282-308.
-  http://doi.org/10.2307/2527917
+  https://doi.org/10.2307/2527917
 }
 \examples{
 
diff -Nru agridat-1.17/man/cochran.crd.Rd agridat-1.18/man/cochran.crd.Rd
--- agridat-1.17/man/cochran.crd.Rd	2020-07-04 20:37:48.000000000 +0000
+++ agridat-1.18/man/cochran.crd.Rd	2020-12-11 20:47:24.000000000 +0000
@@ -39,7 +39,7 @@
   trend analysis for field plot data.
   \emph{Agronomy Journal}, 80, 712-718.
   
-  http://doi.org/10.2134/agronj1988.00021962008000050003x
+  https://doi.org/10.2134/agronj1988.00021962008000050003x
 }
 \examples{
 \dontrun{
diff -Nru agridat-1.17/man/cochran.lattice.Rd agridat-1.18/man/cochran.lattice.Rd
--- agridat-1.17/man/cochran.lattice.Rd	2020-07-04 20:38:26.000000000 +0000
+++ agridat-1.18/man/cochran.lattice.Rd	2020-12-11 20:56:49.000000000 +0000
@@ -46,7 +46,7 @@
   Walter Federer.
   Combining Standard Block Analyses With Spatial Analyses Under a Random
   Effects Model. Cornell Univ Tech Report BU-1373-MA.
-  http://hdl.handle.net/1813/31971
+  https://hdl.handle.net/1813/31971
 
 }
 \examples{
diff -Nru agridat-1.17/man/cochran.wireworms.Rd agridat-1.18/man/cochran.wireworms.Rd
--- agridat-1.17/man/cochran.wireworms.Rd	2020-07-04 20:38:43.000000000 +0000
+++ agridat-1.18/man/cochran.wireworms.Rd	2020-12-11 20:47:26.000000000 +0000
@@ -31,7 +31,7 @@
   Ron Snee (1980). Graphical Display of Means.
   \emph{The American Statistician}, 34, 195-199.
   https://www.jstor.org/stable/2684060 
-  http://doi.org/10.1080/00031305.1980.10483028
+  https://doi.org/10.1080/00031305.1980.10483028
   
   W. Cochran (1940). The analysis of variance when experimental errors
   follow the Poisson or binomial laws.
diff -Nru agridat-1.17/man/connolly.potato.Rd agridat-1.18/man/connolly.potato.Rd
--- agridat-1.17/man/connolly.potato.Rd	2020-07-04 20:38:54.000000000 +0000
+++ agridat-1.18/man/connolly.potato.Rd	2020-12-11 20:47:27.000000000 +0000
@@ -39,7 +39,7 @@
   It would be interesting to fit a model that uses differences in
   maturity between a plot and its neighbor as the actual covariate.
 
-  http://doi.org/10.1111/j.1744-7348.1993.tb04099.x
+  https://doi.org/10.1111/j.1744-7348.1993.tb04099.x
   
   Used with permission of Iain Currie.
 
diff -Nru agridat-1.17/man/cornelius.maize.Rd agridat-1.18/man/cornelius.maize.Rd
--- agridat-1.17/man/cornelius.maize.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/cornelius.maize.Rd	2020-12-11 20:47:29.000000000 +0000
@@ -29,7 +29,7 @@
   Forkman, Johannes and Piepho, Hans-Peter. (2014).
   Parametric bootstrap methods for testing multiplicative terms in GGE
   and AMMI models. \emph{Biometrics}, 70(3), 639-647.
-  http://doi.org/10.1111/biom.12162
+  https://doi.org/10.1111/biom.12162
 }
 
 \examples{
diff -Nru agridat-1.17/man/correa.soybean.uniformity.Rd agridat-1.18/man/correa.soybean.uniformity.Rd
--- agridat-1.17/man/correa.soybean.uniformity.Rd	2020-07-04 20:39:04.000000000 +0000
+++ agridat-1.18/man/correa.soybean.uniformity.Rd	2020-12-11 20:56:50.000000000 +0000
@@ -28,7 +28,7 @@
   Estudo do tamanho e forma de parcelas para experimentos de soja (Plot size and shape for soybean yield trials).
   Pesquisa Agropecuaria Brasileira, Serie Agronomia, 9,  49-59. Table 3,
   page 52-53.
-  http://seer.sct.embrapa.br/index.php/pab/article/view/17250
+  https://seer.sct.embrapa.br/index.php/pab/article/view/17250
 }
 \references{
   None
diff -Nru agridat-1.17/man/corsten.interaction.Rd agridat-1.18/man/corsten.interaction.Rd
--- agridat-1.17/man/corsten.interaction.Rd	2019-11-22 16:54:05.000000000 +0000
+++ agridat-1.18/man/corsten.interaction.Rd	2020-12-11 20:47:31.000000000 +0000
@@ -31,7 +31,7 @@
   L C A Corsten and J B Denis, (1990).
   Structuring Interaction in Two-Way Tables By Clustering.
   Biometrics, 46, 207--215. Table 1.
-  http://doi.org/10.2307/2531644
+  https://doi.org/10.2307/2531644
 }
 
 \examples{
diff -Nru agridat-1.17/man/cox.stripsplit.Rd agridat-1.18/man/cox.stripsplit.Rd
--- agridat-1.17/man/cox.stripsplit.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/cox.stripsplit.Rd	2020-12-11 20:56:51.000000000 +0000
@@ -32,7 +32,7 @@
 \references{
   SAS/STAT(R) 9.2 User's Guide, Second Edition.
   Example 23.5 Strip-Split Plot.
-  http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_anova_sect030.htm
+  https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_anova_sect030.htm
 }
 
 \examples{
diff -Nru agridat-1.17/man/crossa.wheat.Rd agridat-1.18/man/crossa.wheat.Rd
--- agridat-1.17/man/crossa.wheat.Rd	2020-07-05 17:02:20.000000000 +0000
+++ agridat-1.18/man/crossa.wheat.Rd	2020-12-15 22:05:12.000000000 +0000
@@ -64,51 +64,53 @@
   AMMI adjustment for statistical analysis of an
   international wheat yield trial.
   Theoretical and Applied Genetics, 81, 27--37.
-  http://doi.org/10.1007/BF00226108
+  https://doi.org/10.1007/BF00226108
 }
 \references{
   Jean-Louis Laffont, Kevin Wright and Mohamed Hanafi (2013).
   Genotype + Genotype x Block of Environments (GGB) Biplots.
   Crop Science, 53, 2332-2341.
-  http://doi.org/10.2135/cropsci2013.03.0178
+  https://doi.org/10.2135/cropsci2013.03.0178
 }
 
 \examples{
 \dontrun{
 
-library(agridat)
-data(crossa.wheat)
-dat <- crossa.wheat
-
-# AMMI biplot.  Fig 3 of Crossa et al.
-libs(agricolae)
-m1 <- with(dat, AMMI(E=loc, G=gen, R=1, Y=yield))
-b1 <- m1$biplot[,1:4]
-b1$PC1 <- -1 * b1$PC1 # Flip vertical
-plot(b1$yield, b1$PC1, cex=0.0,
-     text(b1$yield, b1$PC1, cex=.5, labels=row.names(b1),col="brown"),
-     main="crossa.wheat AMMI biplot",
-     xlab="Average yield", ylab="PC1", frame=TRUE)
-mn <- mean(b1$yield)
-abline(h=0, v=mn, col='wheat')
-
-g1 <- subset(b1,type=="GEN")
-text(g1$yield, g1$PC1, rownames(g1), col="darkgreen", cex=.5)
-
-e1 <- subset(b1,type=="ENV")
-arrows(mn, 0,
-       0.95*(e1$yield - mn) + mn, 0.95*e1$PC1,
-       col= "brown", lwd=1.8,length=0.1)
-
-# GGB example
-library(agridat)
-data(crossa.wheat)
-dat2 <- crossa.wheat
-libs(gge)
-# Specify env.group as column in data frame
-m2 <- gge(dat2, yield~gen*loc, env.group=locgroup, scale=FALSE)
-biplot(m2, main="crossa.wheat - GGB biplot")
-
+  library(agridat)
+  data(crossa.wheat)
+  dat <- crossa.wheat
+  
+  # AMMI biplot.  Fig 3 of Crossa et al.
+  libs(agricolae)
+  m1 <- with(dat, AMMI(E=loc, G=gen, R=1, Y=yield))
+  b1 <- m1$biplot[,1:4]
+  b1$PC1 <- -1 * b1$PC1 # Flip vertical
+  plot(b1$yield, b1$PC1, cex=0.0,
+       text(b1$yield, b1$PC1, cex=.5, labels=row.names(b1),col="brown"),
+       main="crossa.wheat AMMI biplot",
+       xlab="Average yield", ylab="PC1", frame=TRUE)
+  mn <- mean(b1$yield)
+  abline(h=0, v=mn, col='wheat')
+
+  g1 <- subset(b1,type=="GEN")
+  text(g1$yield, g1$PC1, rownames(g1), col="darkgreen", cex=.5)
+  
+  e1 <- subset(b1,type=="ENV")
+  arrows(mn, 0,
+         0.95*(e1$yield - mn) + mn, 0.95*e1$PC1,
+         col= "brown", lwd=1.8,length=0.1)
+  
+  # GGB example
+  library(agridat)
+  data(crossa.wheat)
+  dat2 <- crossa.wheat
+  libs(gge)
+  # Specify env.group as column in data frame
+  m2 <- gge(dat2, yield~gen*loc,
+            env.group=locgroup, gen.group=gengroup,
+            scale=FALSE)
+  biplot(m2, main="crossa.wheat - GGB biplot")
+  
 }
 }
 \keyword{datasets}
diff -Nru agridat-1.17/man/crowder.seeds.Rd agridat-1.18/man/crowder.seeds.Rd
--- agridat-1.17/man/crowder.seeds.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/crowder.seeds.Rd	2020-12-11 20:47:41.000000000 +0000
@@ -38,7 +38,7 @@
   Crowder, M.J., 1978.
   Beta-binomial anova for proportions.
   \emph{Appl. Statist.}, 27, 34-37.
-  http://doi.org/10.2307/2346223
+  https://doi.org/10.2307/2346223
 
 }
 
@@ -47,7 +47,7 @@
   N. E. Breslow and D. G. Clayton. 1993.
   Approximate inference in generalized linear mixed models.
   \emph{Journal of the American Statistical Association}, 88:9-25.
-  http://doi.org/10.2307/2290687
+  https://doi.org/10.2307/2290687
   
   Y. Lee and J. A. Nelder. 1996.
   Hierarchical generalized linear models with discussion.
@@ -58,39 +58,34 @@
 \examples{
 \dontrun{
 
-library(agridat)
-data(crowder.seeds)
-dat <- crowder.seeds
-m1.glm <- m1.glmm <- m1.bb <- m1.hglm <- NA
-
-
-# ----- Graphic
-libs(lattice)
-dotplot(germ/n~gen|extract, dat, main="crowder.seeds")
-
-
-# ----- GLM.
-# family=binomial() fixes dispersion at 1
-# family=quasibinomial() estimates dispersion, had larger std errors
-m1.glm <- glm(cbind(germ,n-germ) ~ gen*extract,
-              data=dat,
-              #family="binomial",
-              family=quasibinomial()
-              )
-summary(m1.glm)
-
-
-# --- GLMM.  Assumes Gaussian random effects
-libs(MASS)
-m1.glmm <- glmmPQL(cbind(germ, n-germ) ~ gen*extract, random= ~1|plate,
-  family=binomial(), data=dat)
-summary(m1.glmm)
-
-
-# ----- AODS3 package
-# libs(aods3)
-# m1.bb <- aodml(cbind(germ, n-germ) ~ gen * extract, data=dat, family="bb")
-
+  library(agridat)
+  data(crowder.seeds)
+  dat <- crowder.seeds
+  m1.glm <- m1.glmm <- m1.bb <- m1.hglm <- NA
+
+
+  # ----- Graphic
+  libs(lattice)
+  dotplot(germ/n~gen|extract, dat, main="crowder.seeds")
+
+ 
+  # ----- GLM.
+  # family=binomial() fixes dispersion at 1
+  # family=quasibinomial() estimates dispersion, had larger std errors
+  m1.glm <- glm(cbind(germ,n-germ) ~ gen*extract,
+                data=dat,
+                #family="binomial",
+                family=quasibinomial()
+                )
+  summary(m1.glm)
+  
+  
+  # --- GLMM.  Assumes Gaussian random effects
+  libs(MASS)
+  m1.glmm <- glmmPQL(cbind(germ, n-germ) ~ gen*extract, random= ~1|plate,
+                     family=binomial(), data=dat)
+  summary(m1.glmm)
+  
 
 # ----- HGML package. Beta-binomial with beta-distributed random effects
 # libs(hglm)
@@ -145,11 +140,26 @@
 ## extractcucumber         0.53 0.32      -0.12     0.53       1.17  0.53   0
 ## genO75:extractcucumber  0.82 0.42       0.01     0.82       1.66  0.81   0
 
+  
+  # ----- Stan using pre-built models from rstanarm
+  ## libs(tidyverse, rstan, rstanarm)
+  ## m1.stan <- stan_glm(
+  ##   cbind(germ,n-germ) ~ gen*extract,
+  ##   data=dat,
+  ##   family = binomial(link="logit")  )
+  ## round(posterior_interval(m1.stan, prob=.90),3)
+  ## #                            5%    95%
+  ## # (Intercept)            -0.715 -0.111
+  ## # genO75                 -0.512  0.228
+  ## # extractcucumber         0.123  0.977
+  ## # genO75:extractcucumber  0.248  1.284
+
+
 if(0) {
 
   # --- rjags version ---
 
-# JAGS/BUGS.  See http://mathstat.helsinki.fi/openbugs/Examples/Seeds.html
+# JAGS/BUGS.  See https://mathstat.helsinki.fi/openbugs/Examples/Seeds.html
 # Germination rate depends on p, which is a logit of a linear predictor
 # based on genotype and extract, plus random deviation to intercept
 
diff -Nru agridat-1.17/man/cullis.earlygen.Rd agridat-1.18/man/cullis.earlygen.Rd
--- agridat-1.17/man/cullis.earlygen.Rd	2020-07-04 20:39:44.000000000 +0000
+++ agridat-1.18/man/cullis.earlygen.Rd	2020-12-19 04:12:34.000000000 +0000
@@ -54,11 +54,11 @@
   A New Procedure for the Analysis of Early Generation Variety Trials.
   \emph{Journal of the Royal Statistical Society. Series C (Applied
     Statistics)}, 38, 361-375.
-  http://doi.org/10.2307/2348066
+  https://doi.org/10.2307/2348066
 }
 \references{
   Unreplicated early generation variety trial in Wheat.
-  http://www.vsni.co.uk/software/asreml/htmlhelp/asreml/xwheat.htm
+  https://www.vsni.co.uk/software/asreml/htmlhelp/asreml/xwheat.htm
 }
 
 \examples{
@@ -97,59 +97,7 @@
                    shapeCross=shape, layers=NULL)
   dat$mov.avg <- fitted(m0)
 
-  libs(asreml)
-  if( utils::packageVersion("asreml") < "4") {
-    # asreml3
-    
-    # Start with the standard AR1xAR1 analysis
-    dat <- transform(dat, xf=factor(col), yf=factor(row))
-    dat <- dat[order(dat$xf, dat$yf),]
-    m2 <- asreml(yield ~ weed, data=dat, random= ~gen,
-                 rcov = ~ ar1(xf):ar1(yf))
-    
-    # Variogram suggests a polynomial trend
-    m3 <- update(m2, fixed= yield~weed+pol(col,-1))
-    
-    # Now add a nugget variance
-    m4 <- update(m3, random= ~ gen + units)
-    
-    libs(lucid)
-    vc(m4)
-    ##           effect component std.error z.ratio constr
-    ##      gen!gen.var  73770    10420         7.1    pos
-    ##  units!units.var  30440     8074         3.8    pos
-    ##       R!variance  54720    10630         5.1    pos
-    ##         R!xf.cor      0.38     0.115     3.3  uncon
-    ##         R!yf.cor      0.84     0.045    19    uncon
-    
-    # Predictions from models m3 and m4 are non-estimable.  Why?
-    # Use model m2 for predictions
-    predict(m2)$pred
-    ##         gen predicted.value standard.error est.status
-    ## 1    Banks         2723.534       93.14633  Estimable
-    ## 2    Eno008        2981.057      162.85053  Estimable
-    ## 3    Eno009        2978.009      161.56930  Estimable
-    ## 4    Eno010        2821.399      153.96697  Estimable
-    ## 5    Eno011        2991.610      161.53308  Estimable
-    ## 6    Eno012        2771.148      162.21897  Estimable
-    
-    dat$ar1 <- fitted(m2)
-    head(dat[ , c('yield','ar1','mov.avg')])
-    ##    yield      ar1       mg
-    ## 1   2652 2467.980 2531.998
-    ## 11  3394 3071.681 3052.160
-    ## 21  3148 2826.188 2807.031
-    ## 31  3426 3026.985 3183.649
-    ## 41  3555 3070.102 3195.910
-    ## 51  3453 3006.352 3510.511
-    pairs(dat[ , c('yield','ar1','mov.avg')])
-    
-  }
-
-
-  libs(asreml)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
+  libs(asreml) # asreml4
 
   # Start with the standard AR1xAR1 analysis
   dat <- transform(dat, xf=factor(col), yf=factor(row))
@@ -195,8 +143,6 @@
   ## ## 51  3453 3006.352 3510.511
   ## pairs(dat[ , c('yield','ar1','mg')])
 
-  }
-
 }
 }
 \keyword{datasets}
diff -Nru agridat-1.17/man/damesa.maize.Rd agridat-1.18/man/damesa.maize.Rd
--- agridat-1.17/man/damesa.maize.Rd	1970-01-01 00:00:00.000000000 +0000
+++ agridat-1.18/man/damesa.maize.Rd	2020-12-11 21:08:23.000000000 +0000
@@ -0,0 +1,77 @@
+\name{damesa.maize}
+\alias{damesa.maize}
+\docType{data}
+\title{
+  Incomplete-block experiment of maize in Ethiopia.
+}
+\description{
+  Incomplete-block experiment of maize in Ethiopia.
+}
+\usage{data("damesa.maize")}
+\format{
+  A data frame with 264 observations on the following 8 variables.
+  \describe{
+    \item{\code{site}}{site, 4 levels}
+    \item{\code{rep}}{replicate, 3 levels}
+    \item{\code{block}}{incomplete block}
+    \item{\code{plot}}{plot number}
+    \item{\code{gen}}{genotype, 22 levels}
+    \item{\code{row}}{row ordinate}
+    \item{\code{col}}{column ordinate}
+    \item{\code{yield}}{yield, t/ha}
+  }
+}
+\details{
+  An experiment harvested in 2012, evaluating drought-tolerant maize
+  hybrids at 4 sites in Ethiopia.
+  At each, an incomplete-block design was used.
+
+  Damesa et al use this data to compare single-stage and two-stage
+  analyses.
+  
+}
+\source{
+  Tigist Mideksa Damesa, Jens Möhring, Mosisa Worku, Hans-Peter Piepho (2017).
+  One Step at a Time: Stage-Wise Analysis of a Series of Experiments.
+  Agronomy J, 109, 845-857.
+  https://doi.org/10.2134/agronj2016.07.0395
+}
+\references{
+  https://schmidtpaul.github.io/MMFAIR/weighted_two_stage.html
+}
+\examples{
+\dontrun{
+  library(agridat)
+  libs(desplot)
+  desplot(damesa.maize,
+          yield ~ col*row|site,
+          main="damesa.maize",
+          out1=rep, out2=block, num=gen, cex=1)
+
+  if(0){
+    # Fit the single-stage model in Damesa
+    lib(asreml)
+    m0 <- asreml(data=damesa.maize,
+             fixed = yield ~ gen,
+             random = ~ site + gen:site + at(site):rep/block,
+             residual = ~ dsum( ~ units|site) )
+    lucid::vc(m0) # match Damesa table 1 column 3
+    ##                 effect component std.error z.ratio bound %ch
+    ##       at(site, S1):rep   0.08819   0.1814     0.49     P 0  
+    ##       at(site, S2):rep   1.383     1.426      0.97     P 0  
+    ##       at(site, S3):rep   0              NA      NA     B 0  
+    ##       at(site, S4):rep   0.01442   0.02602    0.55     P 0  
+    ##                   site  10.45      8.604      1.2      P 0.1
+    ##               gen:site   0.1054    0.05905    1.8      P 0.1
+    ## at(site, S1):rep:block   0.3312    0.3341     0.99     P 0  
+    ## at(site, S2):rep:block   0.4747    0.1633     2.9      P 0  
+    ## at(site, S3):rep:block   0              NA      NA     B 0  
+    ## at(site, S4):rep:block   0.06954   0.04264    1.6      P 0  
+    ##              site_S1!R   1.346     0.3768     3.6      P 0  
+    ##              site_S2!R   0.1936    0.06628    2.9      P 0  
+    ##              site_S3!R   1.153     0.2349     4.9      P 0  
+    ##              site_S4!R   0.1112    0.03665    3        P 0  
+  }
+}
+} 
+\keyword{datasets}
diff -Nru agridat-1.17/man/darwin.maize.Rd agridat-1.18/man/darwin.maize.Rd
--- agridat-1.17/man/darwin.maize.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/darwin.maize.Rd	2020-12-11 20:56:52.000000000 +0000
@@ -54,7 +54,7 @@
 \source{
   Darwin, C. R. 1876. \emph{The effects of cross and self fertilisation in the
   vegetable kingdom}. London: John Murray. Page 16.
-  http://darwin-online.org.uk/converted/published/1881_Worms_F1357/1876_CrossandSelfFertilisation_F1249/1876_CrossandSelfFertilisation_F1249.html
+  https://darwin-online.org.uk/converted/published/1881_Worms_F1357/1876_CrossandSelfFertilisation_F1249/1876_CrossandSelfFertilisation_F1249.html
 }
 \references{
   R. A. Fisher, (1935) \emph{The Design of Experiments},  Oliver and Boyd. Page 30.
diff -Nru agridat-1.17/man/dasilva.maize.Rd agridat-1.18/man/dasilva.maize.Rd
--- agridat-1.17/man/dasilva.maize.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/dasilva.maize.Rd	2020-12-11 20:48:18.000000000 +0000
@@ -43,7 +43,7 @@
   Carlos Pereira da Silva, Luciano Antonio de Oliveira, Joel Jorge
   Nuvunga, Andrezza Kellen Alves Pamplona, Marcio Balestre.
   Plos One. Supplemental material.
-  http://doi.org/10.1371/journal.pone.0131414
+  https://doi.org/10.1371/journal.pone.0131414
 
   Used via Creative Commons Attribution License.
 }
diff -Nru agridat-1.17/man/day.wheat.uniformity.Rd agridat-1.18/man/day.wheat.uniformity.Rd
--- agridat-1.17/man/day.wheat.uniformity.Rd	2020-07-04 20:40:26.000000000 +0000
+++ agridat-1.18/man/day.wheat.uniformity.Rd	2020-12-11 20:48:20.000000000 +0000
@@ -46,7 +46,7 @@
   The relation of size, shape, and number of replications of plats to
   probable error in field experimentation.
   \emph{Agronomy Journal}, 12, 100-105.
-  http://doi.org/10.2134/agronj1920.00021962001200030002x
+  https://doi.org/10.2134/agronj1920.00021962001200030002x
 }
 \examples{
 \dontrun{
diff -Nru agridat-1.17/man/denis.missing.Rd agridat-1.18/man/denis.missing.Rd
--- agridat-1.17/man/denis.missing.Rd	2019-12-02 03:47:59.000000000 +0000
+++ agridat-1.18/man/denis.missing.Rd	2020-12-11 20:56:53.000000000 +0000
@@ -28,7 +28,7 @@
 \references{
   H P Piepho, (1999) Stability analysis using the SAS system,
   Agron Journal, 91, 154--160.
-  http://doi.og/10.2134/agronj1999.00021962009100010024x
+  https://doi.og/10.2134/agronj1999.00021962009100010024x
 }
 
 \examples{
diff -Nru agridat-1.17/man/denis.ryegrass.Rd agridat-1.18/man/denis.ryegrass.Rd
--- agridat-1.17/man/denis.ryegrass.Rd	2020-07-05 17:02:37.000000000 +0000
+++ agridat-1.18/man/denis.ryegrass.Rd	2020-12-11 20:48:21.000000000 +0000
@@ -32,7 +32,7 @@
   Asymptotic confidence regions for biadditive models:
   interpreting genotype-environment interaction,
   \emph{Applied Statistics}, 45, 479-493.
-  http://doi.org/10.2307/2986069
+  https://doi.org/10.2307/2986069
 }
 \references{
   Gower, J.C. and Hand, D.J., 1996.  Biplots.
diff -Nru agridat-1.17/man/devries.pine.Rd agridat-1.18/man/devries.pine.Rd
--- agridat-1.17/man/devries.pine.Rd	2020-07-04 20:40:59.000000000 +0000
+++ agridat-1.18/man/devries.pine.Rd	2020-12-11 20:56:53.000000000 +0000
@@ -41,7 +41,7 @@
   P.G. De Vries, J.W. Hildebrand, N.R. De Graaf. (1978).
   Analysis of 11 years growth of carribbean pine in a replicated
   Graeco-Latin square spacing-thinning experiment in Surinam. Page 46, 51.
-  http://edepot.wur.nl/287590
+  https://edepot.wur.nl/287590
 }
 \references{
   None
diff -Nru agridat-1.17/man/digby.jointregression.Rd agridat-1.18/man/digby.jointregression.Rd
--- agridat-1.17/man/digby.jointregression.Rd	2020-04-27 21:01:27.000000000 +0000
+++ agridat-1.18/man/digby.jointregression.Rd	2020-12-11 20:48:25.000000000 +0000
@@ -25,16 +25,16 @@
   Digby, P.G.N. (1979). Modified joint regression analysis for
   incomplete variety x environment data.
   \emph{Journal of Agricultural Science}, 93, 81-86.
-  http://doi.org/10.1017/S0021859600086159
+  https://doi.org/10.1017/S0021859600086159
 }
 \references{
   Hans-Pieter Piepho, 1997.
   Analyzing Genotype-Environment Data by Mixed-Models with
   Multiplicative Terms. \emph{Biometrics}, 53, 761-766.
-  http://doi.org/10.2307/2533976
+  https://doi.org/10.2307/2533976
 
   RJOINT procedure in GenStat.
-  http://www.vsni.co.uk/software/genstat/htmlhelp/server/RJOINT.htm
+  https://www.vsni.co.uk/software/genstat/htmlhelp/server/RJOINT.htm
 }
 
 \examples{
diff -Nru agridat-1.17/man/diggle.cow.Rd agridat-1.18/man/diggle.cow.Rd
--- agridat-1.17/man/diggle.cow.Rd	2019-12-02 04:05:57.000000000 +0000
+++ agridat-1.18/man/diggle.cow.Rd	2020-12-19 04:13:22.000000000 +0000
@@ -39,7 +39,7 @@
   \emph{Analysis of Longitudinal Data}. Page 100-101.
 
   Retrieved Oct 2011 from
-  http://www.maths.lancs.ac.uk/~diggle/lda/Datasets/
+  https://www.maths.lancs.ac.uk/~diggle/lda/Datasets/
 }
 
 \references{
@@ -55,7 +55,7 @@
   48, 269--311.
 
   SAS/STAT(R) 9.2 User's Guide, Second Edition.
-  http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_glimmix_sect018.htm
+  https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_glimmix_sect018.htm
 
 }
 
@@ -76,30 +76,7 @@
   dat <- transform(dat, time = (day-122)/10)
 
 
-  libs(asreml)
-  if( utils::packageVersion("asreml") < "4") {
-    # asreml3
-    
-  
-    # Smooth for each animal.  No treatment effects. Similar to SAS Output 38.6.9
-
-    m1 <- asreml(weight ~ 1 + lin(time) + animal + animal:lin(time),
-                 data=dat,
-                 random = ~ animal:spl(time))
-    p1 <- predict(m1, data=dat, classify="animal:time",
-                  predictpoints=list(time=seq(0,65.9, length=50)))
-    p1 <- p1$pred$pval
-    p1 <- merge(dat, p1, all=TRUE) # to get iron/infect merged in
-    foo1 <- xyplot(weight ~ day|iron*infect, dat, group=animal)
-    foo2 <- xyplot(predicted.value ~ day|iron*infect, p1,
-                   type='l', group=animal)
-    print(foo1+foo2)
-
-  }
-
-  libs(asreml)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
+  libs(asreml) # asreml4
   ## libs(latticeExtra)
   
   ## # Smooth for each animal.  No treatment effects. Similar to SAS Output 38.6.9
@@ -113,7 +90,6 @@
   foo1 <- xyplot(weight ~ day|iron*infect, dat, group=animal)
   foo2 <- xyplot(predicted.value ~ day|iron*infect, p1, type='l', group=animal)
   print(foo1+foo2)
-  }
 
 }
 }
diff -Nru agridat-1.17/man/draper.safflower.uniformity.Rd agridat-1.18/man/draper.safflower.uniformity.Rd
--- agridat-1.17/man/draper.safflower.uniformity.Rd	2020-07-04 20:41:36.000000000 +0000
+++ agridat-1.18/man/draper.safflower.uniformity.Rd	2020-12-11 20:56:55.000000000 +0000
@@ -59,7 +59,7 @@
   Arlen D. Draper. (1959).
   Optimum plot size and shape for safflower yield tests.
   Dissertation. University of Arizona. 
-  http://hdl.handle.net/10150/319371
+  https://hdl.handle.net/10150/319371
 }
 \references{
   None
diff -Nru agridat-1.17/man/durban.competition.Rd agridat-1.18/man/durban.competition.Rd
--- agridat-1.17/man/durban.competition.Rd	2020-07-04 20:41:53.000000000 +0000
+++ agridat-1.18/man/durban.competition.Rd	2020-12-11 20:56:56.000000000 +0000
@@ -34,7 +34,7 @@
   
   Field length: 3 blocks * 12m + 2*0.5m spacing = 37m
   Retrieved from
-  http://www.ma.hw.ac.uk/~iain/research/JAgSciData/data/Trial1.dat
+  https://www.ma.hw.ac.uk/~iain/research/JAgSciData/data/Trial1.dat
 
   Used with permission of Iain Currie.
 }
diff -Nru agridat-1.17/man/durban.rowcol.Rd agridat-1.18/man/durban.rowcol.Rd
--- agridat-1.17/man/durban.rowcol.Rd	2020-07-21 16:35:41.000000000 +0000
+++ agridat-1.18/man/durban.rowcol.Rd	2020-12-19 04:13:55.000000000 +0000
@@ -37,9 +37,14 @@
   Newton, Adrian and Thomas, William and Currie, Iain. 2003.
   The practical use of semiparametric models in field trials,
   Journal of Agric Biological and Envir Stats, 8, 48-66.
-  http://doi.org/10.1198/1085711031265
+  https://doi.org/10.1198/1085711031265
+}
+\references{
+  Edmondson, Rodney (2020).
+  Multi-level Block Designs for Comparative Experiments.
+  J of Agric, Biol, and Env Stats.
+  https://doi.org/10.1007/s13253-020-00416-0
 }
-
 \examples{
 \dontrun{
 
@@ -80,9 +85,7 @@
   wireframe(p1lo~row+bed, new1, aspect=c(1,.5), main="Field trend")
 
 
-  libs(asreml)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
+  libs(asreml) # asreml4
 
   dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
   dat <- dat[order(dat$rowf, dat$bedf),]
@@ -107,7 +110,6 @@
   
   v7c <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1lo$residuals)
   wireframe(gamma ~ x*y, v7c, aspect=c(1,.5)) # Fig 7c
-  }
 
 }
 }
diff -Nru agridat-1.17/man/durban.splitplot.Rd agridat-1.18/man/durban.splitplot.Rd
--- agridat-1.17/man/durban.splitplot.Rd	2020-07-21 16:35:52.000000000 +0000
+++ agridat-1.18/man/durban.splitplot.Rd	2020-12-19 04:14:16.000000000 +0000
@@ -34,7 +34,7 @@
   Newton, Adrian and Thomas, William and Currie, Iain. 2003.
   The practical use of semiparametric models in field trials,
   Journal of Agric Biological and Envir Stats, 8, 48-66.
-  http://doi.org/10.1198/1085711031265.
+  https://doi.org/10.1198/1085711031265.
 }
 
 \examples{
@@ -76,30 +76,27 @@
   wireframe(p2lo~row+bed, new2, aspect=c(1,.5),
             main="durban.splitplot - Field trend")
 
-  libs(asreml)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
+  libs(asreml) # asreml4
     
-    # Table 5, variance components.  Table 6, F tests
-    dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
-    dat <- dat[order(dat$rowf, dat$bedf),]
-    m2a2 <- asreml(yield ~ gen*fung, random=~block/fung+units, data=dat,
-                   resid =~ar1v(rowf):ar1(bedf))
-    m2a2 <- update(m2a2)
-    
-    libs(lucid)
-    vc(m2a2)
-    ##             effect component std.error z.ratio bound %ch
-    ##              block   0              NA      NA     B  NA
-    ##         block:fung   0.01206  0.01512      0.8     P   0
-    ##              units   0.02463  0.002465    10       P   0
-    ##       rowf:bedf(R)   1              NA      NA     F   0
-    ## rowf:bedf!rowf!cor   0.8836   0.03646     24       U   0
-    ## rowf:bedf!rowf!var   0.1261   0.04434      2.8     P   0
-    ## rowf:bedf!bedf!cor   0.9202   0.02846     32       U   0
-    
-    wald(m2a2)
-  }
+  # Table 5, variance components.  Table 6, F tests
+  dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
+  dat <- dat[order(dat$rowf, dat$bedf),]
+  m2a2 <- asreml(yield ~ gen*fung, random=~block/fung+units, data=dat,
+                 resid =~ar1v(rowf):ar1(bedf))
+  m2a2 <- update(m2a2)
+  
+  libs(lucid)
+  vc(m2a2)
+  ##             effect component std.error z.ratio bound %ch
+  ##              block   0              NA      NA     B  NA
+  ##         block:fung   0.01206  0.01512      0.8     P   0
+  ##              units   0.02463  0.002465    10       P   0
+  ##       rowf:bedf(R)   1              NA      NA     F   0
+  ## rowf:bedf!rowf!cor   0.8836   0.03646     24       U   0
+  ## rowf:bedf!rowf!var   0.1261   0.04434      2.8     P   0
+  ## rowf:bedf!bedf!cor   0.9202   0.02846     32       U   0
+  
+  wald(m2a2)
   
 }
 }
diff -Nru agridat-1.17/man/eden.nonnormal.Rd agridat-1.18/man/eden.nonnormal.Rd
--- agridat-1.17/man/eden.nonnormal.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/eden.nonnormal.Rd	2020-12-11 20:48:30.000000000 +0000
@@ -21,7 +21,7 @@
   This data was used in a very early example of a permutation test.
   
   Eden & Yates used this data to consider the impact of non-normal data
-  on the validitiy of a hypothesis test that assumes normality.  They
+  on the validity of a hypothesis test that assumes normality.  They
   concluded that the skew data did not negatively affect the analysis of
   variance.
 
@@ -121,7 +121,7 @@
   fobs <- rep(NA, 1000)
   for(i in 1:1000){
     # randomize treatments within each block
-    # trick from http://stackoverflow.com/questions/25085537
+    # trick from https://stackoverflow.com/questions/25085537
     dat2$trt <- with(dat2, ave(trt, block, FUN = sample))
     fobs[i] <- anova(aov(height ~ block + trt, dat2))["trt","F value"]
   }
diff -Nru agridat-1.17/man/eden.potato.Rd agridat-1.18/man/eden.potato.Rd
--- agridat-1.17/man/eden.potato.Rd	2020-07-04 20:44:13.000000000 +0000
+++ agridat-1.18/man/eden.potato.Rd	2020-12-11 20:48:31.000000000 +0000
@@ -50,7 +50,7 @@
   Evidence for conformal invariance of crop yields,
   \emph{Proceedings of the Royal Society A: Mathematical, Physical and
     Engineering Science}, 462, 2119--2143.
-  http://doi.org/10.1098/rspa.2006.1667 
+  https://doi.org/10.1098/rspa.2006.1667 
 }
 
 \examples{
diff -Nru agridat-1.17/man/edwards.oats.Rd agridat-1.18/man/edwards.oats.Rd
--- agridat-1.17/man/edwards.oats.Rd	2019-10-29 19:39:02.000000000 +0000
+++ agridat-1.18/man/edwards.oats.Rd	2020-12-11 20:48:33.000000000 +0000
@@ -50,7 +50,7 @@
   Jode W. Edwards, Jean-Luc Jannink (2006).
   Bayesian Modeling of Heterogeneous Error and Genotype x Environment Interaction Variances.
   Crop Science, 46, 820-833.
-  http://dx.doi.org/10.2135/cropsci2005.0164
+  https://dx.doi.org/10.2135/cropsci2005.0164
 }
 \references{
   None
diff -Nru agridat-1.17/man/federer.diagcheck.Rd agridat-1.18/man/federer.diagcheck.Rd
--- agridat-1.17/man/federer.diagcheck.Rd	2020-07-04 20:44:55.000000000 +0000
+++ agridat-1.18/man/federer.diagcheck.Rd	2020-12-19 04:14:50.000000000 +0000
@@ -42,7 +42,7 @@
   Federer, Walter T. 1998. Recovery of interblock, intergradient, and
   intervariety information in incomplete block and lattice rectangle
   design experiments. \emph{Biometrics}, 54, 471--481.
-  http://doi.org/10.2307/3109756
+  https://doi.org/10.2307/3109756
 }
 \references{
   Walter T Federer and Russell D Wolfinger, 2003.
@@ -161,54 +161,12 @@
   ##  one.13   r1           10360   101.8
   ##  Residual               4127    64.24
   
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") < "4") {
-    # asreml3
-
-    m3 <- asreml(yield ~ -1 + trtn, data=dat,
-                 random = ~ r1 + r2 + r4 + r8 + r10 +
-                   c1 + c2 + c3 + c4 + c6 + c8 +
-                   r1:c1 + r1:c2 + r1:c3 + new:gen)
-  ## coef(m3)
-  # REML cultivar means.  Very similar to Federer table 2.
-  ## rev(sort(round(coef(m3)$fixed[3] + coef(m3)$random[137:256,],0)))
-  ## gen_G060 gen_G021 gen_G011 gen_G099 gen_G002
-  ##      974      949      945      944      942
-  ## gen_G118 gen_G058 gen_G035 gen_G111 gen_G120
-  ##      938      937      937      933      932
-  ## gen_G046 gen_G061 gen_G082 gen_G038 gen_G090
-  ##      932      931      927      927      926
-
-  vc(m3)
-  ##           effect component std.error z.ratio constr
-  ##        r1!r1.var   9201        13720    0.67    pos
-  ##        r2!r2.var    241.7       1059    0.23    pos
-  ##        r4!r4.var   2269         3915    0.58    pos
-  ##        r8!r8.var   1355         2627    0.52    pos
-  ##      r10!r10.var   1133         2312    0.49    pos
-  ##        c1!c1.var      0.01         0    4.8   bound
-  ##        c2!c2.var   5942         8969    0.66    pos
-  ##        c3!c3.var   2549         4177    0.61    pos
-  ##        c4!c4.var   1792         3106    0.58    pos
-  ##        c6!c6.var   1400         2551    0.55    pos
-  ##        c8!c8.var   6456         9702    0.67    pos
-  ##     r1:c1!r1.var 128000       189700    0.67    pos
-  ##     r1:c2!r1.var  58230        90820    0.64    pos
-  ##     r1:c3!r1.var   5531        16550    0.33    pos
-  ##  new:gen!new.var   2869         1367    2.1     pos
-  ##       R!variance   4412          915    4.8     pos
-
-  }
-
-
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
+  libs(asreml,lucid) # asreml4
     
-    m3 <- asreml(yield ~ -1 + trtn, data=dat,
-                 random = ~ r1 + r2 + r4 + r8 + r10 +
-                   c1 + c2 + c3 + c4 + c6 + c8 +
-                   r1:c1 + r1:c2 + r1:c3 + new:gen)
+  m3 <- asreml(yield ~ -1 + trtn, data=dat,
+               random = ~ r1 + r2 + r4 + r8 + r10 +
+                 c1 + c2 + c3 + c4 + c6 + c8 +
+                 r1:c1 + r1:c2 + r1:c3 + new:gen)
   ## coef(m3)
   ## # REML cultivar means.  Very similar to Federer table 2.
   ## rev(sort(round(coef(m3)$fixed[3] + coef(m3)$random[137:256,],0)))
@@ -237,7 +195,6 @@
   ## ##     r1:c3!r1.var   5531        16550    0.33    pos
   ## ##  new:gen!new.var   2869         1367    2.1     pos
   ## ##       R!variance   4412          915    4.8     pos
-}
 
 }
 }
diff -Nru agridat-1.17/man/federer.tobacco.Rd agridat-1.18/man/federer.tobacco.Rd
--- agridat-1.17/man/federer.tobacco.Rd	2020-07-04 20:45:08.000000000 +0000
+++ agridat-1.18/man/federer.tobacco.Rd	2020-12-11 20:48:37.000000000 +0000
@@ -32,7 +32,7 @@
   Walter T Federer and C S Schlottfeldt, 1954.
   The use of covariance to control gradients in experiments.
   \emph{Biometrics}, 10, 282--290.
-  http://doi.org/10.2307/3001881
+  https://doi.org/10.2307/3001881
 }
 \references{
   R. D. Cook and S. Weisberg (1999).
diff -Nru agridat-1.17/man/fisher.barley.Rd agridat-1.18/man/fisher.barley.Rd
--- agridat-1.17/man/fisher.barley.Rd	2019-12-05 02:59:57.000000000 +0000
+++ agridat-1.18/man/fisher.barley.Rd	2020-12-19 04:19:52.000000000 +0000
@@ -40,13 +40,13 @@
   F. Yates & W. G. Cochran (1938).
   The Analysis of Groups of Experiments.
   \emph{Journal of Agricultural Science}, 28, 556-580, table 1.
-  http://doi.org/10.1017/S0021859600050978
+  https://doi.org/10.1017/S0021859600050978
 
   G. K. Shukla, 1972.
   Some statistical aspects of partitioning of genotype-environmental
   components of variability.
   \emph{Heredity}, 29, 237-245. Table 1.
-  http://doi.org/10.1038/hdy.1972.87
+  https://doi.org/10.1038/hdy.1972.87
   
 }
 \examples{
@@ -100,44 +100,40 @@
   }
 
 
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") < "4") {
-    # asreml3
-    # mixed model approach gives similar results (but not identical)
-
-    dat2 <- dat
-    dat2 <- dplyr::group_by(dat2, gen,env)
-    dat2 <- dplyr::summarize(dat2, yield=sum(yield)) # means across years
-    dat2 <- dat2[order(dat2$gen),]
+  libs(asreml,lucid) # asreml3
+  # mixed model approach gives similar results (but not identical)
 
+  dat2 <- dat
+  dat2 <- dplyr::group_by(dat2, gen,env)
+  dat2 <- dplyr::summarize(dat2, yield=sum(yield)) # means across years
+  dat2 <- dat2[order(dat2$gen),]
+  
   # G-side
   m1g <- asreml(yield ~ gen, data=dat2,
-                  random = ~ env + at(gen):units,
-                  family=asreml.gaussian(dispersion=1.0))
+                random = ~ env + at(gen):units,
+                family=asr_gaussian(dispersion=1.0))
   m1g <- update(m1g)
   summary(m1g)$varcomp[-1,1:2]/6
-  ##                                          gamma   component
-  ## at(gen, Manchuria):units!units.var  33.8318944  33.8318944
-  ## at(gen, Peatland):units!units.var   70.4838297  70.4838297
-  ## at(gen, Svansota):units!units.var   25.2558315  25.2558315
-  ## at(gen, Trebi):units!units.var     231.6923935 231.6923935
-  ## at(gen, Velvet):units!units.var     13.9189381  13.9189381
-  ## R!variance                           0.1666667   0.1666667
+  #                            component std.error
+  # at(gen, Manchuria):units  33.8145031  27.22721
+  # at(gen, Peatland):units   70.4489092  50.52680
+  # at(gen, Svansota):units   25.2728568  21.92919
+  # at(gen, Trebi):units     231.6981702 150.80464
+  # at(gen, Velvet):units     13.9325646  16.58571
+  # units!R                    0.1666667        NA
 
   # R-side estimates = G-side estimate + 0.1666 (resid variance)
   m1r <- asreml(yield ~ gen, data=dat2,
-                 random = ~ env,
-                 rcov = ~ at(gen):units) # or diag(gen):units
+                random = ~ env,
+                residual = ~ dsum( ~ units|gen))
   m1r <- update(m1r)
   summary(m1r)$varcomp[-1,1:2]/6
-  ##                            gamma component
-  ## gen_Manchuria!variance  34.03643  34.03643
-  ## gen_Peatland!variance   70.72723  70.72723
-  ## gen_Svansota!variance   25.38494  25.38494
-  ## gen_Trebi!variance     231.84662 231.84662
-  ## gen_Velvet!variance     14.05591  14.05591
-  
-}
+  #                     component std.error
+  # gen_Manchuria!R  34.00058  27.24871
+  # gen_Peatland!R   70.65501  50.58925
+  # gen_Svansota!R   25.42022  21.88606
+  # gen_Trebi!R     231.85846 150.78756
+  # gen_Velvet!R     14.08405  16.55558
 
 }
 }
diff -Nru agridat-1.17/man/foulley.calving.Rd agridat-1.18/man/foulley.calving.Rd
--- agridat-1.17/man/foulley.calving.Rd	2019-11-22 16:57:39.000000000 +0000
+++ agridat-1.18/man/foulley.calving.Rd	2020-12-11 20:48:45.000000000 +0000
@@ -39,7 +39,7 @@
   Statistical Analysis of Ordered Categorical Data via a Structured
   Heteroskedastic Threshold Model.
   Genet Sel Evol, 28, 249--273.
-  http://doi.org/10.1051/gse:19960304
+  https://doi.org/10.1051/gse:19960304
 }
 
 \examples{
diff -Nru agridat-1.17/man/garber.multi.uniformity.Rd agridat-1.18/man/garber.multi.uniformity.Rd
--- agridat-1.17/man/garber.multi.uniformity.Rd	2020-07-04 20:45:37.000000000 +0000
+++ agridat-1.18/man/garber.multi.uniformity.Rd	2020-12-11 20:56:56.000000000 +0000
@@ -56,7 +56,7 @@
   Garber, RJ and Mcllvaine, TC and Hoover, MM. 1926.
   A study of soil heterogeneity in experiment plots.
   Jour Agr Res, 33, 255-268. Tables 3, 5.
-  http://naldc.nal.usda.gov/download/IND43967148/PDF
+  https://naldc.nal.usda.gov/download/IND43967148/PDF
 }
 
 \examples{
diff -Nru agridat-1.17/man/gartner.corn.Rd agridat-1.18/man/gartner.corn.Rd
--- agridat-1.17/man/gartner.corn.Rd	2020-07-27 18:29:36.000000000 +0000
+++ agridat-1.18/man/gartner.corn.Rd	2020-12-11 20:48:49.000000000 +0000
@@ -48,9 +48,9 @@
 \source{
 
   Originally from University of Minnesota Precision Agriculture Center.
-  http://www.soils.umn.edu/academics/classes/soil4111/hw/
+  https://www.soils.umn.edu/academics/classes/soil4111/hw/
 
-  Retrieved 27 Aug 2015 from https://web.archive.org/web/20100717003256/http://www.soils.umn.edu/academics/classes/soil4111/files/yield_a.xls
+  Retrieved 27 Aug 2015 from https://web.archive.org/web/20100717003256/https://www.soils.umn.edu/academics/classes/soil4111/files/yield_a.xls
 
   Used under Creative Commons BY-SA 3.0 license.
 }
@@ -86,7 +86,7 @@
 dat <- transform(dat, yldbin = as.numeric(cut(yield, breaks= yldbrks)))
 
 # Add polygons for soil map units
-# Go to: http://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx
+# Go to: https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx
 # Click: Lat and Long. 43.924, -93.975
 # Click the little AOI rectangle icon.  Drag around the field
 # In the AOI Properties, enter the Name: Gartner
diff -Nru agridat-1.17/man/gathmann.bt.Rd agridat-1.18/man/gathmann.bt.Rd
--- agridat-1.17/man/gathmann.bt.Rd	2019-11-22 16:57:57.000000000 +0000
+++ agridat-1.18/man/gathmann.bt.Rd	2020-12-11 20:56:57.000000000 +0000
@@ -31,7 +31,7 @@
 \source{
   L. A. Hothorn, 2005.
   Evaluation of Bt-Maize Field Trials by a Proof of Safety.
-  http://www.seedtest.org/upload/cms/user/presentation7Hothorn.pdf
+  https://www.seedtest.org/upload/cms/user/presentation7Hothorn.pdf
 }
 
 \examples{
diff -Nru agridat-1.17/man/gauch.soy.Rd agridat-1.18/man/gauch.soy.Rd
--- agridat-1.17/man/gauch.soy.Rd	2020-07-29 20:25:52.000000000 +0000
+++ agridat-1.18/man/gauch.soy.Rd	2020-12-11 20:48:51.000000000 +0000
@@ -42,7 +42,7 @@
   means (personal communication).
 
   Retrieved Sep 2011 from
-  http://www.microcomputerpower.com/matmodel/matmodelmatmodel_sample_.html
+  https://www.microcomputerpower.com/matmodel/matmodelmatmodel_sample_.html
 
   Used with permission of Hugh Gauch.
 }
diff -Nru agridat-1.17/man/george.wheat.Rd agridat-1.18/man/george.wheat.Rd
--- agridat-1.17/man/george.wheat.Rd	2020-07-06 20:36:43.000000000 +0000
+++ agridat-1.18/man/george.wheat.Rd	2020-12-11 20:48:52.000000000 +0000
@@ -42,7 +42,7 @@
   Nicholas George and Mark Lundy (2019).
   Quantifying Genotype x Environment Effects in Long-Term Common Wheat Yield Trials from an Agroecologically Diverse Production Region.
   Crop Science, 59, 1960-1972.
-  http://doi.org/10.2135/cropsci2019.01.0010
+  https://doi.org/10.2135/cropsci2019.01.0010
 }
 \references{
   None
diff -Nru agridat-1.17/man/giles.wheat.Rd agridat-1.18/man/giles.wheat.Rd
--- agridat-1.17/man/giles.wheat.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/giles.wheat.Rd	2020-12-11 20:48:54.000000000 +0000
@@ -46,7 +46,7 @@
   Piepho, HP (2003).
   Model-based mean adjustment in quantitative germplasm evaluation data.
   \emph{Genetic Resources and Crop Evolution}, 50, 281-290.
-  http://doi.org/10.1023/A:1023503900759
+  https://doi.org/10.1023/A:1023503900759
   
 }
 \examples{
diff -Nru agridat-1.17/man/gilmour.serpentine.Rd agridat-1.18/man/gilmour.serpentine.Rd
--- agridat-1.17/man/gilmour.serpentine.Rd	2020-07-04 20:45:54.000000000 +0000
+++ agridat-1.18/man/gilmour.serpentine.Rd	2020-12-19 14:07:11.000000000 +0000
@@ -68,105 +68,53 @@
 
   # ----------------------------------------------------------------------------
 
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") < "4") {
-    # asreml3
-
-    dat <- transform(dat, rowf=factor(row), colf=factor(10*(col-8)))
-    dat <- dat[order(dat$rowf, dat$colf), ] # Sort order needed by asreml
-  
-    # RCB
-    m0 <- asreml(yield ~ gen, data=dat, random=~rep)
-  
-    # Add AR1 x AR1
-    m1 <- asreml(yield ~ gen, data=dat, rcov = ~ar1(rowf):ar1(colf))
   
-    # Add spline
-    m2 <- asreml(yield ~ gen + col, data=dat,
-                 random= ~ spl(col) + colf,
-                 rcov = ~ar1(rowf):ar1(colf))
-  
-    # Figure 4 shows serpentine spraying
-    p2 <- predict(m2, data=dat, classify="colf")$predictions$pvals
-    plot(p2$predicted, type='b', xlab="column number", ylab="BLUP")
-  
-    # Define column code (due to serpentine spraying)
-    # Rhelp doesn't like double-percent modulus symbol, so compute by hand
-    dat <- transform(dat, colcode = factor(dat$col-floor((dat$col-1)/4)*4 -1))
+  libs(asreml,lucid) # asreml4
     
-    m3 <- asreml(yield ~ gen + lin(colf) + colcode, data=dat,
-                 random= ~ colf + rowf + spl(colf),
-                 rcov = ~ar1(rowf):ar1(colf))
-  
-    # Figure 6 shows serpentine row effects
-    p3 <- predict(m3, data=dat, classify="rowf")$predictions$pvals
-    plot(p3$predicted, type='l', xlab="row number", ylab="BLUP")
-    text(1:22, p3$predicted, c('L','L','M','R','R','M','L','L',
-                               'M','R','R','M','L','L','M','R','R','M','L','L','M','R'))
-    
-    # Define row code (due to serpentine planting). 1=middle, 2=left/right
-    dat <- transform(dat, rowcode = factor(row))
-    levels(dat$rowcode) <- c('2','2','1','2','2','1','2','2','1',
-                             '2','2','1','2','2','1','2','2','1','2','2','1','2')
-  
-    m6 <- asreml(yield ~ gen + lin(colf) + colcode +rowcode, data=dat,
-                 random= ~ colf + rowf + spl(col),
-                 rcov = ~ar1(rowf):ar1(colf))
-    plot(variogram(m6), xlim=c(0:17), ylim=c(0,11), zlim=c(0,4000),
-         main="gilmour.serpentine")
-  }
-
-  # ----------------------------------------------------------------------------
+  dat <- transform(dat, rowf=factor(row), colf=factor(10*(col-8)))
+  dat <- dat[order(dat$rowf, dat$colf), ] # Sort order needed by asreml
   
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
-    
-    dat <- transform(dat, rowf=factor(row), colf=factor(10*(col-8)))
-    dat <- dat[order(dat$rowf, dat$colf), ] # Sort order needed by asreml
-    
-    # RCB
-    m0 <- asreml(yield ~ gen, data=dat, random=~rep)
+  # RCB
+  m0 <- asreml(yield ~ gen, data=dat, random=~rep)
     
-    # Add AR1 x AR1
-    m1 <- asreml(yield ~ gen, data=dat,
-                 resid = ~ar1(rowf):ar1(colf))
-  
-    # Add spline
-    m2 <- asreml(yield ~ gen + col, data=dat,
-                 random= ~ spl(col) + colf,
-                 resid = ~ar1(rowf):ar1(colf))
-  
-    # Figure 4 shows serpentine spraying
-    p2 <- predict(m2, data=dat, classify="colf")$pvals
-    plot(p2$predicted, type='b', xlab="column number", ylab="BLUP")
-  
-    # Define column code (due to serpentine spraying)
-    # Rhelp doesn't like double-percent modulus symbol, so compute by hand
-    dat <- transform(dat, colcode = factor(dat$col-floor((dat$col-1)/4)*4 -1))
-  
-    m3 <- asreml(yield ~ gen + lin(colf) + colcode, data=dat,
-                 random= ~ colf + rowf + spl(colf),
-                 resid = ~ar1(rowf):ar1(colf))
-  
-    # Figure 6 shows serpentine row effects
-    p3 <- predict(m3, data=dat, classify="rowf")$pvals
-    plot(p3$predicted, type='l', xlab="row number", ylab="BLUP")
-    text(1:22, p3$predicted, c('L','L','M','R','R','M','L','L',
-                               'M','R','R','M','L','L','M','R','R','M','L','L','M','R'))
-  
-    # Define row code (due to serpentine planting). 1=middle, 2=left/right
-    dat <- transform(dat, rowcode = factor(row))
-    levels(dat$rowcode) <- c('2','2','1','2','2','1','2','2','1',
-                             '2','2','1','2','2','1','2','2','1','2','2','1','2')
-  
-    m6 <- asreml(yield ~ gen + lin(colf) + colcode +rowcode, data=dat,
-                 random= ~ colf + rowf + spl(col),
-                 resid = ~ar1(rowf):ar1(colf))
-    plot(varioGram(m6), xlim=c(0:17), ylim=c(0,11), zlim=c(0,4000),
-         main="gilmour.serpentine")
-  }
-  
+  # Add AR1 x AR1
+  m1 <- asreml(yield ~ gen, data=dat,
+               resid = ~ar1(rowf):ar1(colf))
+  
+  # Add spline
+  m2 <- asreml(yield ~ gen + col, data=dat,
+               random= ~ spl(col) + colf,
+               resid = ~ar1(rowf):ar1(colf))
+  
+  # Figure 4 shows serpentine spraying
+  p2 <- predict(m2, data=dat, classify="colf")$pvals
+  plot(p2$predicted, type='b', xlab="column number", ylab="BLUP")
+  
+  # Define column code (due to serpentine spraying)
+  # Rhelp doesn't like double-percent modulus symbol, so compute by hand
+  dat <- transform(dat, colcode = factor(dat$col-floor((dat$col-1)/4)*4 -1))
+  
+  m3 <- asreml(yield ~ gen + lin(colf) + colcode, data=dat,
+               random= ~ colf + rowf + spl(colf),
+               resid = ~ar1(rowf):ar1(colf))
+  
+  # Figure 6 shows serpentine row effects
+  p3 <- predict(m3, data=dat, classify="rowf")$pvals
+  plot(p3$predicted, type='l', xlab="row number", ylab="BLUP")
+  text(1:22, p3$predicted, c('L','L','M','R','R','M','L','L',
+                             'M','R','R','M','L','L','M','R','R','M','L','L','M','R'))
+  
+  # Define row code (due to serpentine planting). 1=middle, 2=left/right
+  dat <- transform(dat, rowcode = factor(row))
+  levels(dat$rowcode) <- c('2','2','1','2','2','1','2','2','1',
+                           '2','2','1','2','2','1','2','2','1','2','2','1','2')
+  
+  m6 <- asreml(yield ~ gen + lin(colf) + colcode +rowcode, data=dat,
+               random= ~ colf + rowf + spl(col),
+               resid = ~ar1(rowf):ar1(colf))
+  plot(varioGram(m6), xlim=c(0:17), ylim=c(0,11), zlim=c(0,4000),
+       main="gilmour.serpentine")
+
 }
 }
 
diff -Nru agridat-1.17/man/gilmour.slatehall.Rd agridat-1.18/man/gilmour.slatehall.Rd
--- agridat-1.17/man/gilmour.slatehall.Rd	2020-07-04 20:46:08.000000000 +0000
+++ agridat-1.18/man/gilmour.slatehall.Rd	2020-12-19 14:08:10.000000000 +0000
@@ -37,7 +37,7 @@
   of field experiments.
   \emph{Journal of Agricultural, Biological, and Environmental
     Statistics}, 2, 269-293.
-  http://doi.org/10.2307/1400446
+  https://doi.org/10.2307/1400446
 }
 
 \references{
@@ -58,55 +58,26 @@
 
   # ----------------------------------------------------------------------------
 
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") < "4") {
-    # asreml3
-
-    # Model 4 of Gilmour et al 1997
-    dat <- transform(dat, xf=factor(col), yf=factor(row))
-    dat <- dat[order(dat$xf, dat$yf), ]
-    m4 <- asreml(yield ~ gen + lin(row), data=dat,
-                 random = ~ dev(row) + dev(col),
-                 rcov = ~ ar1(xf):ar1(yf))
-    # coef(m4)$fixed[1] # linear row
-    # [1] 31.72252 # (sign switch due to row ordering)
-  
-    vc(m4)
-    ##      effect component std.error z.ratio constr
-    ##    dev(row) 20290     10260         2      pos
-    ##    dev(col)  2519      1959         1.3    pos
-    ##  R!variance 23950      4616         5.2    pos
-    ##    R!xf.cor     0.439     0.113     3.9  uncon
-    ##    R!yf.cor     0.125     0.117     1.1  uncon
-  
-    plot(variogram(m4), main="gilmour.slatehall")
-  }
-  
-  # ----------------------------------------------------------------------------
+  libs(asreml,lucid) # asreml4
 
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
-
-    # Model 4 of Gilmour et al 1997
-    dat <- transform(dat, xf=factor(col), yf=factor(row))
-    dat <- dat[order(dat$xf, dat$yf), ]
-    m4 <- asreml(yield ~ gen + lin(row), data=dat,
-                 random = ~ dev(row) + dev(col),
-                 resid = ~ ar1(xf):ar1(yf))
-    # coef(m4)$fixed[1] # linear row
-    # [1] 31.72252 # (sign switch due to row ordering)
-
-    vc(m4)
-    ##       effect component std.error z.ratio bound %ch
-    ##     dev(col)  2519      1959         1.3     P   0
-    ##     dev(row) 20290     10260         2       P   0
-    ##     xf:yf(R) 23950      4616         5.2     P   0
-    ## xf:yf!xf!cor     0.439     0.113     3.9     U   0
-    ## xf:yf!yf!cor     0.125     0.117     1.1     U   0
-    
-    plot(varioGram(m4), main="gilmour.slatehall")
-  }
+  # Model 4 of Gilmour et al 1997
+  dat <- transform(dat, xf=factor(col), yf=factor(row))
+  dat <- dat[order(dat$xf, dat$yf), ]
+  m4 <- asreml(yield ~ gen + lin(row), data=dat,
+               random = ~ dev(row) + dev(col),
+               resid = ~ ar1(xf):ar1(yf))
+  # coef(m4)$fixed[1] # linear row
+  # [1] 31.72252 # (sign switch due to row ordering)
+
+  vc(m4)
+  ##       effect component std.error z.ratio bound %ch
+  ##     dev(col)  2519      1959         1.3     P   0
+  ##     dev(row) 20290     10260         2       P   0
+  ##     xf:yf(R) 23950      4616         5.2     P   0
+  ## xf:yf!xf!cor     0.439     0.113     3.9     U   0
+  ## xf:yf!yf!cor     0.125     0.117     1.1     U   0
+  
+  plot(varioGram(m4), main="gilmour.slatehall")
 
 }
 }
diff -Nru agridat-1.17/man/gomez.wetdry.Rd agridat-1.18/man/gomez.wetdry.Rd
--- agridat-1.17/man/gomez.wetdry.Rd	2019-11-22 17:04:10.000000000 +0000
+++ agridat-1.18/man/gomez.wetdry.Rd	2020-12-11 20:49:06.000000000 +0000
@@ -32,7 +32,7 @@
   Rong-Cai Yang, Patricia Juskiw. (2011).
   Analysis of covariance in agronomy and crop research.
   Canadian Journal of Plant Science, 91:621-641.
-  http://doi.org/10.4141/cjps2010-032
+  https://doi.org/10.4141/cjps2010-032
 }
 
 \examples{
diff -Nru agridat-1.17/man/gotway.hessianfly.Rd agridat-1.18/man/gotway.hessianfly.Rd
--- agridat-1.17/man/gotway.hessianfly.Rd	2020-07-04 20:50:01.000000000 +0000
+++ agridat-1.18/man/gotway.hessianfly.Rd	2020-12-11 23:47:58.000000000 +0000
@@ -30,10 +30,10 @@
   \emph{Journal of Agricultural, Biological, and Environmental
     Statistics}, 2, 157-178.
 
-  http://doi.org/10.2307/1400401
+  https://doi.org/10.2307/1400401
 }
 \references{
-  The GLIMMIX procedure. http://www.ats.ucla.edu/stat/SAS/glimmix.pdf
+  The GLIMMIX procedure. https://www.ats.ucla.edu/stat/SAS/glimmix.pdf
 }
 \examples{
 \dontrun{
@@ -43,10 +43,11 @@
   dat <- gotway.hessianfly
   
   dat$prop <- dat$y / dat$n
+  
   libs(desplot)
   desplot(dat, prop~long*lat,
           aspect=1, # true aspect
-          out1=block, text=gen, cex=1, shorten='no',
+          out1=block, num=gen, cex=.75,
           main="gotway.hessianfly")
   
 
@@ -58,7 +59,9 @@
              data=dat, family=binomial(), ranPars=list(nu=0.5, rho=1/.7))
   summary(m1)
   fixef(m1)
-  filled.mapMM(m1)
+  # The following line fails with "Invalid graphics state"
+  # when trying to use pkgdown::build_site
+  # filled.mapMM(m1)
 
   # ----------------------------------------------------------------------------
 
diff -Nru agridat-1.17/man/graybill.heteroskedastic.Rd agridat-1.18/man/graybill.heteroskedastic.Rd
--- agridat-1.17/man/graybill.heteroskedastic.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/graybill.heteroskedastic.Rd	2020-12-11 20:49:11.000000000 +0000
@@ -30,7 +30,7 @@
   Hans-Pieter Piepho, 1994.
   Missing observations in the analysis of stability.
   \emph{Heredity}, 72, 141--145.
-  http://doi.org/10.1038/hdy.1994.20
+  https://doi.org/10.1038/hdy.1994.20
 }
 
 \examples{
diff -Nru agridat-1.17/man/gregory.cotton.Rd agridat-1.18/man/gregory.cotton.Rd
--- agridat-1.17/man/gregory.cotton.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/gregory.cotton.Rd	2020-12-11 20:49:14.000000000 +0000
@@ -72,7 +72,7 @@
 
 
 # Figure 2 of Gregory. Not recommended, but an interesting exercise.
-# http://stackoverflow.com/questions/13887365
+# https://stackoverflow.com/questions/13887365
 if(FALSE){
   libs(ggplot2)
   d1 <- subset(dat, year=="Y1")
diff -Nru agridat-1.17/man/hanks.sprinkler.Rd agridat-1.18/man/hanks.sprinkler.Rd
--- agridat-1.17/man/hanks.sprinkler.Rd	2020-07-29 12:33:08.000000000 +0000
+++ agridat-1.18/man/hanks.sprinkler.Rd	2020-12-19 14:14:47.000000000 +0000
@@ -35,7 +35,7 @@
   Statistical Analysis of Results from Irrigation Experiments Using the
   Line-Source Sprinkler System.
   \emph{Soil Science Society of America Journal}, 44, 886-888.
-  http://doi.org/10.2136/sssaj1980.03615995004400040048x
+  https://doi.org/10.2136/sssaj1980.03615995004400040048x
 }
 \references{
   Johnson, D. E., Chaudhuri, U. N., and Kanemasu, E. T. (1983).
@@ -50,7 +50,7 @@
     Disciplines, Southern Cooperative Series Bulletin No. 343}, 104-122.
 
   SAS Stat User's Guide.
-  http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_mixed_sect038.htm
+  https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_mixed_sect038.htm
 }
 
 \examples{
@@ -131,7 +131,7 @@
     m1 <- asreml(yield ~ gen + dir + irrf + gen:dir + gen:irrf + dir:irrf,
                  data=dat,
                  random= ~ block + block:dir + block:irrf,
-                 resid = ~ block:gen:corb(subf, 1))
+                 resid = ~ block:gen:corb(subf, 2))
 
     lucid::vc(m1)
     #                   effect component std.error z.ratio bound %ch
diff -Nru agridat-1.17/man/hanover.whitepine.Rd agridat-1.18/man/hanover.whitepine.Rd
--- agridat-1.17/man/hanover.whitepine.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/hanover.whitepine.Rd	2020-08-24 18:06:50.000000000 +0000
@@ -41,31 +41,30 @@
   None  
 }
 \examples{
-
-library(agridat)
-
-data(hanover.whitepine)
-dat <- hanover.whitepine
-
-libs(lattice)
-# Relatively high male-female interaction in growth comared
-# to additive gene action. Response is more consistent within
-# male progeny than female progeny.
-# with(dat, interaction.plot(female, male, length))
-# with(dat, interaction.plot(male, female, length))
-bwplot(length ~ male|female, data=dat,
-       main="hanover.whitepine - length for male:female crosses",
-       xlab="Male parent", ylab="Epicotyl length")
-
-# Progeny sums match Becker p 83
-sum(dat$length) # 380.58
-aggregate(length ~  female + male, data=dat, FUN=sum)
-
-# Sum of squares matches Becker p 85
-m1 <- aov(length ~ rep + male + female + male:female, data=dat)
-anova(m1)
-
 \dontrun{
+  
+  library(agridat)
+  data(hanover.whitepine)
+  dat <- hanover.whitepine
+
+  libs(lattice)
+  # Relatively high male-female interaction in growth comared
+  # to additive gene action. Response is more consistent within
+  # male progeny than female progeny.
+  # with(dat, interaction.plot(female, male, length))
+  # with(dat, interaction.plot(male, female, length))
+  bwplot(length ~ male|female, data=dat,
+         main="hanover.whitepine - length for male:female crosses",
+         xlab="Male parent", ylab="Epicotyl length")
+  
+  # Progeny sums match Becker p 83
+  sum(dat$length) # 380.58
+  aggregate(length ~  female + male, data=dat, FUN=sum)
+  
+  # Sum of squares matches Becker p 85
+  m1 <- aov(length ~ rep + male + female + male:female, data=dat)
+  anova(m1)
+  
   # Variance components match Becker p. 85
   libs(lme4)
   libs(lucid)
diff -Nru agridat-1.17/man/harris.multi.uniformity.Rd agridat-1.18/man/harris.multi.uniformity.Rd
--- agridat-1.17/man/harris.multi.uniformity.Rd	2020-07-04 20:53:03.000000000 +0000
+++ agridat-1.18/man/harris.multi.uniformity.Rd	2020-12-11 20:56:59.000000000 +0000
@@ -81,13 +81,13 @@
   Harris, J Arthur and Scofield, CS. (1920).
   Permanence of differences in the plats of an experimental field.
   Jour. Agr. Res, 20, 335-356.
-  http://naldc.nal.usda.gov/catalog/IND43966236
+  https://naldc.nal.usda.gov/catalog/IND43966236
 
   Harris, J Arthur and Scofield, CS. (1928).
   Further studies on the permanence of differences in the plots of an
   experimental field.
   Jour. Agr. Res, 36, 15--40.
-  http://naldc.nal.usda.gov/catalog/IND43967538
+  https://naldc.nal.usda.gov/catalog/IND43967538
 }
 
 \examples{
diff -Nru agridat-1.17/man/harville.lamb.Rd agridat-1.18/man/harville.lamb.Rd
--- agridat-1.17/man/harville.lamb.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/harville.lamb.Rd	2020-12-11 20:49:19.000000000 +0000
@@ -32,7 +32,7 @@
   Confidence Intervals for a Variance Ratio, or for Heritability, in an
   Unbalanced Mixed Linear Model.
   \emph{Biometrics}, 41, 137-152.
-  http://doi.org/10.2307/2530650
+  https://doi.org/10.2307/2530650
 }
 \references{
   Jiming Jiang,
diff -Nru agridat-1.17/man/hayman.tobacco.Rd agridat-1.18/man/hayman.tobacco.Rd
--- agridat-1.17/man/hayman.tobacco.Rd	2019-12-05 22:59:38.000000000 +0000
+++ agridat-1.18/man/hayman.tobacco.Rd	2020-12-11 20:49:22.000000000 +0000
@@ -42,11 +42,11 @@
 
   B. I. Hayman (1954a). The Analysis of Variance of Diallel Tables.
   \emph{Biometrics}, 10, 235-244.  Table 5, page 241.
-  http://doi.org/10.2307/3001877
+  https://doi.org/10.2307/3001877
 
   Hayman, B.I. (1954b). The theory and analysis of diallel crosses.
   \emph{Genetics}, 39, 789-809.  Table 3, page 805.
-  http://www.genetics.org/content/39/6/789.full.pdf
+  https://www.genetics.org/content/39/6/789.full.pdf
 
 }
 
@@ -63,6 +63,11 @@
   C. Clark Cockerham and B. S. Weir. (1977).
   Quadratic analyses of reciprocal crosses.
   \emph{Biometrics} 33, 187-203.  Appendix C.
+
+  Andrea Onofri, Niccolo Terzaroli, Luigi Russi (2020).
+  Linear models for diallel crosses: A review with R functions.
+  Theoretical and Applied Genetics.
+  https://doi.org/10.1007/s00122-020-03716-8
 }
 
 \examples{
@@ -73,11 +78,26 @@
   # 1951 data.  Fit the first REML model of Mohring 2011 Supplement.  
   data(hayman.tobacco)
   dat1 <- subset(hayman.tobacco, year==1951)
+
+  # libs(lmDiallel)
+  # m1  <- lm.diallel(day ~ male+female, Block=block, data=dat1, fct="HAYMAN2")
+  # anova(m1) # Similar to table 7 of Hayman 1954a
+  ## Response: day
+  ##                Df  Sum Sq Mean Sq F value    Pr(>F)    
+  ## Block           1    1.42    1.42  0.3416   0.56100    
+  ## Mean Dom. Dev.  1  307.97  307.97 73.8840 3.259e-12 ***
+  ## GCA             7 2777.17  396.74 95.1805 < 2.2e-16 ***
+  ## Dom. Dev.       7  341.53   48.79 11.7050 1.957e-09 ***
+  ## SCA            20  372.89   18.64  4.4729 2.560e-06 ***
+  ## RGCA            7   67.39    9.63  2.3097   0.03671 *  
+  ## RSCA           21  123.73    5.89  1.4135   0.14668    
+  ## Residuals      63  262.60                              
   
   # Make a factor 'comb' in which G1xG2 is the same cross as G2xG1
-  dat1 <- transform(dat1, comb =
-                            ifelse(as.character(male) < as.character(female),
-                                   paste0(male,female), paste0(female,male)))
+  dat1 <- transform(dat1,
+                    comb =
+                      ifelse(as.character(male) < as.character(female),
+                             paste0(male,female), paste0(female,male)))
   # 'dr' is the direction of the cross, 0 for self
   dat1$dr <- 1
   dat1 <- transform(dat1,
diff -Nru agridat-1.17/man/hazell.vegetables.Rd agridat-1.18/man/hazell.vegetables.Rd
--- agridat-1.17/man/hazell.vegetables.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/hazell.vegetables.Rd	2020-12-11 20:49:24.000000000 +0000
@@ -31,7 +31,7 @@
   A linear alternative to quadratic and semivariance programming for farm planning
   under uncertainty.
   \emph{Am. J. Agric. Econ.}, 53, 53-62.
-  http://doi.org/10.2307/3180297
+  https://doi.org/10.2307/3180297
 }
 \references{
   Carlos Romero, Tahir Rehman. (2003).
diff -Nru agridat-1.17/man/hessling.argentina.Rd agridat-1.18/man/hessling.argentina.Rd
--- agridat-1.17/man/hessling.argentina.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/hessling.argentina.Rd	2020-12-11 20:49:25.000000000 +0000
@@ -37,7 +37,7 @@
   N. A. Hessling, 1922.
   Relations between the weather and the yield of wheat in the
   Argentine republic, \emph{Monthly Weather Review}, 50, 302-308.
-  http://doi.org/10.1175/1520-0493(1922)50<302:RBTWAT>2.0.CO;2
+  https://doi.org/10.1175/1520-0493(1922)50<302:RBTWAT>2.0.CO;2
 }
 \examples{
 \dontrun{
diff -Nru agridat-1.17/man/hildebrand.systems.Rd agridat-1.18/man/hildebrand.systems.Rd
--- agridat-1.17/man/hildebrand.systems.Rd	2020-07-21 16:36:28.000000000 +0000
+++ agridat-1.18/man/hildebrand.systems.Rd	2020-12-19 14:27:45.000000000 +0000
@@ -37,7 +37,7 @@
   H. P. Piepho, 1998.
   Methods for Comparing the Yield Stability of Cropping Systems.
   \emph{Journal of Agronomy and Crop Science}, 180, 193--213.
-  http://doi.org/10.1111/j.1439-037X.1998.tb00526.x
+  https://doi.org/10.1111/j.1439-037X.1998.tb00526.x
 }
 
 \examples{
@@ -72,48 +72,45 @@
 
   # ----------
 
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
-
-    # Environmental variance model, unstructured correlations
-  
-    dat <- dat[order(dat$system, dat$farm),]
-    m1 <- asreml(yield ~ system, data=dat,
-                 resid = ~us(system):farm)
-  
-    # Means, table 5
-    ## predict(m1, data=dat, classify="system")$pvals
-    ##  system pred.value std.error  est.stat
-    ##     CCA      1.164    0.2816 Estimable
-    ##    CCAF      2.657    0.3747 Estimable
-    ##      LM      1.35     0.1463 Estimable
-    ##     LMF      2.7      0.2561 Estimable
-  
-    # Variances, table 5
-    # vc(m1)[c(2,4,7,11),]
-    ##              effect component std.error z.ratio constr
-    ##    R!system.CCA:CCA    1.11      0.4354     2.5    pos
-    ##  R!system.CCAF:CCAF    1.966     0.771      2.5    pos
-    ##      R!system.LM:LM    0.2996    0.1175     2.5    pos
-    ##    R!system.LMF:LMF    0.9185    0.3603     2.5    pos
-  
-    # Stability variance model
-    m2 <- asreml(yield ~ system, data=dat,
-                 random = ~ farm,
-                 resid = ~ dsum( ~ units|system))
-    m2 <- update(m2)
-    # predict(m2, data=dat, classify="system")$pvals
-  
-    ## # Variances, table 6
-    # vc(m2)
-    ##        effect component std.error z.ratio bound %ch
-    ##          farm 0.2998      0.1187   2.5        P 0  
-    ##  system_CCA!R 0.4133      0.1699   2.4        P 0  
-    ## system_CCAF!R 1.265       0.5152   2.5        P 0  
-    ##   system_LM!R 0.0003805   0.05538  0.0069     P 1.5
-    ##  system_LMF!R 0.5294      0.2295   2.3        P 0  
-}
+  libs(asreml,lucid) # asreml4
+
+  # Environmental variance model, unstructured correlations
+  
+  dat <- dat[order(dat$system, dat$farm),]
+  m1 <- asreml(yield ~ system, data=dat,
+               resid = ~us(system):farm)
+  
+  # Means, table 5
+  ## predict(m1, data=dat, classify="system")$pvals
+  ##  system pred.value std.error  est.stat
+  ##     CCA      1.164    0.2816 Estimable
+  ##    CCAF      2.657    0.3747 Estimable
+  ##      LM      1.35     0.1463 Estimable
+  ##     LMF      2.7      0.2561 Estimable
+  
+  # Variances, table 5
+  # vc(m1)[c(2,4,7,11),]
+  ##              effect component std.error z.ratio constr
+  ##    R!system.CCA:CCA    1.11      0.4354     2.5    pos
+  ##  R!system.CCAF:CCAF    1.966     0.771      2.5    pos
+  ##      R!system.LM:LM    0.2996    0.1175     2.5    pos
+  ##    R!system.LMF:LMF    0.9185    0.3603     2.5    pos
+  
+  # Stability variance model
+  m2 <- asreml(yield ~ system, data=dat,
+               random = ~ farm,
+               resid = ~ dsum( ~ units|system))
+  m2 <- update(m2)
+  # predict(m2, data=dat, classify="system")$pvals
+  
+  ## # Variances, table 6
+  # vc(m2)
+  ##        effect component std.error z.ratio bound %ch
+  ##          farm 0.2998      0.1187   2.5        P 0  
+  ##  system_CCA!R 0.4133      0.1699   2.4        P 0  
+  ## system_CCAF!R 1.265       0.5152   2.5        P 0  
+  ##   system_LM!R 0.0003805   0.05538  0.0069     P 1.5
+  ##  system_LMF!R 0.5294      0.2295   2.3        P 0  
 
 }
 }
diff -Nru agridat-1.17/man/holland.arthropods.Rd agridat-1.18/man/holland.arthropods.Rd
--- agridat-1.17/man/holland.arthropods.Rd	2019-11-22 17:05:26.000000000 +0000
+++ agridat-1.18/man/holland.arthropods.Rd	2020-12-11 20:49:31.000000000 +0000
@@ -39,7 +39,7 @@
   within winter wheat.
   Bulletin of Entomological Research, 89: 499-513.  Figure 3 (large
   grid in 1996).
-  http://doi.org/10.1017/S0007485399000656
+  https://doi.org/10.1017/S0007485399000656
 }
 
 \examples{
diff -Nru agridat-1.17/man/holtsmark.timothy.uniformity.Rd agridat-1.18/man/holtsmark.timothy.uniformity.Rd
--- agridat-1.17/man/holtsmark.timothy.uniformity.Rd	2020-07-04 20:55:15.000000000 +0000
+++ agridat-1.18/man/holtsmark.timothy.uniformity.Rd	2020-12-11 20:57:00.000000000 +0000
@@ -31,7 +31,7 @@
   Om Muligheder for at indskraenke de Fejl, som ved Markforsog betinges af Jordens Uensartethed.
   Tidsskrift for Landbrugets Planteavl. 12, 330-351. (In Danish)
   https://books.google.com/books?id=MdM0AQAAMAAJ&pg=PA330
-  http://dca.au.dk/publikationer/historiske/planteavl/
+  https://dca.au.dk/publikationer/historiske/planteavl/
 
   Uber die Fehler, welche bei Feldversuchen, durch die Ungleichartigkeit des Bodens bedingt werden.
   Die Landwirtschaftlichen Versuchs-Stationen, 65, 1--22. (In German)
diff -Nru agridat-1.17/man/huehn.wheat.Rd agridat-1.18/man/huehn.wheat.Rd
--- agridat-1.17/man/huehn.wheat.Rd	2020-07-05 17:02:54.000000000 +0000
+++ agridat-1.18/man/huehn.wheat.Rd	2020-12-11 20:57:01.000000000 +0000
@@ -31,7 +31,7 @@
   Beitrage zur Erfassung der phanotypischen Stabilitat I.
   Vorschlag einiger auf Ranginformationen beruhenden Stabilitatsparameter.
   \emph{EDV in Medizin und Biologie}, 10 (4), 112-117.  Table 1.
-  http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-145979
+  https://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-145979
 }
 
 \references{
diff -Nru agridat-1.17/man/hughes.grapes.Rd agridat-1.18/man/hughes.grapes.Rd
--- agridat-1.17/man/hughes.grapes.Rd	2019-11-22 17:06:08.000000000 +0000
+++ agridat-1.18/man/hughes.grapes.Rd	2020-12-11 20:49:34.000000000 +0000
@@ -43,7 +43,7 @@
   Some methods allowing for aggregated patterns of disease incidence in
   the analysis of data from designed experiments.
   Plant Pathology, 44, 927--943.
-  http://doi.org/10.1111/j.1365-3059.1995.tb02651.x
+  https://doi.org/10.1111/j.1365-3059.1995.tb02651.x
 }
 
 \references{
@@ -51,7 +51,7 @@
   Analysing disease incidence data from designed experiments by
   generalized linear mixed models.
   Plant Pathology, 48, 668--684.
-  http://doi.org/10.1046/j.1365-3059.1999.00383.x
+  https://doi.org/10.1046/j.1365-3059.1999.00383.x
 }
 
 \examples{
diff -Nru agridat-1.17/man/hunter.corn.Rd agridat-1.18/man/hunter.corn.Rd
--- agridat-1.17/man/hunter.corn.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/hunter.corn.Rd	2020-12-11 20:49:36.000000000 +0000
@@ -28,7 +28,7 @@
   Variations in Fertility Levels Upon the Yield and Protein
   Content of Field Corn in Eastern Oregon.
   \emph{Soil Science Society of America Journal}, 19, 214-218.
-  http://doi.org/10.2136/sssaj1955.03615995001900020027x
+  https://doi.org/10.2136/sssaj1955.03615995001900020027x
 }
 \references{
   James Leo Paschal, Burton Leroy French (1956).
diff -Nru agridat-1.17/man/igue.sugarcane.uniformity.Rd agridat-1.18/man/igue.sugarcane.uniformity.Rd
--- agridat-1.17/man/igue.sugarcane.uniformity.Rd	2020-07-04 20:55:31.000000000 +0000
+++ agridat-1.18/man/igue.sugarcane.uniformity.Rd	2020-12-11 20:49:37.000000000 +0000
@@ -29,7 +29,7 @@
   Toshio Igue, Ademar Espironelo, Heitor Cantarella, Erseni Joao Nelli. (1991).
   Tamanho e forma de parcela experimental para cana-de-acucar (Plot size and shape for sugar cane experiments).
   Bragantia, 50, 163-180. Appendix, page 169-170.
-  http://dx.doi.org/10.1590/S0006-87051991000100016
+  https://dx.doi.org/10.1590/S0006-87051991000100016
 }
 \references{
   None
diff -Nru agridat-1.17/man/ilri.sheep.Rd agridat-1.18/man/ilri.sheep.Rd
--- agridat-1.17/man/ilri.sheep.Rd	2019-12-07 16:49:07.000000000 +0000
+++ agridat-1.18/man/ilri.sheep.Rd	2020-12-11 20:57:02.000000000 +0000
@@ -49,7 +49,7 @@
 \source{ 
   Case Study 4: Mixed model analysis for the estimation of components of genetic
   variation in lamb weaning weight. International Livestock Research Institute.
-  http://www.ilri.org/biometrics/CS/case\%20study\%204/case\%20study\%204.1.htm
+  https://www.ilri.org/biometrics/CS/case\%20study\%204/case\%20study\%204.1.htm
   Retrieved Dec 2011.
 
   Licensed with Creative Commons BY-NC-SA 3.0 Unported license.  
diff -Nru agridat-1.17/man/immer.sugarbeet.uniformity.Rd agridat-1.18/man/immer.sugarbeet.uniformity.Rd
--- agridat-1.17/man/immer.sugarbeet.uniformity.Rd	2020-07-04 20:56:04.000000000 +0000
+++ agridat-1.18/man/immer.sugarbeet.uniformity.Rd	2020-12-11 20:57:03.000000000 +0000
@@ -33,7 +33,7 @@
   Planted in 1930.  Field conditions were uniform.  Beets were planted
   in rows 22 inches apart.  After thinning, one beet was left in each
   12-inch unit.  At harvest, the field was marked out in plot 33 feet
-  long, with a 2-foot alley between plots to minimize carrover from the
+  long, with a 2-foot alley between plots to minimize carryover from the
   harvester.  A sample of 10 beets was taken uniformly (approximately
   every third beet) and measured for sugar percentage and apparent
   purity.  The beets were counted at weighing time and the yields were
@@ -47,7 +47,7 @@
   F. R. Immer. 1932.
   Size and shape of plot in relation to field experiments with sugar beets.
   Jour. Agr. Research, 44, 649--668.
-  http://naldc.nal.usda.gov/download/IND43968078/PDF
+  https://naldc.nal.usda.gov/download/IND43968078/PDF
 }
 
 \examples{
diff -Nru agridat-1.17/man/ivins.herbs.Rd agridat-1.18/man/ivins.herbs.Rd
--- agridat-1.17/man/ivins.herbs.Rd	2019-11-25 18:11:27.000000000 +0000
+++ agridat-1.18/man/ivins.herbs.Rd	2020-12-11 20:49:43.000000000 +0000
@@ -58,20 +58,20 @@
   Ivins, JD. (1952).
   Concerning the Ecology of Urtica Dioica L.,
   \emph{Journal of Ecology}, 40, 380-382.
-  http://doi.org/10.2307/2256806
+  https://doi.org/10.2307/2256806
 }
 
 \references{
   Ivins, JD (1950). Weeds in relation to the establishment of the Ley.
   \emph{Grass and Forage Science}, 5, 237--242.
-  http://doi.org/10.1111/j.1365-2494.1950.tb01287.x
+  https://doi.org/10.1111/j.1365-2494.1950.tb01287.x
 
   O'Gorman, T.W. (2001). A comparison of the F-test, Friedman's test,
   and several aligned rank tests for the analysis of randomized complete
   blocks.
   \emph{Journal of agricultural, biological, and environmental
     statistics}, 6, 367--378.
-  http://doi.org/10.1198/108571101317096578
+  https://doi.org/10.1198/108571101317096578
 }
 
 \examples{
diff -Nru agridat-1.17/man/jansen.apple.Rd agridat-1.18/man/jansen.apple.Rd
--- agridat-1.17/man/jansen.apple.Rd	2019-11-22 17:06:28.000000000 +0000
+++ agridat-1.18/man/jansen.apple.Rd	2020-12-11 20:49:45.000000000 +0000
@@ -35,7 +35,7 @@
   The analysis of proportions in agricultural experiments by a
   generalized linear mixed model. 
   Statistica Neerlandica, 47(3), 161-174.
-  http://doi.org/10.1111/j.1467-9574.1993.tb01414.x
+  https://doi.org/10.1111/j.1467-9574.1993.tb01414.x
 }
 
 \references{
diff -Nru agridat-1.17/man/jansen.carrot.Rd agridat-1.18/man/jansen.carrot.Rd
--- agridat-1.17/man/jansen.carrot.Rd	2019-11-22 17:06:46.000000000 +0000
+++ agridat-1.18/man/jansen.carrot.Rd	2020-12-11 20:49:46.000000000 +0000
@@ -37,7 +37,7 @@
   The analysis of proportions in agricultural experiments by a
   generalized linear mixed model. 
   Statistica Neerlandica, 47(3), 161-174.
-  http://doi.org/10.1111/j.1467-9574.1993.tb01414.x
+  https://doi.org/10.1111/j.1467-9574.1993.tb01414.x
 }
 \references{
   None.
diff -Nru agridat-1.17/man/jansen.strawberry.Rd agridat-1.18/man/jansen.strawberry.Rd
--- agridat-1.17/man/jansen.strawberry.Rd	2019-11-22 17:06:59.000000000 +0000
+++ agridat-1.18/man/jansen.strawberry.Rd	2020-12-11 20:49:48.000000000 +0000
@@ -41,7 +41,7 @@
   J. Jansen, 1990.
   On the statistical analysis of ordinal data when extravariation is present.
   Applied Statistics, 39, 75-84, Table 1.
-  http://doi.org/10.2307/2347813
+  https://doi.org/10.2307/2347813
 }
 
 \examples{
diff -Nru agridat-1.17/man/jayaraman.bamboo.Rd agridat-1.18/man/jayaraman.bamboo.Rd
--- agridat-1.17/man/jayaraman.bamboo.Rd	1970-01-01 00:00:00.000000000 +0000
+++ agridat-1.18/man/jayaraman.bamboo.Rd	2020-08-24 21:28:00.000000000 +0000
@@ -0,0 +1,52 @@
+\name{jayaraman.bamboo}
+\alias{jayaraman.bamboo}
+\docType{data}
+\title{
+  Bamboo progeny trial
+}
+\description{
+  Bamboo progeny trial in 2 locations, 3 blocks
+}
+\usage{data("jayaraman.bamboo")}
+\format{
+  A data frame with 216 observations on the following 5 variables.
+  \describe{
+    \item{\code{loc}}{location factor}
+    \item{\code{block}}{block factor}
+    \item{\code{tree}}{tree factor}
+    \item{\code{family}}{family factor}
+    \item{\code{height}}{height, cm}
+  }
+}
+\details{
+  Data from a replicated trial of bamboo at two locations in Kerala,
+  India.  Each location had 3 blocks.  In each block were 6 families,
+  with 6 trees in each family. 
+}
+\source{
+  K. Jayaraman (1999). "A Statistical Manual For Forestry Research".
+  Forestry Research Support Programme for Asia and the Pacific. Page 170.
+}
+\references{
+  None
+}
+\examples{
+\dontrun{
+  library(agridat)
+  data(jayaraman.bamboo)
+  dat <- jayaraman.bamboo
+
+  # very surprising differences between locations
+  libs(lattice)
+  bwplot(height ~ family|loc, d2, main="jayaraman.bamboo")
+  # match Jayarman's anova table 6.3, page 173
+  # m1 <- aov(height ~ loc+loc:block + family + family:loc + family:loc:block, data=d2)
+  # anova(m1)
+
+  # more modern approach with mixed model, match variance components needed
+  # for equation 6.9, heritability of the half-sib averages as
+  m2 <- lme4::lmer(height ~ 1 + (1|loc/block) + (1|family/loc/block), data=d2)
+  lucid::vc(m2)
+}
+}
+\keyword{datasets}
diff -Nru agridat-1.17/man/jenkyn.mildew.Rd agridat-1.18/man/jenkyn.mildew.Rd
--- agridat-1.17/man/jenkyn.mildew.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/jenkyn.mildew.Rd	2020-12-11 20:49:49.000000000 +0000
@@ -33,7 +33,7 @@
   Incorporating Overlap Effects from Neighboring Units into Response
   Surface Models.
   \emph{Appl Statist}, 29, 128--134.
-  http://doi.org/10.2307/2986297
+  https://doi.org/10.2307/2986297
 }
 
 \references{
diff -Nru agridat-1.17/man/john.alpha.Rd agridat-1.18/man/john.alpha.Rd
--- agridat-1.17/man/john.alpha.Rd	2019-12-08 14:46:01.000000000 +0000
+++ agridat-1.18/man/john.alpha.Rd	2020-12-11 20:49:52.000000000 +0000
@@ -39,7 +39,7 @@
   Computing heritability and selection response from unbalanced
   plant breeding trials,
   \emph{Genetics}, 177, 1881-1888.
-  http://doi.org/10.1534/genetics.107.074229
+  https://doi.org/10.1534/genetics.107.074229
 }
 
 \examples{
@@ -90,83 +90,45 @@
   
 
   # ----------
+  # asreml4
 
   libs(asreml,lucid)
-  if( utils::packageVersion("asreml") < "4") {
-    # asreml3
 
-    # Heritability calculation of Piepho & Mohring, Example 1
-    m3 <- asreml(yield ~ 1 + rep, data=dat, random=~ gen + rep:block)
-    sg2 <- summary(m3)$varcomp['gen!gen.var','component'] # .142902
-
-    # Average variance of a difference of two adjusted means (BLUP)
-
-    p3 <- predict(m3, data=dat, classify="gen", sed=TRUE)
-    # Matrix of pair-wise SED values, squared
-    vdiff <- p3$predictions$sed^2
-    # Average variance of two DIFFERENT means (using lower triangular of vdiff)
-    vblup <- mean(vdiff[lower.tri(vdiff)]) # .05455038
-
-    # Note that without sed=TRUE, asreml reports square root of the average variance
-    # of a difference between the variety means, so the following gives the same value
-    # predict(m3, data=dat, classify="gen")$pred$avsed ^ 2 # .05455038
-
-    # Average variance of a difference of two adjusted means (BLUE)
-    m4 <- asreml(yield ~ 1 + gen + rep, data=dat, random = ~ rep:block)
-    p4 <- predict(m4, data=dat, classify="gen", sed=TRUE)
-    vdiff <- p4$predictions$sed^2
-    vblue <- mean(vdiff[lower.tri(vdiff)]) # .07010875
-    # Again, could use predict(m4, data=dat, classify="gen")$pred$avsed ^ 2
+  # Heritability calculation of Piepho & Mohring, Example 1
 
-    # H^2 Ad-hoc measure of heritability
-    sg2 / (sg2 + vblue/2) # .803
-
-    # H^2c Similar measure proposed by Cullis.
-    1-(vblup / 2 / sg2) # .809
-
-  }
-
-  # ----------
-
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
-
-    # Heritability calculation of Piepho & Mohring, Example 1
-
-    m3 <- asreml(yield ~ 1 + rep, data=dat, random=~ gen + rep:block)
-    sg2 <- summary(m3)$varcomp['gen','component'] # .142902
+  m3 <- asreml(yield ~ 1 + rep, data=dat, random=~ gen + rep:block)
+  sg2 <- summary(m3)$varcomp['gen','component'] # .142902
+  
+  # Average variance of a difference of two adjusted means (BLUP)
+  
+  p3 <- predict(m3, data=dat, classify="gen", sed=TRUE)
+  # Matrix of pair-wise SED values, squared
+  vdiff <- p3$sed^2
+  # Average variance of two DIFFERENT means (using lower triangular of vdiff)
+  vblup <- mean(vdiff[lower.tri(vdiff)]) # .05455038
+  
+  # Note that without sed=TRUE, asreml reports square root of the average variance
+  # of a difference between the variety means, so the following gives the same value
+  # predict(m3, data=dat, classify="gen")$avsed ^ 2 # .05455038
+  
+  # Average variance of a difference of two adjusted means (BLUE)
+  m4 <- asreml(yield ~ 1 + gen + rep, data=dat, random = ~ rep:block)
+  p4 <- predict(m4, data=dat, classify="gen", sed=TRUE)
+  vdiff <- p4$sed^2
+  vblue <- mean(vdiff[lower.tri(vdiff)]) # .07010875
+  # Again, could use predict(m4, data=dat, classify="gen")$avsed ^ 2
+  
+  # H^2 Ad-hoc measure of heritability
+  sg2 / (sg2 + vblue/2) # .803
+  
+  # H^2c Similar measure proposed by Cullis.
+  1-(vblup / 2 / sg2) # .809
 
-    # Average variance of a difference of two adjusted means (BLUP)
-    
-    p3 <- predict(m3, data=dat, classify="gen", sed=TRUE)
-    # Matrix of pair-wise SED values, squared
-    vdiff <- p3$sed^2
-    # Average variance of two DIFFERENT means (using lower triangular of vdiff)
-    vblup <- mean(vdiff[lower.tri(vdiff)]) # .05455038
-  
-    # Note that without sed=TRUE, asreml reports square root of the average variance
-    # of a difference between the variety means, so the following gives the same value
-    # predict(m3, data=dat, classify="gen")$avsed ^ 2 # .05455038
-
-    # Average variance of a difference of two adjusted means (BLUE)
-    m4 <- asreml(yield ~ 1 + gen + rep, data=dat, random = ~ rep:block)
-    p4 <- predict(m4, data=dat, classify="gen", sed=TRUE)
-    vdiff <- p4$sed^2
-    vblue <- mean(vdiff[lower.tri(vdiff)]) # .07010875
-    # Again, could use predict(m4, data=dat, classify="gen")$avsed ^ 2
-  
-    # H^2 Ad-hoc measure of heritability
-    sg2 / (sg2 + vblue/2) # .803
-  
-    # H^2c Similar measure proposed by Cullis.
-    1-(vblup / 2 / sg2) # .809
-  }
 
   # ----------
 
   # Illustrate how to do the same calculations with lme4
-  # http://stackoverflow.com/questions/38697477
+  # https://stackoverflow.com/questions/38697477
   
   libs(lme4)
   
diff -Nru agridat-1.17/man/johnson.blight.Rd agridat-1.18/man/johnson.blight.Rd
--- agridat-1.17/man/johnson.blight.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/johnson.blight.Rd	2020-12-11 20:49:54.000000000 +0000
@@ -27,7 +27,7 @@
   Johnson, D.A. and Alldredge, J.R. and Vakoch, D.L. (1996).
   Potato late blight forecasting models for the semiarid environment of
   south-central Washington. \emph{Phytopathology}, 86, 480--484.
-  http://doi.org/10.1094/Phyto-86-480
+  https://doi.org/10.1094/Phyto-86-480
 }
 
 \references{
diff -Nru agridat-1.17/man/kalamkar.potato.uniformity.Rd agridat-1.18/man/kalamkar.potato.uniformity.Rd
--- agridat-1.17/man/kalamkar.potato.uniformity.Rd	2020-07-04 20:57:04.000000000 +0000
+++ agridat-1.18/man/kalamkar.potato.uniformity.Rd	2020-12-11 20:49:55.000000000 +0000
@@ -31,7 +31,7 @@
   Kalamkar, R.J. (1932).
   Experimental Error and the Field-Plot Technique with Potatoes.
   The Journal of Agricultural Science, 22, 373-385.
-  http://doi.org/10.1017/S0021859600053697
+  https://doi.org/10.1017/S0021859600053697
 }
 
 \examples{
diff -Nru agridat-1.17/man/kalamkar.wheat.uniformity.Rd agridat-1.18/man/kalamkar.wheat.uniformity.Rd
--- agridat-1.17/man/kalamkar.wheat.uniformity.Rd	2020-07-21 16:36:47.000000000 +0000
+++ agridat-1.18/man/kalamkar.wheat.uniformity.Rd	2020-12-19 14:28:36.000000000 +0000
@@ -79,21 +79,18 @@
   
   # ----------
   
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
+  libs(asreml,lucid) # asreml4
     
-    # Show the negative correlation between rows
-    dat <- transform(dat,
-                     rowf=factor(row), colf=factor(col))
-    dat <- dat[order(dat$rowf, dat$colf),]
-    m1 = asreml(yield ~ 1, data=dat, resid= ~ ar1(rowf):ar1(colf))
-    vc(m1)
-    ##             effect component std.error z.ratio bound pctch
-    ##        rowf:colf!R  81.53      3.525      23       P 0  
-    ## rowf:colf!rowf!cor  -0.09464   0.0277     -3.4     U 0.1
-    ## rowf:colf!colf!cor   0.2976    0.02629    11       U 0.1
-  }
+  # Show the negative correlation between rows
+  dat <- transform(dat,
+                   rowf=factor(row), colf=factor(col))
+  dat <- dat[order(dat$rowf, dat$colf),]
+  m1 = asreml(yield ~ 1, data=dat, resid= ~ ar1(rowf):ar1(colf))
+  vc(m1)
+  ##             effect component std.error z.ratio bound pctch
+  ##        rowf:colf!R  81.53      3.525      23       P 0  
+  ## rowf:colf!rowf!cor  -0.09464   0.0277     -3.4     U 0.1
+  ## rowf:colf!colf!cor   0.2976    0.02629    11       U 0.1
 
 }
 }
diff -Nru agridat-1.17/man/keen.potatodamage.Rd agridat-1.18/man/keen.potatodamage.Rd
--- agridat-1.17/man/keen.potatodamage.Rd	2019-11-22 17:07:30.000000000 +0000
+++ agridat-1.18/man/keen.potatodamage.Rd	2020-12-11 20:49:56.000000000 +0000
@@ -44,7 +44,7 @@
   A. Keen and B. Engel.
   Analysis of a mixed model for ordinal data by iterative re-weighted REML.
   Statistica Neerlandica, 51, 129--144. Table 2.
-  http://doi.org/10.1111/1467-9574.00044
+  https://doi.org/10.1111/1467-9574.00044
 }
 
 \examples{
diff -Nru agridat-1.17/man/kempton.barley.uniformity.Rd agridat-1.18/man/kempton.barley.uniformity.Rd
--- agridat-1.17/man/kempton.barley.uniformity.Rd	2020-07-21 16:37:03.000000000 +0000
+++ agridat-1.18/man/kempton.barley.uniformity.Rd	2020-12-19 14:29:07.000000000 +0000
@@ -31,14 +31,14 @@
   R. A. Kempton and C. W. Howes (1981).
   The use of neighbouring plot values in the analysis of variety trials.
   Applied Statistics, 30, 59--70.
-  http://doi.org/10.2307/2346657
+  https://doi.org/10.2307/2346657
 }
 \references{
   McCullagh, P. and Clifford, D., (2006).
   Evidence for conformal invariance of crop yields,
   Proceedings of the Royal Society A: Mathematical, Physical and
     Engineering Science. 462, 2119--2143.
-  http://doi.org/10.1098/rspa.2006.1667
+  https://doi.org/10.1098/rspa.2006.1667
 }
 
 \examples{
@@ -60,22 +60,19 @@
 
   # ----------
 
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
-  
-    dat <- transform(dat, xf = factor(col), yf=factor(row))
-    m1 <- asreml(yield ~ 1, data=dat, resid = ~ar1(xf):ar1(yf))
-    
-    # vc(m1)
-    ##       effect component std.error z.ratio bound %ch
-    ##      xf:yf!R    0.1044   0.02197     4.7     P   0
-    ## xf:yf!xf!cor    0.2458   0.07484     3.3     U   0
-    ## xf:yf!yf!cor    0.8186   0.03821    21       U   0
-    
-    # asreml estimates auto-regression correlations of 0.25, 0.82
-    # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91
-  }
+  libs(asreml,lucid) # asreml4
+  
+  dat <- transform(dat, xf = factor(col), yf=factor(row))
+  m1 <- asreml(yield ~ 1, data=dat, resid = ~ar1(xf):ar1(yf))
+  
+  # vc(m1)
+  ##       effect component std.error z.ratio bound %ch
+  ##      xf:yf!R    0.1044   0.02197     4.7     P   0
+  ## xf:yf!xf!cor    0.2458   0.07484     3.3     U   0
+  ## xf:yf!yf!cor    0.8186   0.03821    21       U   0
+  
+  # asreml estimates auto-regression correlations of 0.25, 0.82
+  # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91
 
   # ----------
 
diff -Nru agridat-1.17/man/kempton.competition.Rd agridat-1.18/man/kempton.competition.Rd
--- agridat-1.17/man/kempton.competition.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/kempton.competition.Rd	2020-12-11 20:50:01.000000000 +0000
@@ -29,11 +29,12 @@
   R Kempton, 1982.
   Adjustment for competition between varieties in plant breeding trials,
   \emph{Journal of Agricultural Science}, 98, 599-611.
-  http://doi.org/10.1017/S0021859600054381
+  https://doi.org/10.1017/S0021859600054381
 }
 
 \examples{
-
+\dontrun{
+  
 library(agridat)
 
 data(kempton.competition)
@@ -58,4 +59,4 @@
 plot(m1, main="kempton.competition")
 
 }
-
+}
diff -Nru agridat-1.17/man/kenward.cattle.Rd agridat-1.18/man/kenward.cattle.Rd
--- agridat-1.17/man/kenward.cattle.Rd	2019-12-05 23:59:28.000000000 +0000
+++ agridat-1.18/man/kenward.cattle.Rd	2020-12-19 14:29:40.000000000 +0000
@@ -39,14 +39,14 @@
   Kenward, Michael G. (1987).
   A Method for Comparing Profiles of Repeated Measurements.
   \emph{Applied Statistics}, 36, 296-308.  Table 1.  
-  http://doi.org/10.2307/2347788
+  https://doi.org/10.2307/2347788
 }
 
 \references{
   W. Zhang, C. Leng and C. Y. Tang (2015).
   A joint modelling approach for longitudinal studies
   \emph{J. R. Statist. Soc. B}, 77 (2015), 219--238.
-  http://doi.org/10.1111/rssb.12065
+  https://doi.org/10.1111/rssb.12065
 }
 
 \examples{
@@ -110,11 +110,8 @@
                  dev(day) + trt:dev(day) ) # non-spline deviation at each time*trt
   
   p1 <- predict(m1, data=dat, classify="trt:day")
-  if( utils::packageVersion("asreml") < "4") {
-    p1 <- p1$predictions$pvals
-  } else {
-    p1 <- p1$pvals
-  }
+  p1 <- p1$pvals
+  
   foo2 <- xyplot(predicted.value ~ day|trt, p1, type='l', lwd=2, lty=1, col="black")
   
   libs(latticeExtra)
diff -Nru agridat-1.17/man/khin.rice.uniformity.Rd agridat-1.18/man/khin.rice.uniformity.Rd
--- agridat-1.17/man/khin.rice.uniformity.Rd	2020-07-04 20:59:14.000000000 +0000
+++ agridat-1.18/man/khin.rice.uniformity.Rd	2020-12-11 20:57:04.000000000 +0000
@@ -31,7 +31,7 @@
   efficiency of designs.
   Dissertation: Imperial College of Tropical Agriculture (ICTA).
   Appendix XV.
-  http://hdl.handle.net/2139/42422
+  https://hdl.handle.net/2139/42422
 }
 \references{
  None.
diff -Nru agridat-1.17/man/kiesselbach.oats.uniformity.Rd agridat-1.18/man/kiesselbach.oats.uniformity.Rd
--- agridat-1.17/man/kiesselbach.oats.uniformity.Rd	2020-07-04 20:59:26.000000000 +0000
+++ agridat-1.18/man/kiesselbach.oats.uniformity.Rd	2020-12-11 20:57:05.000000000 +0000
@@ -40,7 +40,7 @@
   University of Nebraska Agricultural Experiment Station Research Bulletin No. 13.
   Pages 51-72.
   https://archive.org/details/StudiesConcerningTheEliminationOfExperimentalErrorInComparativeCrop
-  http://digitalcommons.unl.edu/extensionhist/430/
+  https://digitalcommons.unl.edu/extensionhist/430/
 }
 \references{
   None.
diff -Nru agridat-1.17/man/kristensen.barley.uniformity.Rd agridat-1.18/man/kristensen.barley.uniformity.Rd
--- agridat-1.17/man/kristensen.barley.uniformity.Rd	2020-07-04 20:59:46.000000000 +0000
+++ agridat-1.18/man/kristensen.barley.uniformity.Rd	2020-12-11 20:50:07.000000000 +0000
@@ -41,7 +41,7 @@
   Anlaeg og Opgoerelse af Markforsoeg.
   Tidsskrift for landbrugets planteavl, Vol 31, 464-494.
   Fig 1, pg. 467.
-  http://dca.au.dk/publikationer/historiske/planteavl/ 
+  https://dca.au.dk/publikationer/historiske/planteavl/ 
 }
 
 \references{
@@ -49,7 +49,7 @@
   Statistical Problems in Agricultural Experimentation.
   Supplement to the Journal of the Royal Statistical Society,
   Vol. 2, No. 2 (1935), pp. 107-180.
-  http://doi.org/10.2307/2983637
+  https://doi.org/10.2307/2983637
 }
 
 \examples{
diff -Nru agridat-1.17/man/lambert.soiltemp.Rd agridat-1.18/man/lambert.soiltemp.Rd
--- agridat-1.17/man/lambert.soiltemp.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/lambert.soiltemp.Rd	2020-12-11 20:57:06.000000000 +0000
@@ -39,9 +39,9 @@
 
 \source{
   Johann Heinrich Lambert (1779), \emph{Pyrometrie}.  Page 358.
-  http://books.google.com/books?id=G5I_AAAAcAAJ&pg=PA358
+  https://books.google.com/books?id=G5I_AAAAcAAJ&pg=PA358
   
-  Graph: http://www.fisme.science.uu.nl/wiskrant/artikelen/hist_grafieken/begin/images/pyrometrie.gif
+  Graph: https://www.fisme.science.uu.nl/wiskrant/artikelen/hist_grafieken/begin/images/pyrometrie.gif
 }
 
 \examples{
diff -Nru agridat-1.17/man/lavoranti.eucalyptus.Rd agridat-1.18/man/lavoranti.eucalyptus.Rd
--- agridat-1.17/man/lavoranti.eucalyptus.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/lavoranti.eucalyptus.Rd	2020-12-11 20:50:08.000000000 +0000
@@ -48,7 +48,7 @@
   C.T. and Krzanowski, W.J. (2010). An alternative methodology for
   imputing missing data in trials with genotype-by-environment
   interaction, \emph{Biometrical Letters}, 47, 1-14.
-  http://doi.org/10.2478/bile-2014-0006
+  https://doi.org/10.2478/bile-2014-0006
 }
 
 \examples{
diff -Nru agridat-1.17/man/lee.potatoblight.Rd agridat-1.18/man/lee.potatoblight.Rd
--- agridat-1.17/man/lee.potatoblight.Rd	2020-07-04 21:01:26.000000000 +0000
+++ agridat-1.18/man/lee.potatoblight.Rd	2020-12-11 20:57:07.000000000 +0000
@@ -46,7 +46,7 @@
 which after several years of testing is changed to a registered
 commercial variety name.
 For this R package, the Potato Pedigree Database,
-http://www.plantbreeding.wur.nl/potatopedigree/reverselookup.php,
+https://www.plantbreeding.wur.nl/potatopedigree/reverselookup.php,
 was used to change breeder codes (in early testing) to the variety names
 used in later testing.  For example, among the changes made were the
 following:
@@ -71,7 +71,7 @@
   https://researchspace.auckland.ac.nz/handle/2292/5240.
   
   Licensed via Open Database License 1.0. (allows sub-licensing).
-  See: http://opendatacommons.org/licenses/dbcl/1.0/
+  See: https://opendatacommons.org/licenses/dbcl/1.0/
 }
 \source{
   Lee, Arier Chi-Lun (2009).
@@ -88,7 +88,7 @@
 dat <- lee.potatoblight
 
 # Common cultivars across years.
-# Based on code from here: http://stackoverflow.com/questions/20709808
+# Based on code from here: https://stackoverflow.com/questions/20709808
 gg <- tapply(dat$gen, dat$year, function(x) as.character(unique(x)))
 tab <- outer(1:11, 1:11,
              Vectorize(function(a, b) length(Reduce(intersect, gg[c(a, b)]))))
diff -Nru agridat-1.17/man/lehner.soybeanmold.Rd agridat-1.18/man/lehner.soybeanmold.Rd
--- agridat-1.17/man/lehner.soybeanmold.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/lehner.soybeanmold.Rd	2020-12-11 20:57:07.000000000 +0000
@@ -26,7 +26,7 @@
   Data are the mean of 4 reps.
 
   Original source (Portuguese)
-  \url{http://ainfo.cnptia.embrapa.br/digital/bitstream/item/101371/1/Ensaios-cooperativos-de-controle-quimico-de-mofo-branco-na-cultura-da-soja-safras-2009-a-2012.pdf}
+  \url{https://ainfo.cnptia.embrapa.br/digital/bitstream/item/101371/1/Ensaios-cooperativos-de-controle-quimico-de-mofo-branco-na-cultura-da-soja-safras-2009-a-2012.pdf}
 
   Data included here via GPL3 license.
 }
diff -Nru agridat-1.17/man/lillemo.wheat.Rd agridat-1.18/man/lillemo.wheat.Rd
--- agridat-1.17/man/lillemo.wheat.Rd	2019-11-22 17:08:40.000000000 +0000
+++ agridat-1.18/man/lillemo.wheat.Rd	2020-12-11 20:50:26.000000000 +0000
@@ -53,7 +53,7 @@
   Identification of Stable Resistance to Powdery Mildew in Wheat Based
   on Parametric and Nonparametric Methods
   Crop Sci. 50:478-485.
-  http://doi.org/10.2135/cropsci2009.03.0116
+  https://doi.org/10.2135/cropsci2009.03.0116
 }
 \references{
   None.
diff -Nru agridat-1.17/man/li.millet.uniformity.Rd agridat-1.18/man/li.millet.uniformity.Rd
--- agridat-1.17/man/li.millet.uniformity.Rd	2020-07-04 21:02:49.000000000 +0000
+++ agridat-1.18/man/li.millet.uniformity.Rd	2020-12-11 20:50:28.000000000 +0000
@@ -34,7 +34,7 @@
   Li, HW and Meng, CJ and Liu, TN. 1936.
   Field Results in a Millet Breeding Experiment.
   Agronomy Journal, 28, 1-15.  Table 1.
-  http://doi.org/10.2134/agronj1936.00021962002800010001x
+  https://doi.org/10.2134/agronj1936.00021962002800010001x
 }
 
 \examples{
diff -Nru agridat-1.17/man/lin.superiority.Rd agridat-1.18/man/lin.superiority.Rd
--- agridat-1.17/man/lin.superiority.Rd	2020-07-05 17:03:47.000000000 +0000
+++ agridat-1.18/man/lin.superiority.Rd	2020-12-11 20:57:08.000000000 +0000
@@ -28,20 +28,20 @@
   C. S. Lin, M. R. Binns (1985).
   Procedural approach for assessing cultivar-location data: Pairwise genotype-environment interactions of test cultivars with checks
   \emph{Canadian Journal of Plant Science}, 1985, 65(4): 1065-1071. Table 1.
-  http://doi.org/10.4141/cjps85-136
+  https://doi.org/10.4141/cjps85-136
 }
 
 \references{
   C. S. Lin, M. R. Binns (1988).
   A Superiority Measure Of Cultivar Performance For Cultivar x Location Data.
   \emph{Canadian Journal of Plant Science}, 68, 193-198.
-  http://doi.org/10.4141/cjps88-018
+  https://doi.org/10.4141/cjps88-018
 
   Mohammed Ali Hussein, Asmund Bjornstad, and A. H. Aastveit (2000).
   SASG x ESTAB: A SAS Program for Computing Genotype x Environment
   Stability Statistics.
   \emph{Agronomy Journal}, 92; 454-459.
-  http://doi.org/10.2134/agronj2000.923454x
+  https://doi.org/10.2134/agronj2000.923454x
 }
 \examples{
 \dontrun{
diff -Nru agridat-1.17/man/lin.unbalanced.Rd agridat-1.18/man/lin.unbalanced.Rd
--- agridat-1.17/man/lin.unbalanced.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/lin.unbalanced.Rd	2020-12-11 20:57:09.000000000 +0000
@@ -30,7 +30,7 @@
   A Method for Assessing Regional Trial Data When The Test Cultivars Are
   Unbalanced With Respect to Locations.
   \emph{Canadian Journal of Plant Science}, 68(4): 1103-1110.
-  http://doi.org/10.4141/cjps88-130
+  https://doi.org/10.4141/cjps88-130
 }
 
 \references{
diff -Nru agridat-1.17/man/lonnquist.maize.Rd agridat-1.18/man/lonnquist.maize.Rd
--- agridat-1.17/man/lonnquist.maize.Rd	2019-12-06 00:02:52.000000000 +0000
+++ agridat-1.18/man/lonnquist.maize.Rd	2020-12-11 20:57:10.000000000 +0000
@@ -18,9 +18,9 @@
 }
 \details{
 
-  Twelve hybrids were selfed/crossed in a half-diallel design planted in
-  3 reps at 2 locations in 2 years.  The data here are means adjusted
-  for block effects.
+  Twelve hybrids were selfed/crossed in a half-diallel design.
+  The data here are means adjusted for block effects.
+  Original experiment was 3 reps at 2 locations in 2 years.  
 
 }
 
@@ -28,19 +28,19 @@
   J. H. Lonnquist, C. O. Gardner. (1961)
   Heterosis in Intervarietal Crosses in Maize and Its Implication in
   Breeding Procedures.
-  \emph{Crop Science}, 1, 179-183.  Table 1.
+  Crop Science, 1, 179-183.  Table 1.
 }
 
 \references{
   Mohring, Melchinger, Piepho. (2011).
   REML-Based Diallel Analysis.
   \emph{Crop Science}, 51, 470-478.
-  http://doi.org/10.2135/cropsci2010.05.0272
+  https://doi.org/10.2135/cropsci2010.05.0272
 
   C. O. Gardner and S. A. Eberhart. 1966.
   Analysis and Interpretation of the Variety Cross Diallel and Related
   Populations. \emph{Biometrics}, 22, 439-452.
-  http://doi.org/10.2307/2528181
+  https://doi.org/10.2307/2528181
 }
 
 \examples{
@@ -73,7 +73,7 @@
 
   # ----------
 
-  # asreml3 & asreml4
+  # asreml4
   # Mohring 2011 used 6 varieties to calculate GCA & SCA
   # Matches Table 3, column 2
   d2 <- subset(dat, is.element(p1, c("M","H","G","B","K","K2")) &
diff -Nru agridat-1.17/man/love.cotton.uniformity.Rd agridat-1.18/man/love.cotton.uniformity.Rd
--- agridat-1.17/man/love.cotton.uniformity.Rd	2020-07-04 21:03:54.000000000 +0000
+++ agridat-1.18/man/love.cotton.uniformity.Rd	2020-12-11 20:57:11.000000000 +0000
@@ -25,7 +25,7 @@
 
   Possibly more information would be in the collected papers of Harry
   Love at Cornell:
-  http://rmc.library.cornell.edu/EAD/htmldocs/RMA00890.html
+  https://rmc.library.cornell.edu/EAD/htmldocs/RMA00890.html
   Cotton - Plot Technic Study 1930-1932. Box 3, Folder 34
 }
 
diff -Nru agridat-1.17/man/lucas.switchback.Rd agridat-1.18/man/lucas.switchback.Rd
--- agridat-1.17/man/lucas.switchback.Rd	2019-11-22 17:26:11.000000000 +0000
+++ agridat-1.18/man/lucas.switchback.Rd	2020-12-11 20:57:12.000000000 +0000
@@ -32,14 +32,14 @@
   Lucas, HL. 1956.
   Switchback trials for more than two treatments.
   Journal of Dairy Science, 39, 146-154.
-  http://doi.org/10.3168/jds.S0022-0302(56)94721-X
+  https://doi.org/10.3168/jds.S0022-0302(56)94721-X
 }
 
 \references{
   Sanders, WL and Gaynor, PJ. 1987.
   Analysis of Switchback Data Using Statistical Analysis System.
   Journal of Dairy Science, 70, 2186-2191.
-  http://doi.org/10.3168/jds.S0022-0302(87)80273-4
+  https://doi.org/10.3168/jds.S0022-0302(87)80273-4
 }
 
 \examples{
diff -Nru agridat-1.17/man/lyon.potato.uniformity.Rd agridat-1.18/man/lyon.potato.uniformity.Rd
--- agridat-1.17/man/lyon.potato.uniformity.Rd	2020-07-04 21:04:11.000000000 +0000
+++ agridat-1.18/man/lyon.potato.uniformity.Rd	2020-12-11 20:57:13.000000000 +0000
@@ -38,7 +38,7 @@
   Lyon, T.L. (1911).
   Some experiments to estimate errors in field plat tests.
   Proc. Amer. Soc. Agron, 3, 89-114. Table III.
-  http://doi.org/10.2134/agronj1911.00021962000300010016x
+  https://doi.org/10.2134/agronj1911.00021962000300010016x
 }
 \references{
   None.
diff -Nru agridat-1.17/man/masood.rice.uniformity.Rd agridat-1.18/man/masood.rice.uniformity.Rd
--- agridat-1.17/man/masood.rice.uniformity.Rd	2020-07-04 21:05:17.000000000 +0000
+++ agridat-1.18/man/masood.rice.uniformity.Rd	2020-12-11 20:57:15.000000000 +0000
@@ -46,7 +46,7 @@
   Estimation of optimum field plot size and shape in paddy yield trial.
   American-Eurasian Journal of Scientific Research, 7, 264-269.
   Table 1.
-  http://doi.org/10.5829/idosi.aejsr.2012.7.6.1926
+  https://doi.org/10.5829/idosi.aejsr.2012.7.6.1926
 }
 \examples{
 \dontrun{
diff -Nru agridat-1.17/man/mcclelland.corn.uniformity.Rd agridat-1.18/man/mcclelland.corn.uniformity.Rd
--- agridat-1.17/man/mcclelland.corn.uniformity.Rd	2020-07-04 21:05:36.000000000 +0000
+++ agridat-1.18/man/mcclelland.corn.uniformity.Rd	2020-12-11 20:57:16.000000000 +0000
@@ -34,7 +34,7 @@
   McClelland, Chalmer Kirk (1926).
   Some determinations of plat variability.
   Agronomy Journal, 18, 819-823.
-  http://doi.org/10.2134/agronj1926.00021962001800090009x
+  https://doi.org/10.2134/agronj1926.00021962001800090009x
 }
 \references{
   None
diff -Nru agridat-1.17/man/mcconway.turnip.Rd agridat-1.18/man/mcconway.turnip.Rd
--- agridat-1.17/man/mcconway.turnip.Rd	2019-11-22 17:09:42.000000000 +0000
+++ agridat-1.18/man/mcconway.turnip.Rd	2020-09-11 19:02:14.000000000 +0000
@@ -33,8 +33,8 @@
 }
 
 \source{
-  Statistical Modelling Using Genstat, K. J. McConway, M. C. Jones,
-  P. C. Taylor.
+  K. J. McConway, M. C. Jones, P. C. Taylor.
+  Statistical Modelling Using Genstat.
 }
 
 \references{
@@ -51,6 +51,7 @@
   Data transformation in statistical analysis of field trials
   with changing treatment variance.
   Agronomy Journal, 101, 865--869.
+  https://doi.org/10.2134/agronj2008.0226x
 }
 
 \examples{
diff -Nru agridat-1.17/man/mcleod.barley.Rd agridat-1.18/man/mcleod.barley.Rd
--- agridat-1.17/man/mcleod.barley.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/mcleod.barley.Rd	2020-12-11 20:57:17.000000000 +0000
@@ -34,7 +34,7 @@
   C. C. McLeod (1982).
   Effects of rates of seeding on barley sown for grain.
   \emph{New Zealand Journal of Experimental Agriculture}, 10, 133-136.
-  http://doi.org/10.1080/03015521.1982.10427857.
+  https://doi.org/10.1080/03015521.1982.10427857.
 }
 
 \references{
diff -Nru agridat-1.17/man/mead.cauliflower.Rd agridat-1.18/man/mead.cauliflower.Rd
--- agridat-1.17/man/mead.cauliflower.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/mead.cauliflower.Rd	2020-12-11 18:09:41.000000000 +0000
@@ -33,7 +33,7 @@
 }
 \references{
   Mick O'Neill. Regression & Generalized Linear (Mixed) Models.
-  STatistical Advisory & Training Service Pty Ltd.
+  Statistical Advisory & Training Service Pty Ltd.
 }
 
 \examples{
diff -Nru agridat-1.17/man/mead.cowpeamaize.Rd agridat-1.18/man/mead.cowpeamaize.Rd
--- agridat-1.17/man/mead.cowpeamaize.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/mead.cowpeamaize.Rd	2020-12-11 20:57:18.000000000 +0000
@@ -26,7 +26,7 @@
 \source{
   Roger Mead. 1990. A Review of Methodology For The Analysis of Intercropping
   Experiments.   Training Working Document No. 6.  CIMMYT.
-  http://repository.cimmyt.org/xmlui/handle/10883/868
+  https://repository.cimmyt.org/xmlui/handle/10883/868
 }
 \references{
   Roger Mead, Robert N Curnow, Anne M Hasted. 2002.
diff -Nru agridat-1.17/man/mercer.mangold.uniformity.Rd agridat-1.18/man/mercer.mangold.uniformity.Rd
--- agridat-1.17/man/mercer.mangold.uniformity.Rd	2020-07-04 21:06:20.000000000 +0000
+++ agridat-1.18/man/mercer.mangold.uniformity.Rd	2020-12-11 20:57:19.000000000 +0000
@@ -34,7 +34,7 @@
 \source{
   Mercer, WB and Hall, AD, 1911. The experimental error of field trials
   The Journal of Agricultural Science, 4, 107-132. Table 1.
-  http://doi.org/10.1017/S002185960000160X
+  https://doi.org/10.1017/S002185960000160X
 }
 
 \references{
@@ -42,7 +42,7 @@
   Evidence for conformal invariance of crop yields,
   Proceedings of the Royal Society A: Mathematical, Physical and
     Engineering Science, 462, 2119--2143.
-  http://doi.org/10.1098/rspa.2006.1667
+  https://doi.org/10.1098/rspa.2006.1667
 }
 
 \examples{
diff -Nru agridat-1.17/man/mercer.wheat.uniformity.Rd agridat-1.18/man/mercer.wheat.uniformity.Rd
--- agridat-1.17/man/mercer.wheat.uniformity.Rd	2020-07-04 21:06:35.000000000 +0000
+++ agridat-1.18/man/mercer.wheat.uniformity.Rd	2020-12-11 20:57:21.000000000 +0000
@@ -38,7 +38,7 @@
   Mercer, WB and Hall, AD, (1911).
   The experimental error of field trials
   The Journal of Agricultural Science, 4, 107-132. Table 5.
-  http://doi.org/10.1017/S002185960000160X
+  https://doi.org/10.1017/S002185960000160X
 }
 
 \references{
@@ -47,7 +47,7 @@
   Evidence for conformal invariance of crop yields,
   \emph{Proceedings of the Royal Society A: Mathematical, Physical and
     Engineering Science}, 462, 2119--2143.
-  http://doi.org/10.1098/rspa.2006.1667
+  https://doi.org/10.1098/rspa.2006.1667
 
   D. G. Rossiter (2014). Tutorial: Using the R Environment for Statistical Computing
   An example with the Mercer & Hall wheat yield dataset.
@@ -55,7 +55,7 @@
   G. A. Baker (1941).
   Fundamental Distribution of Errors for Agricultural Field Trials.
   \emph{National Mathematics Magazine}, 16, 7-19.
-  http://doi.org/10.2307/3028105
+  https://doi.org/10.2307/3028105
   
   The 'spdep' package includes the grain yields
   (only) and spatial positions of plot centres in its example dataset
diff -Nru agridat-1.17/man/miguez.biomass.Rd agridat-1.18/man/miguez.biomass.Rd
--- agridat-1.17/man/miguez.biomass.Rd	2019-12-07 17:06:25.000000000 +0000
+++ agridat-1.18/man/miguez.biomass.Rd	2020-12-11 20:57:22.000000000 +0000
@@ -33,16 +33,16 @@
 }
 \source{
   Fernando E. Miguez. R package nlraa.
-  https://r-forge.r-project.org/projects/nlraa/
+  https://github.com/femiguez/nlraa
 }
 \references{
   Sotirios V. Archontoulis and Fernando E. Miguez (2013).
   Nonlinear Regression Models and Applications in Agricultural Research.
   \emph{Agron. Journal}, 105:1-13.
-  http://doi.org/10.2134/agronj2012.0506
+  https://doi.org/10.2134/agronj2012.0506
 
   Hamze Dokoohaki.
-  http://www.rpubs.com/Para2x/100378
+  https://www.rpubs.com/Para2x/100378
   https://rstudio-pubs-static.s3.amazonaws.com/100440_26eb9108524c4cc99071b0db8e648e7d.html
 }
 \examples{
diff -Nru agridat-1.17/man/minnesota.barley.weather.Rd agridat-1.18/man/minnesota.barley.weather.Rd
--- agridat-1.17/man/minnesota.barley.weather.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/minnesota.barley.weather.Rd	2020-12-11 20:57:23.000000000 +0000
@@ -46,13 +46,13 @@
   No data is available for Duluth in Dec, 1931.
 }
 \source{
-  National Climate Data Center, http://www.ncdc.noaa.gov/.
+  National Climate Data Center, https://www.ncdc.noaa.gov/.
 }
 
 \references{
   Kevin Wright. 2013.  Revisiting Immer's Barley Data.
   \emph{The American Statistitician}, 67, 129-133.
-  http://doi.org/10.1080/00031305.2013.801783
+  https://doi.org/10.1080/00031305.2013.801783
 }
 
 \examples{
diff -Nru agridat-1.17/man/minnesota.barley.yield.Rd agridat-1.18/man/minnesota.barley.yield.Rd
--- agridat-1.17/man/minnesota.barley.yield.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/minnesota.barley.yield.Rd	2020-12-11 20:57:24.000000000 +0000
@@ -2,7 +2,7 @@
 \alias{minnesota.barley.yield}
 \docType{data}
 \title{
-  Multi-environment trial of barley in Minnesoty at 6 sites in 1927-1936.
+  Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936.
 }
 \description{
   This is an expanded version of the barley data that is often
@@ -92,12 +92,12 @@
   H V Harlan and P R Cowan and Lucille Reinbach. (1935).
   \emph{Yields of barley varieties in the United States and Canada, 1927-1931}.
   United States Dept of Agriculture.
-  http://naldc.nal.usda.gov/download/CAT86200440/PDF
+  https://naldc.nal.usda.gov/download/CAT86200440/PDF
 
   Gustav A. Wiebe, Philip Russell Cowan, Lucille Reinbach-Welch. (1940).
   \emph{Yields of barley varieties in the United States and Canada, 1932-36}.
   United States Dept of Agriculture.
-  http://books.google.com/books?id=OUfxLocnpKkC&pg=PA19
+  https://books.google.com/books?id=OUfxLocnpKkC&pg=PA19
 }
 
 \references{
@@ -110,7 +110,7 @@
 
   Kevin Wright. (2013).  Revisiting Immer's Barley Data.
   \emph{The American Statistitician}, 67, 129-133.
-  http://doi.org/10.1080/00031305.2013.801783
+  https://doi.org/10.1080/00031305.2013.801783
 }
 \examples{
 
diff -Nru agridat-1.17/man/montgomery.wheat.uniformity.Rd agridat-1.18/man/montgomery.wheat.uniformity.Rd
--- agridat-1.17/man/montgomery.wheat.uniformity.Rd	2020-07-04 21:08:24.000000000 +0000
+++ agridat-1.18/man/montgomery.wheat.uniformity.Rd	2020-12-11 20:57:25.000000000 +0000
@@ -60,7 +60,7 @@
   Variation in Nitrogen and Yield.
   U.S. Dept of Agriculture, Bureau of Plant Industry, Bulletin 269.
   Figure 10, page 37.
-  http://doi.org/10.5962/bhl.title.43602
+  https://doi.org/10.5962/bhl.title.43602
 }
 
 \references{
diff -Nru agridat-1.17/man/nagai.strawberry.uniformity.Rd agridat-1.18/man/nagai.strawberry.uniformity.Rd
--- agridat-1.17/man/nagai.strawberry.uniformity.Rd	2020-07-04 21:10:18.000000000 +0000
+++ agridat-1.18/man/nagai.strawberry.uniformity.Rd	2020-12-11 20:57:26.000000000 +0000
@@ -31,7 +31,7 @@
   Violeta Nagai (1978).
   Tamanho da parcela e numero de repeticoes em experimentos com morangueiro (Plot size and number of repetitions in experiments with strawberry).
   Bragantia, 37, 71-81. Table 2, page 75.
-  http://dx.doi.org/10.1590/S0006-87051978000100009
+  https://dx.doi.org/10.1590/S0006-87051978000100009
 }
 \references{
   None
diff -Nru agridat-1.17/man/nair.turmeric.uniformity.Rd agridat-1.18/man/nair.turmeric.uniformity.Rd
--- agridat-1.17/man/nair.turmeric.uniformity.Rd	1970-01-01 00:00:00.000000000 +0000
+++ agridat-1.18/man/nair.turmeric.uniformity.Rd	2020-12-11 20:57:28.000000000 +0000
@@ -0,0 +1,65 @@
+\name{nair.turmeric.uniformity}
+\alias{nair.turmeric.uniformity}
+\docType{data}
+\title{
+  Uniformity trial of turmeric.
+}
+\description{
+  Uniformity trial of turmeric in India, 1984.
+}
+\usage{data("nair.turmeric.uniformity")}
+\format{
+  A data frame with 864 observations on the following 3 variables.
+  \describe{
+    \item{\code{row}}{row ordinate}
+    \item{\code{col}}{column ordinate}
+    \item{\code{yield}}{yield per plot, kg}
+  }
+}
+\details{
+
+  An experiment conducted at the College of Horticulture, Vellanikkara,
+  India, in 1984. The crop was grown in raised beds.
+
+  The gross experimental area was 74.2 m long x 15.2 m wide. Small
+  elevated beds 0.6 m x 1.5 m were raised providing channels of 0.4 m
+  around each bed. One row of beds all around the experiment was
+  discarded to eliminate border effects. After discarding the borders,
+  there were 432 beds in the experiment. At the time of harvest, each
+  bed was divided into equal plots of size .6 m x .75 m, and the yield
+  from each plot was recorded.
+
+  
+  Field map on page 64 of Nair.
+
+  Field length: 14 plots * .6 m + 13 alleys * .4 m = 13.6 m
+
+  Field width: 72 plots * .75 m + 35 alleys * .4 m = 68 m
+
+  Data found in the appendix.
+  
+}
+\source{
+  Nair, B. Gopakumaran  (1984).
+  Optimum plot size for field experiments on turmeric.
+  Thesis, Kerala Agriculture University.
+  https://krishikosh.egranth.ac.in/handle/1/5810147397
+}
+\references{
+  None.
+}
+\examples{
+\dontrun{
+  
+  library(agridat)
+  data(nair.turmeric.uniformity)
+  dat <- gopakumaran.turmeric.uniformity
+  
+  libs(desplot)
+  desplot(dat, yield ~ col*row,
+          flip=TRUE, aspect=13.6/68,
+          main="nair.turmeric.uniformity")
+
+}
+}
+\keyword{datasets}
diff -Nru agridat-1.17/man/nass.corn.Rd agridat-1.18/man/nass.corn.Rd
--- agridat-1.17/man/nass.corn.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/nass.corn.Rd	2020-12-11 20:57:29.000000000 +0000
@@ -47,7 +47,7 @@
 \source{
   United States Department of Agriculture,
   National Agricultural Statistics Service.
-  http://quickstats.nass.usda.gov/
+  https://quickstats.nass.usda.gov/
 }
 
 \examples{
diff -Nru agridat-1.17/man/nebraska.farmincome.Rd agridat-1.18/man/nebraska.farmincome.Rd
--- agridat-1.17/man/nebraska.farmincome.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/nebraska.farmincome.Rd	2020-12-11 20:57:30.000000000 +0000
@@ -34,7 +34,7 @@
 }
 \source{
   U.S. Department of Agriculture-National Agriculture Statistics
-  Service. http://censtats.census.gov/usa/usa.shtml
+  Service. https://censtats.census.gov/usa/usa.shtml
 }
 \examples{
 \dontrun{
diff -Nru agridat-1.17/man/nonnecke.peas.uniformity.Rd agridat-1.18/man/nonnecke.peas.uniformity.Rd
--- agridat-1.17/man/nonnecke.peas.uniformity.Rd	2020-07-04 21:10:47.000000000 +0000
+++ agridat-1.18/man/nonnecke.peas.uniformity.Rd	2020-12-11 20:57:31.000000000 +0000
@@ -41,14 +41,14 @@
   Ib Libner Nonnecke. 1958.
   Yield variability of sweet corn and canning peas as affected by plot size and shape.
   Thesis at Oregon State College.
-  http://hdl.handle.net/1957/23367
+  https://hdl.handle.net/1957/23367
 }
 
 \references{
   I. L. Nonnecke, 1960.
   The precision of field experiments with vegetable crops as influenced by plot and block size and shape: II. Canning peas.
   Canadian Journal of Plant Science, 40(2): 396-404.
-  http://doi.org/10.4141/cjps60-053
+  https://doi.org/10.4141/cjps60-053
 }
 
 \examples{
diff -Nru agridat-1.17/man/nonnecke.sweetcorn.uniformity.Rd agridat-1.18/man/nonnecke.sweetcorn.uniformity.Rd
--- agridat-1.17/man/nonnecke.sweetcorn.uniformity.Rd	2020-07-04 21:10:59.000000000 +0000
+++ agridat-1.18/man/nonnecke.sweetcorn.uniformity.Rd	2020-12-11 20:57:32.000000000 +0000
@@ -38,14 +38,14 @@
   Ib Libner Nonnecke. 1958.
   Yield variability of sweet corn and canning peas as affected by plot size and shape.
   Thesis at Oregon State College.
-  http://hdl.handle.net/1957/23367
+  https://hdl.handle.net/1957/23367
 }
 
 \references{
   I. L. Nonnecke, 1959.
   The precision of field experiments with vegetable crops as influenced by plot and block size and shape: I. Sweet corn.
   Canadian Journal of Plant Science, 39(4): 443-457. Tables 1-7.
-  http://doi.org/10.4141/cjps59-061
+  https://doi.org/10.4141/cjps59-061
 }
 
 \examples{
diff -Nru agridat-1.17/man/obsi.potato.uniformity.Rd agridat-1.18/man/obsi.potato.uniformity.Rd
--- agridat-1.17/man/obsi.potato.uniformity.Rd	2020-07-04 21:11:18.000000000 +0000
+++ agridat-1.18/man/obsi.potato.uniformity.Rd	2020-12-11 20:57:33.000000000 +0000
@@ -34,7 +34,7 @@
   Dechassa Obsi. 2008.
   Application of Spatial Modeling to the Study of Soil Fertility Pattern.
   MS Thesis, Addis Ababa University. Page 122-125.
-  http://etd.aau.edu.et/handle/123456789/3221
+  https://etd.aau.edu.et/handle/123456789/3221
 }
 \references{
   None.
diff -Nru agridat-1.17/man/odland.soy.uniformity.Rd agridat-1.18/man/odland.soy.uniformity.Rd
--- agridat-1.17/man/odland.soy.uniformity.Rd	2020-07-04 21:11:42.000000000 +0000
+++ agridat-1.18/man/odland.soy.uniformity.Rd	2020-12-11 20:57:34.000000000 +0000
@@ -50,7 +50,7 @@
   Size of Plat and Number of Replications in Field Experiments with
   Soybeans.
   Agronomy Journal, 20, 93--108.
-  http://doi.org/10.2134/agronj1928.00021962002000020002x
+  https://doi.org/10.2134/agronj1928.00021962002000020002x
 }
 
 \examples{
diff -Nru agridat-1.17/man/onofri.winterwheat.Rd agridat-1.18/man/onofri.winterwheat.Rd
--- agridat-1.17/man/onofri.winterwheat.Rd	2019-11-22 17:10:29.000000000 +0000
+++ agridat-1.18/man/onofri.winterwheat.Rd	2020-12-11 20:57:35.000000000 +0000
@@ -22,7 +22,7 @@
   Yield of 8 durum winter wheat varieties across 7 years with 3 reps.
 
   Downloaded electronic version from here Nov 2015:
-  http://www.casaonofri.it/Biometry/index.html
+  https://www.casaonofri.it/Biometry/index.html
 
   Used with permission of Andrea Onofri.
 }
@@ -36,10 +36,10 @@
 \references{
   F. Mendiburu.
   AMMI.
-  http://tarwi.lamolina.edu.pe/~fmendiburu/AMMI.htm
+  https://tarwi.lamolina.edu.pe/~fmendiburu/AMMI.htm
 
   A. Onofri.
-  http://accounts.unipg.it/~onofri/RTutorial/CaseStudies/WinterWheat.htm
+  https://accounts.unipg.it/~onofri/RTutorial/CaseStudies/WinterWheat.htm
 }
 
 \examples{
diff -Nru agridat-1.17/man/ortiz.tomato.Rd agridat-1.18/man/ortiz.tomato.Rd
--- agridat-1.17/man/ortiz.tomato.Rd	2019-11-22 17:10:50.000000000 +0000
+++ agridat-1.18/man/ortiz.tomato.Rd	2020-12-11 20:57:36.000000000 +0000
@@ -83,7 +83,7 @@
   Studying the Effect of Environmental Variables On the Genotype x
   Environment Interaction of Tomato.
   Euphytica, 153, 119--134.
-  http://doi.org/10.1007/s10681-006-9248-7
+  https://doi.org/10.1007/s10681-006-9248-7
 }
 
 \examples{
diff -Nru agridat-1.17/man/pacheco.soybean.Rd agridat-1.18/man/pacheco.soybean.Rd
--- agridat-1.17/man/pacheco.soybean.Rd	2019-11-22 17:11:21.000000000 +0000
+++ agridat-1.18/man/pacheco.soybean.Rd	2020-12-11 20:57:37.000000000 +0000
@@ -25,7 +25,7 @@
   R M Pacheco, J B Duarte, R Vencovsky, J B Pinheiro, A B Oliveira,
   (2005). Use of supplementary genotypes in AMMI analysis.
   Theor Appl Genet, 110, 812-818.
-  http://doi.org/10.1007/s00122-004-1822-6
+  https://doi.org/10.1007/s00122-004-1822-6
 }
 
 \examples{
diff -Nru agridat-1.17/man/paez.coffee.uniformity.Rd agridat-1.18/man/paez.coffee.uniformity.Rd
--- agridat-1.17/man/paez.coffee.uniformity.Rd	2020-07-04 21:17:13.000000000 +0000
+++ agridat-1.18/man/paez.coffee.uniformity.Rd	2020-12-11 20:57:37.000000000 +0000
@@ -36,7 +36,7 @@
   Gilberto Paez Bogarin (1962).
   Estudios sobre tamano y forma de parcela para ensayos en cafe.
   Instituto Interamericano de Ciencias Agricolas de la O.E.A. Centro Tropical de Investigacion y Ensenanza para Graduados. Costa Rica.
-  http://hdl.handle.net/11554/1892
+  https://hdl.handle.net/11554/1892
 }
 \references{
   None
diff -Nru agridat-1.17/man/parker.orange.uniformity.Rd agridat-1.18/man/parker.orange.uniformity.Rd
--- agridat-1.17/man/parker.orange.uniformity.Rd	2020-07-04 21:18:13.000000000 +0000
+++ agridat-1.18/man/parker.orange.uniformity.Rd	2020-12-11 20:57:39.000000000 +0000
@@ -57,7 +57,7 @@
   E. R. Parker & L. D. Batchelor. (1932).
   Variation in the Yields of Fruit Trees in Relation to the Planning of Future Experiments.
   Hilgardia, 7(2), 81-161. Tables 3-9.
-  http://doi.org/10.3733/hilg.v07n02p081
+  https://doi.org/10.3733/hilg.v07n02p081
 }
 
 \references{
diff -Nru agridat-1.17/man/patterson.switchback.Rd agridat-1.18/man/patterson.switchback.Rd
--- agridat-1.17/man/patterson.switchback.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/patterson.switchback.Rd	2020-12-11 20:57:40.000000000 +0000
@@ -40,7 +40,7 @@
   Statistical design and analysis of dairy nutrition experiments to
   improve detection of milk response differences.
   \emph{Proceedings of the Conference on Applied Statistics in Agriculture}, 1989.
-  http://newprairiepress.org/agstatconference/1989/proceedings/7/
+  https://newprairiepress.org/agstatconference/1989/proceedings/7/
 }
 
 \examples{
diff -Nru agridat-1.17/man/pearl.kernels.Rd agridat-1.18/man/pearl.kernels.Rd
--- agridat-1.17/man/pearl.kernels.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/pearl.kernels.Rd	2020-12-11 20:57:41.000000000 +0000
@@ -55,7 +55,7 @@
   Raymond Pearl, 1911.
   The Personal Equation In Breeding Experiments Involving Certain
   Characters of Maize, Biol. Bull., 21, 339-366.
-  http://www.biolbull.org/cgi/reprint/21/6/339.pdf
+  https://www.biolbull.org/cgi/reprint/21/6/339.pdf
 }
 
 \examples{
diff -Nru agridat-1.17/man/perry.springwheat.Rd agridat-1.18/man/perry.springwheat.Rd
--- agridat-1.17/man/perry.springwheat.Rd	2019-12-06 00:06:30.000000000 +0000
+++ agridat-1.18/man/perry.springwheat.Rd	2020-12-11 20:57:42.000000000 +0000
@@ -41,7 +41,7 @@
   Yield improvement and associated characteristics of some Australian
   spring wheat cultivars introduced between 1860 and 1982.
   Australian Journal of Agricultural Research, 40(3), 457--472.
-  http://www.publish.csiro.au/nid/43/issue/1237.htm
+  https://www.publish.csiro.au/nid/43/issue/1237.htm
 }
 
 \examples{
diff -Nru agridat-1.17/man/piepho.barley.uniformity.Rd agridat-1.18/man/piepho.barley.uniformity.Rd
--- agridat-1.17/man/piepho.barley.uniformity.Rd	2020-07-30 11:22:51.000000000 +0000
+++ agridat-1.18/man/piepho.barley.uniformity.Rd	2020-10-09 16:38:08.000000000 +0000
@@ -17,16 +17,15 @@
   }
 }
 \details{
-    Uniformity trial of barley at Ihinger Hof farm, conducted by the
+  Uniformity trial of barley at Ihinger Hof farm, conducted by the
   University of Hohenheim, Germany, in 2007.
   
-  Note: The paper by Piepho says
-  "The trial had 30 rows and 36 columns. Plot widths were 1.90 m along rows and 3.73 m along columns."
+  Note: The paper by Piepho says "The trial had 30 rows and 36
+  columns. Plot widths were 1.90 m along rows and 3.73 m along columns."
   However, the SAS code supplement to the paper, called
   "PBR_1654_sm_example1.sas", has row=1-36, col=1-30.  We cannot
   determine which dimension is "row" and which is "column", and
   therefore cannot determine the actual dimensions of the field.
-
 }
 \source{
   H. P. Piepho & E. R. Williams (2010).
@@ -46,17 +45,20 @@
           tick=TRUE, # aspect unknown
           main="piepho.barley.uniformity.csv")
 
-  # Piepho AR1xAR1 model (in random term, NOT residual)
-  libs(asreml)
+  libs(asreml,dplyr)
   dat <- mutate(dat, x=factor(col), y=factor(row))
   dat <- arrange(dat, x, y)
+  
+  # Piepho AR1xAR1 model (in random term, NOT residual)
   m1 <- asreml(data=dat,
                yield ~ 1, 
                random = ~ x + y + ar1(x):ar1(y), 
                residual = ~  units,
                na.action=na.method(x="keep") )
   m1 <- update(m1)
-  # Match Piepho: .9671, .9705 for col,row correlation
+  # Match Piepho table 3, footnote 4: .9671, .9705 for col,row correlation
+  # Note these parameters are basically at the boundary of the parameter
+  # space. Questionable fit.
   libs(lucid)
   lucid::vc(m1) 
 }
diff -Nru agridat-1.17/man/piepho.cocksfoot.Rd agridat-1.18/man/piepho.cocksfoot.Rd
--- agridat-1.17/man/piepho.cocksfoot.Rd	2019-12-08 17:45:43.000000000 +0000
+++ agridat-1.18/man/piepho.cocksfoot.Rd	2020-12-11 20:57:43.000000000 +0000
@@ -31,7 +31,7 @@
   Fitting a Regression Model for Genotype-by-Environment Data on Heading
   Dates in Grasses by Methods for Nonlinear Mixed Models.
   \emph{Biometrics}, 55, 1120-1128.
-  http://doi.org/10.1111/j.0006-341X.1999.01120.x  
+  https://doi.org/10.1111/j.0006-341X.1999.01120.x  
 }
 
 \examples{
diff -Nru agridat-1.17/man/polson.safflower.uniformity.Rd agridat-1.18/man/polson.safflower.uniformity.Rd
--- agridat-1.17/man/polson.safflower.uniformity.Rd	2020-07-04 21:18:32.000000000 +0000
+++ agridat-1.18/man/polson.safflower.uniformity.Rd	2020-12-11 20:57:44.000000000 +0000
@@ -36,7 +36,7 @@
   Estimation of Optimum Size, Shape, and Replicate Number of Safflower Plots for Yield Trials.
   Utah State University, All Graduate Theses and Dissertations, 2979.
   Table 6.
-  http://digitalcommons.usu.edu/etd/2979
+  https://digitalcommons.usu.edu/etd/2979
 }
 
 \references{
diff -Nru agridat-1.17/man/ratkowsky.onions.Rd agridat-1.18/man/ratkowsky.onions.Rd
--- agridat-1.17/man/ratkowsky.onions.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/ratkowsky.onions.Rd	2020-12-11 20:57:46.000000000 +0000
@@ -25,7 +25,7 @@
 \references{
   Ruppert, D., Wand, M.P. and Carroll, R.J. (2003).
   \emph{Semiparametric Regression}. Cambridge University Press.
-  http://stat.tamu.edu/~carroll/semiregbook/
+  https://stat.tamu.edu/~carroll/semiregbook/
 }
 \examples{
 \dontrun{
diff -Nru agridat-1.17/man/rothamsted.brussels.Rd agridat-1.18/man/rothamsted.brussels.Rd
--- agridat-1.17/man/rothamsted.brussels.Rd	2020-07-04 21:19:03.000000000 +0000
+++ agridat-1.18/man/rothamsted.brussels.Rd	2020-12-11 20:57:47.000000000 +0000
@@ -36,7 +36,7 @@
   Evidence for conformal invariance of crop yields,
   \emph{Proceedings of the Royal Society A: Mathematical, Physical and
     Engineering Science}, 462, 2119--2143.
-  http://doi.org/10.1098/rspa.2006.1667 
+  https://doi.org/10.1098/rspa.2006.1667 
 }
 
 \examples{
diff -Nru agridat-1.17/man/rothamsted.oats.Rd agridat-1.18/man/rothamsted.oats.Rd
--- agridat-1.17/man/rothamsted.oats.Rd	2020-07-04 21:19:24.000000000 +0000
+++ agridat-1.18/man/rothamsted.oats.Rd	2020-12-11 20:57:48.000000000 +0000
@@ -33,7 +33,7 @@
 }
 \source{
   Rothamsted Report 1925-26, p. 146.
-  http://www.era.rothamsted.ac.uk/eradoc/article/ResReport1925-26-138-155
+  https://www.era.rothamsted.ac.uk/eradoc/article/ResReport1925-26-138-155
   Electronic version of data supplied by David Clifford.
 }
 \references{
@@ -41,7 +41,7 @@
   Evidence for conformal invariance of crop yields,
   \emph{Proceedings of the Royal Society A: Mathematical, Physical and
     Engineering Science}, 462, 2119--2143.
-  http://doi.org/10.1098/rspa.2006.1667 
+  https://doi.org/10.1098/rspa.2006.1667 
 }
 \examples{
 \dontrun{
diff -Nru agridat-1.17/man/ryder.groundnut.Rd agridat-1.18/man/ryder.groundnut.Rd
--- agridat-1.17/man/ryder.groundnut.Rd	2020-07-04 21:19:51.000000000 +0000
+++ agridat-1.18/man/ryder.groundnut.Rd	2020-12-11 20:57:49.000000000 +0000
@@ -30,31 +30,30 @@
   K. Ryder (1981). Field plans: why the biometrician finds them useful,
   \emph{Experimental Agriculture}, 17, 243--256.
 
-  http://doi.org/10.1017/S0014479700011601
+  https://doi.org/10.1017/S0014479700011601
 }
 
 \examples{
 \dontrun{
   
-library(agridat)
-data(ryder.groundnut)
-dat <- ryder.groundnut
-
-# RCB model
-m1 <- lm(dry~block+gen,dat)
-dat$res1 <- resid(m1)
-
-# Table 3 of Ryder.  Scale up from kg/plot to kg/ha
-round(dat$res1 * 596.6,0)
+  library(agridat)
+  data(ryder.groundnut)
+  dat <- ryder.groundnut
+
+  # RCB model
+  m1 <- lm(dry~block+gen,dat)
+  dat$res1 <- resid(m1)
 
+  # Table 3 of Ryder.  Scale up from kg/plot to kg/ha
+  round(dat$res1 * 596.6,0)
+  
   # Visually.  Note largest positive/negative residuals are adjacent
   libs(desplot)
   desplot(dat, res1 ~ col + row,
           text=gen, # aspect unknown
           main="ryder.groundnut - residuals")
-
-
-if(0){
+  
+  
   libs(desplot)
   # Swap the dry yields for two plots and re-analyze
   dat[dat$block=="B3" & dat$gen=="A", "dry"] <- 2.8
@@ -64,7 +63,6 @@
   desplot(dat, res2 ~ col+row,
           # aspect unknown
           text=gen, main="ryder.groundnut")
-}
 
 }
 }
diff -Nru agridat-1.17/man/salmon.bunt.Rd agridat-1.18/man/salmon.bunt.Rd
--- agridat-1.17/man/salmon.bunt.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/salmon.bunt.Rd	2020-12-11 20:57:50.000000000 +0000
@@ -41,7 +41,7 @@
   S.C. Salmon, 1938.
   Generalized standard errors for evaluating bunt experiments with wheat.
   \emph{Agronomy Journal}, 30, 647--663.  Table 1.
-  http://doi.org/10.2134/agronj1938.00021962003000080003x
+  https://doi.org/10.2134/agronj1938.00021962003000080003x
 }
 
 \references{
diff -Nru agridat-1.17/man/sawyer.multi.uniformity.Rd agridat-1.18/man/sawyer.multi.uniformity.Rd
--- agridat-1.17/man/sawyer.multi.uniformity.Rd	2020-07-04 21:20:50.000000000 +0000
+++ agridat-1.18/man/sawyer.multi.uniformity.Rd	2020-12-11 20:57:51.000000000 +0000
@@ -59,11 +59,11 @@
 \source{
   Rothamsted Experimental Station, Report 1925-26.
   Lawes Agricultural Trust, p. 154-155.
-  http://www.era.rothamsted.ac.uk/eradoc/book/84
+  https://www.era.rothamsted.ac.uk/eradoc/book/84
 
   Rothamsted Experimental Station, Report 1927-1928.
   Lawes Agricultural Trust, p. 153.  
-  http://www.era.rothamsted.ac.uk/eradoc/article/ResReport1927-28-131-175
+  https://www.era.rothamsted.ac.uk/eradoc/article/ResReport1927-28-131-175
 }
 
 \references{
@@ -77,7 +77,7 @@
   Evidence for conformal invariance of crop yields,
   Proceedings of the Royal Society A: Mathematical, Physical and
     Engineering Science, 462, 2119--2143.
-  http://doi.org/10.1098/rspa.2006.1667
+  https://doi.org/10.1098/rspa.2006.1667
 
   Winifred A. Mackenzie. (1926)
   Note on a remarkable correlation between grain and straw, obtained at
diff -Nru agridat-1.17/man/senshu.rice.Rd agridat-1.18/man/senshu.rice.Rd
--- agridat-1.17/man/senshu.rice.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/senshu.rice.Rd	2020-12-11 20:57:52.000000000 +0000
@@ -30,7 +30,7 @@
   Seshu, D. V. and Cady, F. B. 1984.
   Response of rice to solar radiation and temperature estimated from international yield trials.
   \emph{Crop Science}, 24, 649-654.
-  http://doi.org/10.2135/cropsci1984.0011183X002400040006x
+  https://doi.org/10.2135/cropsci1984.0011183X002400040006x
 }
 
 \references{
@@ -39,7 +39,8 @@
 }
 
 \examples{
-
+\dontrun{
+  
 library(agridat)
 
 data(senshu.rice)
@@ -72,4 +73,5 @@
 lines(17:32, predict(m2, new=data.frame(mint=17:32)))
 abline(v=ci, col="blue")
 }
+}
 \keyword{datasets}
diff -Nru agridat-1.17/man/shafii.rapeseed.Rd agridat-1.18/man/shafii.rapeseed.Rd
--- agridat-1.17/man/shafii.rapeseed.Rd	2019-11-22 17:12:27.000000000 +0000
+++ agridat-1.18/man/shafii.rapeseed.Rd	2020-12-11 20:57:53.000000000 +0000
@@ -30,7 +30,7 @@
 
 
   Electronic version from:
-  http://www.uiweb.uidaho.edu/ag/statprog/ammi/yld.data
+  https://www.uiweb.uidaho.edu/ag/statprog/ammi/yld.data
 
   Used with permission of Bill Price.
 }
@@ -40,7 +40,7 @@
   Additive Main Effects and Multiplicative Interaction Model
   and Stability Estimates.
   JABES, 3, 335--345.
-  http://doi.org/10.2307/1400587
+  https://doi.org/10.2307/1400587
 }
 \references{
   Matthew Kramer (2018).
diff -Nru agridat-1.17/man/shafi.tomato.uniformity.Rd agridat-1.18/man/shafi.tomato.uniformity.Rd
--- agridat-1.17/man/shafi.tomato.uniformity.Rd	1970-01-01 00:00:00.000000000 +0000
+++ agridat-1.18/man/shafi.tomato.uniformity.Rd	2020-12-11 20:57:54.000000000 +0000
@@ -0,0 +1,51 @@
+\name{shafi.tomato.uniformity}
+\alias{shafi.tomato.uniformity}
+\docType{data}
+\title{
+  Uniformity trial of tomato
+}
+\description{
+  Uniformity trial of tomato in India.
+}
+\usage{data("shafi.tomato.uniformity")}
+\format{
+  A data frame with 200 observations on the following 3 variables.
+  \describe{
+    \item{\code{row}}{row ordinate}
+    \item{\code{col}}{column ordinate}
+    \item{\code{yield}}{yield, kg/plot}
+  }
+}
+\details{
+  The original data was collected on 1m x 1m plots. The data here are
+  aggregated 2m x 2m plots.
+
+  Field length: 20 row * 2 m = 40 m
+
+  Field width: 10 col * 2 m = 20 m
+}
+\source{
+  Shafi, Sameera (2007).
+  On Some Aspects of Plot Techniques in Field Experiments on Tomato (Lycopersicon esculentum mill.) in Soils of Kashmir.
+  Thesis. Univ. of Ag. Sciences & Technology of Kashmir. Table 2.2.1.
+  https://krishikosh.egranth.ac.in/handle/1/5810083035
+}
+\references{
+  Shafi, Sameera; S.A.Mir, Nageena Nazir, and Anjum Rashid. (2010).
+  Optimum plot size for tomato by using S-PLUS and R-software's in the soils of Kashmir.
+  Asian J. Soil Sci., 4, 311-314.
+  https://www.researchjournal.co.in/upload/assignments/4_311-314.pdf
+}
+\examples{
+\dontrun{
+  library(agridat)
+  data(shafi.tomato.uniformity)
+  shafi.tomato.uniformity <- dat
+
+  libs(desplot)
+  desplot(dat, yield ~ col*row,
+          aspect=40/20, # true aspect
+          main="shafi.tomato.uniformity")
+}
+}
+\keyword{datasets}
diff -Nru agridat-1.17/man/silva.cotton.Rd agridat-1.18/man/silva.cotton.Rd
--- agridat-1.17/man/silva.cotton.Rd	2019-11-22 17:12:50.000000000 +0000
+++ agridat-1.18/man/silva.cotton.Rd	2020-12-11 20:57:55.000000000 +0000
@@ -48,7 +48,7 @@
   Used with permission of Walmes Zeviani.
 
   Electronic version from:
-  http://www.leg.ufpr.br/~walmes/data/desfolha_algodao.txt
+  https://www.leg.ufpr.br/~walmes/data/desfolha_algodao.txt
 
 }
 \source{
@@ -56,18 +56,18 @@
   Fernandes, Marcos Gino; & Zeviani, Walmes Marques. (2012).
   Impacto de diferentes niveis de desfolha artificial nos estadios fenologicos do algodoeiro.
   Revista de Ciencias Agrarias, 35(1), 163-172.
-  http://www.scielo.mec.pt/scielo.php?script=sci_arttext&pid=S0871-018X2012000100016&lng=pt&tlng=pt.
+  https://www.scielo.mec.pt/scielo.php?script=sci_arttext&pid=S0871-018X2012000100016&lng=pt&tlng=pt.
 }
 \references{
 
   Zeviani, W. M., Ribeiro, P. J., Bonat, W. H., Shimakura, S. E., Muniz, J. A. (2014).
   The Gamma-count distribution in the analysis of experimental underdispersed data.
   \emph{Journal of Applied Statistics}, 41(12), 1-11.
-  http://doi.org/10.1080/02664763.2014.922168
-  Online supplement: http://leg.ufpr.br/doku.php/publications:papercompanions:zeviani-jas2014
+  https://doi.org/10.1080/02664763.2014.922168
+  Online supplement: https://leg.ufpr.br/doku.php/publications:papercompanions:zeviani-jas2014
 
   Regression Models for Count Data.
-  http://cursos.leg.ufpr.br/rmcd/applications.html#cotton-bolls
+  https://cursos.leg.ufpr.br/rmcd/applications.html#cotton-bolls
 
   Wagner Hugo Bonat & Walmes Marques Zeviani (2017).
   Multivariate Covariance Generalized Linear Models for the Analysis of
diff -Nru agridat-1.17/man/sinclair.clover.Rd agridat-1.18/man/sinclair.clover.Rd
--- agridat-1.17/man/sinclair.clover.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/sinclair.clover.Rd	2020-12-11 20:57:56.000000000 +0000
@@ -33,7 +33,7 @@
   Dodds, KG and Sinclair, AG and Morrison, JD. (1995).
   A bivariate response surface for growth data.
   \emph{Fertilizer research}, 45, 117-122.
-  http://doi.org/10.1007/BF00790661
+  https://doi.org/10.1007/BF00790661
 }
 
 \examples{
diff -Nru agridat-1.17/man/smith.beans.uniformity.Rd agridat-1.18/man/smith.beans.uniformity.Rd
--- agridat-1.17/man/smith.beans.uniformity.Rd	2020-07-04 21:22:17.000000000 +0000
+++ agridat-1.18/man/smith.beans.uniformity.Rd	2020-12-11 20:57:57.000000000 +0000
@@ -57,7 +57,7 @@
   Effects of plot size, plot shape, and number of replications on the
   efficacy of bean yield trials.
   Hilgardia, 28, 43-63.
-  http://doi.org/10.3733/hilg.v28n02p043  
+  https://doi.org/10.3733/hilg.v28n02p043  
 }
 
 \references{
diff -Nru agridat-1.17/man/smith.corn.uniformity.Rd agridat-1.18/man/smith.corn.uniformity.Rd
--- agridat-1.17/man/smith.corn.uniformity.Rd	2020-07-04 21:30:47.000000000 +0000
+++ agridat-1.18/man/smith.corn.uniformity.Rd	2020-12-11 20:57:58.000000000 +0000
@@ -101,12 +101,12 @@
 \source{
   Smith, L.H. 1910. Plot arrangement for variety experiments with corn.
   Agronomy Journal, 1, 84--89. Table 1.
-  http://books.google.com/books?id=mQT0AAAAMAAJ&pg=PA84
+  https://books.google.com/books?id=mQT0AAAAMAAJ&pg=PA84
 
   Harris, J.A. 1920. Practical universality of field heterogeneity as a factor
   influencing plot yields.
   Journal of Agricultural Research, 19, 279--314. Page 296-297.
-  http://books.google.com/books?id=jyEXAAAAYAAJ&pg=PA279
+  https://books.google.com/books?id=jyEXAAAAYAAJ&pg=PA279
 }
 
 \examples{
diff -Nru agridat-1.17/man/snedecor.asparagus.Rd agridat-1.18/man/snedecor.asparagus.Rd
--- agridat-1.17/man/snedecor.asparagus.Rd	2019-12-06 00:14:14.000000000 +0000
+++ agridat-1.18/man/snedecor.asparagus.Rd	2020-12-19 14:30:57.000000000 +0000
@@ -61,124 +61,60 @@
 
   # ----------
 
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") < "4") {
-    # asreml3
-    # Split-plot with asreml
-    libs(asreml)
-    m2 <- asreml(yield ~ trt + year + trt:year, data=dat,
-                 random = ~ block + block:trt)
-    
-    libs(lucid)
-    vc(m2)
-    ##              effect component std.error z.ratio constr
-    ##     block!block.var     354.3     405.3    0.87      P
-    ## block:trt!block.var     462.8     256.8    1.8       P
-    ##          R!variance     404.7      82.6    4.9       P
-    
-    # Antedependence with asreml.  See O'Neill (2010).
-    dat <- dat[order(dat$block, dat$trt), ]
-    m3 <- asreml(yield ~ year * trt, data=dat,
-                 random = ~ block,
-                 rcov = ~ block:trt:ante(year,1))
-    
-    # Extract the covariance matrix for years and convert to correlation
-    covmat <- diag(4)
-    covmat[upper.tri(covmat,diag=TRUE)] <- m3$R.param$R$year$initial
-    covmat[lower.tri(covmat)] <- t(covmat)[lower.tri(covmat)]
-    round(cov2cor(covmat),2) # correlation among the 4 years
-    #      [,1] [,2] [,3] [,4]
-    # [1,] 1.00 0.45 0.39 0.31
-    # [2,] 0.45 1.00 0.86 0.69
-    # [3,] 0.39 0.86 1.00 0.80
-    # [4,] 0.31 0.69 0.80 1.00
-    
-    # We can also build the covariance Sigma by hand from the estimated
-    # variance components via: Sigma^-1 = U D^-1 U'
-    vv <- vc(m3)
-    print(vv)
-    ##            effect component std.error z.ratio constr
-    ##   block!block.var  86.56    156.9        0.55    pos
-    ##        R!variance   1              NA      NA    fix
-    ##  R!year.1930:1930   0.00233   0.00106    2.2   uncon
-    ##  R!year.1931:1930  -0.7169    0.4528    -1.6   uncon
-    ##  R!year.1931:1931   0.00116   0.00048    2.4   uncon
-    ##  R!year.1932:1931  -1.139     0.1962    -5.8   uncon
-    ##  R!year.1932:1932   0.00208   0.00085    2.4   uncon
-    ##  R!year.1933:1932  -0.6782    0.1555    -4.4   uncon
-    ##  R!year.1933:1933   0.00201   0.00083    2.4   uncon
-    
-    U <- diag(4)
-    U[1,2] <- vv[4,2] ; U[2,3] <- vv[6,2] ; U[3,4] <- vv[8,2]
-    Dinv <- diag(c(vv[3,2], vv[5,2], vv[7,2], vv[9,2]))
-    # solve(U %*% Dinv %*% t(U)) # same as 'covmat' above
-    solve(crossprod(t(U), tcrossprod(Dinv, U)) )
-    ##          [,1]      [,2]      [,3]      [,4]
-    ## [1,] 428.4310  307.1478  349.8152  237.2453
-    ## [2,] 307.1478 1083.9717 1234.5516  837.2751
-    ## [3,] 349.8152 1234.5516 1886.5150 1279.4378
-    ## [4,] 237.2453  837.2751 1279.4378 1364.8446
-  }
+  libs(asreml,lucid) # asreml4
 
-  # ----------
-
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
-    # Split-plot with asreml
-    m2 <- asreml(yield ~ trt + year + trt:year, data=dat,
-                 random = ~ block + block:trt)
-    # vc(m2)
-    ##    effect component std.error z.ratio bound %ch
-    ##     block     354.3     405      0.87     P 0.1
-    ## block:trt     462.8     256.9    1.8      P 0  
-    ##   units!R     404.7      82.6    4.9      P 0  
-
-    ## # Antedependence with asreml.  See O'Neill (2010).
-    dat <- dat[order(dat$block, dat$trt), ]
-    m3 <- asreml(yield ~ year * trt, data=dat,
-                 random = ~ block,
-                 residual = ~ block:trt:ante(year,1),
-                 max=50)
-
-    ## # Extract the covariance matrix for years and convert to correlation
-    ## covmat <- diag(4)
-    ## covmat[upper.tri(covmat,diag=TRUE)] <- m3$R.param$`block:trt:year`$year$initial
-    ## covmat[lower.tri(covmat)] <- t(covmat)[lower.tri(covmat)]
-    ## round(cov2cor(covmat),2) # correlation among the 4 years
-    ## #      [,1] [,2] [,3] [,4]
-    ## # [1,] 1.00 0.45 0.39 0.31
-    ## # [2,] 0.45 1.00 0.86 0.69
-    ## # [3,] 0.39 0.86 1.00 0.80
-    ## # [4,] 0.31 0.69 0.80 1.00
-
-    ## # We can also build the covariance Sigma by hand from the estimated
-    ## # variance components via: Sigma^-1 = U D^-1 U'
-    ## vv <- vc(m3)
-    ## print(vv)
-    ## ##            effect component std.error z.ratio constr
-    ## ##   block!block.var  86.56    156.9        0.55    pos
-    ## ##        R!variance   1              NA      NA    fix
-    ## ##  R!year.1930:1930   0.00233   0.00106    2.2   uncon
-    ## ##  R!year.1931:1930  -0.7169    0.4528    -1.6   uncon
-    ## ##  R!year.1931:1931   0.00116   0.00048    2.4   uncon
-    ## ##  R!year.1932:1931  -1.139     0.1962    -5.8   uncon
-    ## ##  R!year.1932:1932   0.00208   0.00085    2.4   uncon
-    ## ##  R!year.1933:1932  -0.6782    0.1555    -4.4   uncon
-    ## ##  R!year.1933:1933   0.00201   0.00083    2.4   uncon
-
-    ## U <- diag(4)
-    ## U[1,2] <- vv[4,2] ; U[2,3] <- vv[6,2] ; U[3,4] <- vv[8,2]
-    ## Dinv <- diag(c(vv[3,2], vv[5,2], vv[7,2], vv[9,2]))
-    ## # solve(U %*% Dinv %*% t(U)) # same as 'covmat' above
-    ## solve(crossprod(t(U), tcrossprod(Dinv, U)) )
-    ## ##          [,1]      [,2]      [,3]      [,4]
-    ## ## [1,] 428.4310  307.1478  349.8152  237.2453
-    ## ## [2,] 307.1478 1083.9717 1234.5516  837.2751
-    ## ## [3,] 349.8152 1234.5516 1886.5150 1279.4378
-    ## ## [4,] 237.2453  837.2751 1279.4378 1364.8446
-    
-  }
+  # Split-plot with asreml
+  m2 <- asreml(yield ~ trt + year + trt:year, data=dat,
+               random = ~ block + block:trt)
+  # vc(m2)
+  ##    effect component std.error z.ratio bound %ch
+  ##     block     354.3     405      0.87     P 0.1
+  ## block:trt     462.8     256.9    1.8      P 0  
+  ##   units!R     404.7      82.6    4.9      P 0  
+
+  ## # Antedependence with asreml.  See O'Neill (2010).
+  dat <- dat[order(dat$block, dat$trt), ]
+  m3 <- asreml(yield ~ year * trt, data=dat,
+               random = ~ block,
+               residual = ~ block:trt:ante(year,1),
+               max=50)
+
+  ## # Extract the covariance matrix for years and convert to correlation
+  ## covmat <- diag(4)
+  ## covmat[upper.tri(covmat,diag=TRUE)] <- m3$R.param$`block:trt:year`$year$initial
+  ## covmat[lower.tri(covmat)] <- t(covmat)[lower.tri(covmat)]
+  ## round(cov2cor(covmat),2) # correlation among the 4 years
+  ## #      [,1] [,2] [,3] [,4]
+  ## # [1,] 1.00 0.45 0.39 0.31
+  ## # [2,] 0.45 1.00 0.86 0.69
+  ## # [3,] 0.39 0.86 1.00 0.80
+  ## # [4,] 0.31 0.69 0.80 1.00
+
+  ## # We can also build the covariance Sigma by hand from the estimated
+  ## # variance components via: Sigma^-1 = U D^-1 U'
+  ## vv <- vc(m3)
+  ## print(vv)
+  ## ##            effect component std.error z.ratio constr
+  ## ##   block!block.var  86.56    156.9        0.55    pos
+  ## ##        R!variance   1              NA      NA    fix
+  ## ##  R!year.1930:1930   0.00233   0.00106    2.2   uncon
+  ## ##  R!year.1931:1930  -0.7169    0.4528    -1.6   uncon
+  ## ##  R!year.1931:1931   0.00116   0.00048    2.4   uncon
+  ## ##  R!year.1932:1931  -1.139     0.1962    -5.8   uncon
+  ## ##  R!year.1932:1932   0.00208   0.00085    2.4   uncon
+  ## ##  R!year.1933:1932  -0.6782    0.1555    -4.4   uncon
+  ## ##  R!year.1933:1933   0.00201   0.00083    2.4   uncon
+  
+  ## U <- diag(4)
+  ## U[1,2] <- vv[4,2] ; U[2,3] <- vv[6,2] ; U[3,4] <- vv[8,2]
+  ## Dinv <- diag(c(vv[3,2], vv[5,2], vv[7,2], vv[9,2]))
+  ## # solve(U %*% Dinv %*% t(U)) # same as 'covmat' above
+  ## solve(crossprod(t(U), tcrossprod(Dinv, U)) )
+  ## ##          [,1]      [,2]      [,3]      [,4]
+  ## ## [1,] 428.4310  307.1478  349.8152  237.2453
+  ## ## [2,] 307.1478 1083.9717 1234.5516  837.2751
+  ## ## [3,] 349.8152 1234.5516 1886.5150 1279.4378
+  ## ## [4,] 237.2453  837.2751 1279.4378 1364.8446
 
 }
 }
diff -Nru agridat-1.17/man/snijders.fusarium.Rd agridat-1.18/man/snijders.fusarium.Rd
--- agridat-1.17/man/snijders.fusarium.Rd	2019-11-22 17:13:05.000000000 +0000
+++ agridat-1.18/man/snijders.fusarium.Rd	2020-12-11 20:57:59.000000000 +0000
@@ -40,14 +40,14 @@
   Genotype x strain interactions for resistance to Fusarium head blight
   caused by Fusarium culmorum in winter wheat.
   Theoretical and Applied Genetics, 81, 239--244. Table 1.
-  http://doi.org/10.1007/BF00215729
+  https://doi.org/10.1007/BF00215729
 }
 
 \references{
   Fred A van Eeuwijk. 1995.
   Multiplicative interaction in generalized linear models.
   \emph{Biometrics}, 51, 1017-1032.
-  http://doi.org/10.2307/2533001
+  https://doi.org/10.2307/2533001
 }
 
 \examples{
diff -Nru agridat-1.17/man/stephens.sorghum.uniformity.Rd agridat-1.18/man/stephens.sorghum.uniformity.Rd
--- agridat-1.17/man/stephens.sorghum.uniformity.Rd	2020-07-04 21:31:05.000000000 +0000
+++ agridat-1.18/man/stephens.sorghum.uniformity.Rd	2020-12-11 20:58:00.000000000 +0000
@@ -32,7 +32,7 @@
   Stephens, Joseph C. 1928.
   Experimental methods and the probable error in field experiments with sorghum.
   Journal of Agricultural Research, 37, 629--646.
-  http://naldc.nal.usda.gov/catalog/IND43967516
+  https://naldc.nal.usda.gov/catalog/IND43967516
 }
 
 \examples{
diff -Nru agridat-1.17/man/steptoe.morex.pheno.Rd agridat-1.18/man/steptoe.morex.pheno.Rd
--- agridat-1.17/man/steptoe.morex.pheno.Rd	2020-07-30 16:50:50.000000000 +0000
+++ agridat-1.18/man/steptoe.morex.pheno.Rd	2020-12-11 20:58:00.000000000 +0000
@@ -50,7 +50,7 @@
   set of 150 lines was developed.
   
   Phenotypic values for the parents Steptoe and Morex are here:
-  http://wheat.pw.usda.gov/ggpages/SxM/parental_values.html
+  https://wheat.pw.usda.gov/ggpages/SxM/parental_values.html
   
   There are 16 locations, The average across locations is in column 17.  Not all
   traits were collected at every location.  At each location, all 150 lines were
@@ -66,16 +66,16 @@
   Grain Yield (Mt/Ha).
   
   Phenotypic values of the 150 lines in the F1 population are here:
-  http://wheat.pw.usda.gov/ggpages/SxM/phenotypes.html
+  https://wheat.pw.usda.gov/ggpages/SxM/phenotypes.html
   
   Each trait is in a different file, in which each block of numbers
   represents one location.
   
   The 223-markers Steptoe/Morex base map is here:
-  http://wheat.pw.usda.gov/ggpages/SxM/smbasev2.map
+  https://wheat.pw.usda.gov/ggpages/SxM/smbasev2.map
 
   The data for these markers on the 150 lines is
-  http://wheat.pw.usda.gov/ggpages/SxM/smbasev2.mrk
+  https://wheat.pw.usda.gov/ggpages/SxM/smbasev2.mrk
   
   These were hand-assembled (e.g. marker distances were cumulated to
   marker positions) into a .csv file which was then imported into
@@ -85,14 +85,14 @@
   The marker data is coded as A = Steptoe, B = Morex, - = missing.
   
   The pedigrees for the 150 lines are found here:
-  http://wheat.pw.usda.gov/ggpages/SxM/pedigrees.html
+  https://wheat.pw.usda.gov/ggpages/SxM/pedigrees.html
 
 }
 
 \source{
   The Steptoe x Morex Barley Mapping Population.
   Map: Version 2, August 1, 1995
-  http://wheat.pw.usda.gov/ggpages/SxM.  Accessed Jan 2015.
+  https://wheat.pw.usda.gov/ggpages/SxM.  Accessed Jan 2015.
 
   Data provided by the United States Department of Agriculture.
 }
@@ -104,13 +104,13 @@
   Quantitative trait locus effects and environmental interaction in a sample of
   North American barley germplasm.
   \emph{Theoretical and Applied Genetics}, 87, 392--401.
-  http://doi.org/10.1007/BF01184929
+  https://doi.org/10.1007/BF01184929
 
   Ignacio Romagosa, Steven E. Ullrich, Feng Han, Patrick M. Hayes. 1996.
   Use of the additive main effects and multiplicative interaction model
   in QTL mapping for adaptation in barley.
   \emph{Theor Appl Genet}, 93, 30-37.
-  http://doi.org/10.1007/BF00225723
+  https://doi.org/10.1007/BF00225723
 
   Piepho, Hans-Peter. 2000.
   A mixed-model approach to mapping quantitative trait loci in barley on
@@ -121,7 +121,7 @@
   Mixed models including environmental covariables for studying QTL by
   environment interaction.
   \emph{Euphytica}, 137, 139-145.
-  http://doi.org/10.1023/B:EUPH.0000040511.4638
+  https://doi.org/10.1023/B:EUPH.0000040511.4638
 
 }
 
@@ -174,12 +174,12 @@
   qtl::plotMissing(datg)
   wgaim::linkMap(datg)
   # Create an interval object for wgaim
-  dati <- cross2int(datg, id="gen")
+  dati <- wgaim::cross2int(datg, id="gen")
   
   # Whole genome qtl
-  q1 <- wgaim(m1, intervalObj=dati, merge.by="gen", na.action=na.method(x="include"))
+  q1 <- wgaim::wgaim(m1, intervalObj=dati, merge.by="gen", na.action=na.method(x="include"))
   #wgaim::linkMap(q1, dati) # Visualize
-  outStat(q1, dati) # outlier statistic
+  wgaim::outStat(q1, dati) # outlier statistic
   summary(q1, dati)  # Table of important intervals
   # Chrom Left Marker dist(cM) Right Marker dist(cM)   Size Pvalue % Var
   #     3      ABG399     52.6       BCD828     56.1  0.254  0.000  45.0
diff -Nru agridat-1.17/man/streibig.competition.Rd agridat-1.18/man/streibig.competition.Rd
--- agridat-1.17/man/streibig.competition.Rd	2019-11-22 17:13:34.000000000 +0000
+++ agridat-1.18/man/streibig.competition.Rd	2020-12-11 04:03:38.000000000 +0000
@@ -42,7 +42,8 @@
 }
 
 \examples{
-
+\dontrun{
+  
 library(agridat)
 
 data(streibig.competition)
@@ -73,4 +74,5 @@
 }
 
 }
+}
 \keyword{datasets}
diff -Nru agridat-1.17/man/strickland.apple.uniformity.Rd agridat-1.18/man/strickland.apple.uniformity.Rd
--- agridat-1.17/man/strickland.apple.uniformity.Rd	2020-07-04 21:31:33.000000000 +0000
+++ agridat-1.18/man/strickland.apple.uniformity.Rd	2020-12-11 20:58:01.000000000 +0000
@@ -27,7 +27,7 @@
   A. G. Strickland (1935).
   Error in horticultural experiments.
   Journal of Agriculture, Victoria, 33, 408-416.
-  http://handle.slv.vic.gov.au/10381/386642
+  https://handle.slv.vic.gov.au/10381/386642
 }
 \references{
   None
diff -Nru agridat-1.17/man/strickland.grape.uniformity.Rd agridat-1.18/man/strickland.grape.uniformity.Rd
--- agridat-1.17/man/strickland.grape.uniformity.Rd	2020-07-04 21:31:43.000000000 +0000
+++ agridat-1.18/man/strickland.grape.uniformity.Rd	2020-12-11 20:58:03.000000000 +0000
@@ -19,7 +19,7 @@
 \details{
   Yields of individual grape vines, planted 8 feet apart in rows 10 feet
   apart.
-  Grown in Rutherglen, North-East Victoria, Australiaj, 1930.
+  Grown in Rutherglen, North-East Victoria, Australia, 1930.
 
   Certain sections were omitted because of missing vines.
 }
@@ -27,7 +27,7 @@
   A. G. Strickland (1932).
   A vine uniformity trial.
   Journal of Agriculture, Victoria, 30, 584-593.
-  http://handle.slv.vic.gov.au/10381/386462
+  https://handle.slv.vic.gov.au/10381/386462
 }
 \references{
   None
diff -Nru agridat-1.17/man/strickland.peach.uniformity.Rd agridat-1.18/man/strickland.peach.uniformity.Rd
--- agridat-1.17/man/strickland.peach.uniformity.Rd	2020-07-04 21:31:55.000000000 +0000
+++ agridat-1.18/man/strickland.peach.uniformity.Rd	2020-12-11 20:58:04.000000000 +0000
@@ -23,7 +23,7 @@
   A. G. Strickland (1935).
   Error in horticultural experiments.
   Journal of Agriculture, Victoria, 33, 408-416.
-  http://handle.slv.vic.gov.au/10381/386642
+  https://handle.slv.vic.gov.au/10381/386642
 }
 \references{
   None
diff -Nru agridat-1.17/man/strickland.tomato.uniformity.Rd agridat-1.18/man/strickland.tomato.uniformity.Rd
--- agridat-1.17/man/strickland.tomato.uniformity.Rd	2020-07-04 21:32:09.000000000 +0000
+++ agridat-1.18/man/strickland.tomato.uniformity.Rd	2020-12-11 20:58:05.000000000 +0000
@@ -30,7 +30,7 @@
   A. G. Strickland (1935).
   Error in horticultural experiments.
   Journal of Agriculture, Victoria, 33, 408-416.
-  http://handle.slv.vic.gov.au/10381/386642
+  https://handle.slv.vic.gov.au/10381/386642
 }
 \references{
   None
diff -Nru agridat-1.17/man/stroup.nin.Rd agridat-1.18/man/stroup.nin.Rd
--- agridat-1.17/man/stroup.nin.Rd	2020-07-04 21:32:17.000000000 +0000
+++ agridat-1.18/man/stroup.nin.Rd	2020-12-11 20:58:06.000000000 +0000
@@ -29,7 +29,7 @@
   Littel et al are given in meters and make the orientation clear.
 
   Field length: 11 plots * 4.3 m = 47.3 m
-  
+
   Field width: 22 plots * 1.2 m = 26.4 m
 
   All plots with missing data are coded as being gen = "Lancer".
@@ -68,14 +68,14 @@
   Note that the figures in Stroup 2002 claim to be based on this data,
   but the number of rows and columns are both off by 1 and the positions
   of Buckskin as shown in Stroup 2002 do not appear to be quite right.
-  
+
 }
 
 \source{
   Stroup, Walter W., P Stephen Baenziger, Dieter K Mulitze (1994)
   Removing Spatial Variation from Wheat Yield Trials: A Comparison of Methods.
   \emph{Crop Science}, 86:62--66.
-  http://doi.org/10.2135/cropsci1994.0011183X003400010011x
+  https://doi.org/10.2135/cropsci1994.0011183X003400010011x
 }
 
 \references{
@@ -94,8 +94,8 @@
   Comparing Design and Analysis Strategies in the Presence of Spatial
   Variability. \emph{Journal of Agricultural, Biological, and
     Environmental Statistics}, 7(4), 491-511.
-  http://doi.org/10.1198/108571102780
-  
+  https://doi.org/10.1198/108571102780
+
 }
 
 \seealso{
@@ -105,58 +105,57 @@
 
 \examples{
 \dontrun{
-  
+
   library(agridat)
   data(stroup.nin)
   dat <- stroup.nin
-  
-  # Experiment layout. All "Buckskin" plots are near left side
+
+  # Experiment layout. All "Buckskin" plots are near left side and suffer
+  # from poor fertility in two of the reps.
   libs(desplot)
   desplot(dat, yield~col*row,
           aspect=47.3/26.4, out1="rep", num=gen, cex=0.6, # true aspect
           main="stroup.nin - yield heatmap (true shape)")
 
+  # Dataframe to hold model predictions
+  preds <- data.frame(gen=levels(dat$gen))
 
-  # ----- nlme -----
-  
+
+  # -----
+  # nlme
   libs(nlme)
   # Random RCB model
   lme1 <- lme(yield ~ 0 + gen, random=~1|rep, data=dat, na.action=na.omit)
-  
+  preds$lme1 <- fixef(lme1)
+
   # Linear (Manhattan distance) correlation model
   lme2 <- gls(yield ~ 0 + gen, data=dat,
               correlation = corLin(form = ~ col + row, nugget=TRUE),
               na.action=na.omit)
-  
+  preds$lme2 <- coef(lme2)
+
   # Random block and spatial correlation.
   # Note: corExp and corSpher give nearly identical results
   lme3 <- lme(yield ~ 0 + gen, data=dat,
               random = ~ 1 | rep,
               correlation = corExp(form = ~ col + row),
               na.action=na.omit)
-  
-  AIC(lme1,lme2,lme3) # lme2 is lowest
+  preds$lme3 <- fixef(lme3)
+
+  # AIC(lme1,lme2,lme3) # lme2 is lowest
   ##      df      AIC
   ## lme1 58 1333.702
   ## lme2 59 1189.135
   ## lme3 59 1216.704
-  
-  # Compare the estimates from the two methods
-  eff = data.frame(ranblock=fixef(lme1), corLin = coef(lme2),
-                   corExp = fixef(lme3))
-  rownames(eff) <- gsub("gen", "", rownames(eff))
-  plot(eff$ranblock, eff$corLin, xlim=c(13,37), ylim=c(13,37),
-       main="stroup.nin - Gen estimates (intercepts differ)",
-       xlab="RCB (random block)", ylab="corLin",type='n')
-  text(eff$ranblock, eff$corLin, rownames(eff), cex=0.5)
-  abline(0,1)
 
-  # -----------
 
+  # -----
+  # SpATS
   libs(SpATS)
-    
+
   dat <- transform(dat, yf = as.factor(row), xf = as.factor(col))
-  
+
+  # what are colcode and rowcode???
   sp1 <- SpATS(response = "yield",
                spatial = ~ SAP(col, row, nseg = c(10,20), degree = 3, pord = 2),
                genotype = "gen",
@@ -164,90 +163,65 @@
                random = ~ yf + xf,
                data = dat,
                control = list(tolerance = 1e-03))
-  plot(sp1)
-  
-  eff <- cbind(eff, spats=predict(sp1, which="gen")$predicted.values)
-  plot(eff$ranblock, eff$spats, xlim=c(13,37), ylim=c(13,37),
-       main="stroup.nin - Gen estimates",
-       xlab="RCB (random block)", ylab="SpATS",type='n')
-  text(eff$ranblock, eff$spats, rownames(eff), cex=0.5)
-  abline(0,1)
+  #plot(sp1)
+  preds$spats <- predict(sp1, which="gen")$predicted.value
 
 
-  # ----------------------------------------------------------------------------
-  # TMB = Template Model Builder could do this.  See the ar1xar1 example:
+  # -----
+  # Template Model Builder
+  # See the ar1xar1 example:
   # https://github.com/kaskr/adcomp/tree/master/TMB/inst/examples
   # This example uses dpois() in the cpp file to model a Poisson response
   # with separable AR1xAR1.  I think this example could be used for the
   # stroup.nin data, changing dpois() to something Normal.
-  # ----------------------------------------------------------------------------
 
 
+  # -----
+  # asreml4
   libs(asreml,lucid)
-  if( utils::packageVersion("asreml") < "4") {
-    # asreml3
-    
-    # RCB analysis
-    dat.rcb <- asreml(yield ~ gen, random = ~ rep, data=dat,
-                      na.method.X="omit")
-    pred.rcb <- predict(dat.rcb, data=dat, classify="gen")$predictions
-    
-    # Two-dimensional AR1xAR1 spatial model
-    dat <- transform(dat, xf=factor(col), yf=factor(row))
-    dat <- dat[order(dat$xf, dat$yf),]
-    dat.sp <- asreml(yield~gen, data=dat,
-                     rcov=~ar1(xf):ar1(yf),
-                     na.method.X='ignore')
-    pred.sp <- predict(dat.sp, data=dat, classify="gen")$predictions
-
-    # lucid::vc(dat.sp)
-    ##     effect component std.error z.ratio constr
-    ## R!variance   48.7      7.155       6.8    pos
-    ##   R!xf.cor    0.6555   0.05638    12      unc
-    ##   R!yf.cor    0.4375   0.0806      5.4    unc
-
-    # Compare the estimates from the two methods
-    plot(pred.rcb$pvals[,2], pred.sp$pvals[,2], xlim=c(13,37), ylim=c(13,37),
-         xlab="RCB",ylab="AR1xAR1",type='n')
-    title("stroup.nin: Comparison of predicted values")
-    text(pred.rcb$pvals[,2],pred.sp$pvals[,2],
-         as.character(pred.rcb$pvals[,1]),cex=0.5)
-    abline(0,1) 
-  }
 
-  # ----------
+  # RCB analysis
+  as1 <- asreml(yield ~ gen, random = ~ rep, data=dat,
+                na.action=na.method(x="omit"))
+  preds$asreml1 <- predict(as1, data=dat, classify="gen")$pvals$predicted.value
+
+  # Two-dimensional AR1xAR1 spatial model
+  dat <- transform(dat, xf=factor(col), yf=factor(row))
+  dat <- dat[order(dat$xf, dat$yf),]
+  as2 <- asreml(yield~gen, data=dat,
+                residual = ~ar1(xf):ar1(yf),
+                na.action=na.method(x="omit"))
+  preds$asreml2 <- predict(as2, data=dat, classify="gen")$pvals$predicted.value
+
+  lucid::vc(as2)
+  ##     effect component std.error z.ratio constr
+  ## R!variance   48.7      7.155       6.8    pos
+  ##   R!xf.cor    0.6555   0.05638    12      unc
+  ##   R!yf.cor    0.4375   0.0806      5.4    unc
+
+  # Compare the estimates from the two asreml models.
+  # We see that Buckskin has correctly been shifted upward by the spatial model
+  plot(preds$as1, preds$as2, xlim=c(13,37), ylim=c(13,37),
+       xlab="RCB", ylab="AR1xAR1", type='n')
+  title("stroup.nin: Comparison of predicted values")
+  text(preds$asreml1, preds$asreml2, preds$gen, cex=0.5)
+  abline(0,1)
 
-  libs(asreml,lucid)
-  if( utils::packageVersion("asreml") > "4") {
-    # asreml4
 
-    # RCB analysis
-    dat.rcb <- asreml(yield ~ gen, random = ~ rep, data=dat,
-                      na.action=na.method(x="omit"))
-    pred.rcb <- predict(dat.rcb, data=dat, classify="gen")
-    
-    # Two-dimensional AR1xAR1 spatial model
-    dat <- transform(dat, xf=factor(col), yf=factor(row))
-    dat <- dat[order(dat$xf, dat$yf),]
-    dat.sp <- asreml(yield~gen, data=dat,
-                     residual = ~ar1(xf):ar1(yf),
-                     na.action=na.method(x="omit"))
-    pred.sp <- predict(dat.sp, data=dat, classify="gen")
-
-    lucid::vc(dat.sp)
-    ##     effect component std.error z.ratio constr
-    ## R!variance   48.7      7.155       6.8    pos
-    ##   R!xf.cor    0.6555   0.05638    12      unc
-    ##   R!yf.cor    0.4375   0.0806      5.4    unc
-
-    # Compare the estimates from the two methods
-    plot(pred.rcb$pvals[,2], pred.sp$pvals[,2], xlim=c(13,37), ylim=c(13,37),
-         xlab="RCB", ylab="AR1xAR1", type='n')
-  title("stroup.nin: Comparison of predicted values")
-  text(pred.rcb$pvals[,2],pred.sp$pvals[,2],
-       as.character(pred.rcb$pvals[,1]),cex=0.5)
-  abline(0,1)  
-}
+  # -----
+  # sommer
+  # Fixed gen, random row, col, 2D spline
+  libs(sommer)
+  dat <- transform(dat, yf = as.factor(row), xf = as.factor(col))
+  so1 <- mmer(yield ~ 0+gen,
+              random = ~ vs(xf) + vs(yf) + vs(spl2D(row,col)),
+              data=dat)
+  preds$so1 <- coef(so1)[,"Estimate"]
+  # spatPlot
+
+  # -----
+  # compare variety effects from different packages
+  lattice::splom(preds[,-1], main="stroup.nin")
 
 }
 }
diff -Nru agridat-1.17/man/stroup.splitplot.Rd agridat-1.18/man/stroup.splitplot.Rd
--- agridat-1.17/man/stroup.splitplot.Rd	2019-12-13 13:30:13.000000000 +0000
+++ agridat-1.18/man/stroup.splitplot.Rd	2020-12-11 20:58:07.000000000 +0000
@@ -10,7 +10,7 @@
   space, narrow space, etc.).
 
   For example, the density of narrow, intermediate and broad-space
-  predictible function for factor level A1 is shown below (html help only)
+  predictable function for factor level A1 is shown below (html help only)
   \if{html}{\figure{stroupsplitplot.png}{options: width=50\% alt="Figure: stroup.splitplot.png"}}
 
 }
@@ -36,7 +36,7 @@
   Wolfinger, R.D. and Kass, R.E., 2000.
   Nonconjugate Bayesian analysis of variance component models,
   Biometrics, 56, 768--774.
-  http://doi.org/10.1111/j.0006-341X.2000.00768.x
+  https://doi.org/10.1111/j.0006-341X.2000.00768.x
 }
 
 \examples{
diff -Nru agridat-1.17/man/student.barley.Rd agridat-1.18/man/student.barley.Rd
--- agridat-1.17/man/student.barley.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/student.barley.Rd	2020-12-11 20:58:08.000000000 +0000
@@ -50,7 +50,7 @@
   Student. 1923.
   On Testing Varieties of Cereals.
   \emph{Biometrika}, 15, 271--293.
-  http://doi.org/10.1093/biomet/15.3-4.271
+  https://doi.org/10.1093/biomet/15.3-4.271
 }
 
 \references{
diff -Nru agridat-1.17/man/tai.potato.Rd agridat-1.18/man/tai.potato.Rd
--- agridat-1.17/man/tai.potato.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/tai.potato.Rd	2020-12-11 20:58:09.000000000 +0000
@@ -33,7 +33,7 @@
   G.C.C. Tai, 1971.
   Genotypic stability analysis and its application to potato regional trials.
   Crop Sci 11, 184-190.  Table 2, p. 187.
-  http://doi.org/10.2135/cropsci1971.0011183X001100020006x
+  https://doi.org/10.2135/cropsci1971.0011183X001100020006x
 }
 
 \references{
diff -Nru agridat-1.17/man/theobald.barley.Rd agridat-1.18/man/theobald.barley.Rd
--- agridat-1.17/man/theobald.barley.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/theobald.barley.Rd	2020-12-11 20:58:10.000000000 +0000
@@ -33,7 +33,7 @@
   Chris M. Theobald and Mike Talbot, (2002).
   The Bayesian choice of crop variety and fertilizer dose.
   \emph{Appl Statistics}, 51, 23-36.
-  http://doi.org/10.1111/1467-9876.04863
+  https://doi.org/10.1111/1467-9876.04863
 
   Data provided by Chris Theobald and Mike Talbot.
 }
diff -Nru agridat-1.17/man/theobald.covariate.Rd agridat-1.18/man/theobald.covariate.Rd
--- agridat-1.17/man/theobald.covariate.Rd	2019-11-22 17:14:46.000000000 +0000
+++ agridat-1.18/man/theobald.covariate.Rd	2020-12-11 20:58:11.000000000 +0000
@@ -33,7 +33,7 @@
   A Bayesian Approach to Regional and Local-Area Prediction From Crop
   Variety Trials.
   Journ Agric Biol Env Sciences, 7, 403--419.
-  http://doi.org/10.1198/108571102230
+  https://doi.org/10.1198/108571102230
 }
 
 \examples{
diff -Nru agridat-1.17/man/thompson.cornsoy.Rd agridat-1.18/man/thompson.cornsoy.Rd
--- agridat-1.17/man/thompson.cornsoy.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/thompson.cornsoy.Rd	2020-12-11 20:58:12.000000000 +0000
@@ -53,7 +53,7 @@
 
   Some relevant maps of yield, heat, and precipitation can be found in
   \emph{Atlas of crop yield and summer weather patterns, 1931-1975},
-  http://www.isws.illinois.edu/pubdoc/C/ISWSC-150.pdf
+  https://www.isws.illinois.edu/pubdoc/C/ISWSC-150.pdf
 
   The following notes pertain to the Iowa data.
 
diff -Nru agridat-1.17/man/turner.herbicide.Rd agridat-1.18/man/turner.herbicide.Rd
--- agridat-1.17/man/turner.herbicide.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/turner.herbicide.Rd	2020-12-11 20:58:13.000000000 +0000
@@ -27,7 +27,7 @@
   David L. Turner and Michael H. Ralphs and John O. Evans (1992).
   Logistic Analysis for Monitoring and Assessing Herbicide Efficacy.
   \emph{Weed Technology}, 6, 424-430.
-  http://www.jstor.org/stable/3987312
+  https://www.jstor.org/stable/3987312
 }
 
 \references{
diff -Nru agridat-1.17/man/urquhart.feedlot.Rd agridat-1.18/man/urquhart.feedlot.Rd
--- agridat-1.17/man/urquhart.feedlot.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/urquhart.feedlot.Rd	2020-12-11 20:58:14.000000000 +0000
@@ -45,13 +45,13 @@
   N. Scott Urquhart (1982).
   Adjustment in Covariance when One Factor Affects the Covariate
   Biometrics, 38, 651-660.  Table 4, p. 659.
-  http://doi.org/10.2307/2530046
+  https://doi.org/10.2307/2530046
 }
 \references{
   N. Scott Urquhart and David L. Weeks (1978).
   Linear Models in Messy Data: Some Problems and Alternatives
   Biometrics, 34, 696-705.
-  http://doi.org/10.2307/2530391
+  https://doi.org/10.2307/2530391
 
   Also available in the 'emmeans' package as the 'feedlot' data.
 }
diff -Nru agridat-1.17/man/vaneeuwijk.drymatter.Rd agridat-1.18/man/vaneeuwijk.drymatter.Rd
--- agridat-1.17/man/vaneeuwijk.drymatter.Rd	2019-11-22 17:15:25.000000000 +0000
+++ agridat-1.18/man/vaneeuwijk.drymatter.Rd	2020-12-11 20:58:15.000000000 +0000
@@ -35,7 +35,7 @@
   van Eeuwijk, Fred A. and Pieter M. Kroonenberg (1998).
   Multiplicative Models for Interaction in Three-Way ANOVA, with Applications to Plant Breeding
   Biometrics, 54, 1315-1333.
-  http://doi.org/10.2307/2533660
+  https://doi.org/10.2307/2533660
 }
 
 \references{
@@ -43,13 +43,13 @@
   Kroonenberg, P.M., Basford, K.E. & Ebskamp, A.G.M. (1995).
   Three-way cluster and component analysis of maize variety trials.
   Euphytica, 84(1):31-42.
-  http://doi.org/10.1007/BF01677554
+  https://doi.org/10.1007/BF01677554
 
   van Eeuwijk, F.A., Keizer, L.C.P. & Bakker, J.J. Van Eeuwijk. (1995b).
   Linear and bilinear models for the analysis of multi-environment trials: 
   II. An application to data from the Dutch Maize Variety Trials
   Euphytica, 84(1):9-22.
-  http://doi.org/10.1007/BF01677552
+  https://doi.org/10.1007/BF01677552
 
   Hardeo Sahai, Mario M. Ojeda.
   Analysis of Variance for Random Models, Volume 1. Page 261.
diff -Nru agridat-1.17/man/vaneeuwijk.fusarium.Rd agridat-1.18/man/vaneeuwijk.fusarium.Rd
--- agridat-1.17/man/vaneeuwijk.fusarium.Rd	2019-11-22 17:15:51.000000000 +0000
+++ agridat-1.18/man/vaneeuwijk.fusarium.Rd	2020-12-11 20:58:16.000000000 +0000
@@ -37,7 +37,7 @@
   van Eeuwijk, Fred A. and Pieter M. Kroonenberg (1998).
   Multiplicative Models for Interaction in Three-Way ANOVA, with Applications to Plant Breeding
   Biometrics, 54, 1315-1333.
-  http://doi.org/10.2307/2533660
+  https://doi.org/10.2307/2533660
 }
 
 \references{
@@ -49,7 +49,7 @@
   F. graminearum and F. nivale using a multiplicative model for
   interaction.
   Theor Appl Genet. 90(2), 221-8. 
-  http://doi.org/10.1007/BF00222205
+  https://doi.org/10.1007/BF00222205
 }
 
 \examples{
diff -Nru agridat-1.17/man/vaneeuwijk.nematodes.Rd agridat-1.18/man/vaneeuwijk.nematodes.Rd
--- agridat-1.17/man/vaneeuwijk.nematodes.Rd	2019-12-07 17:41:42.000000000 +0000
+++ agridat-1.18/man/vaneeuwijk.nematodes.Rd	2020-12-11 20:58:17.000000000 +0000
@@ -34,7 +34,7 @@
   Fred A. van Eeuwijk, (1995).
   Multiplicative Interaction in Generalized Linear Models.
   \emph{Biometrics}, 51, 1017-1032.
-  http://doi.org/10.2307/2533001
+  https://doi.org/10.2307/2533001
 }
 
 \references{
@@ -42,7 +42,7 @@
   Variation in resistance level of potato genotypes and virulence level of
   potato cyst nematode populations.
   \emph{Euphytica}, 62, 135-143.
-  http://doi.org/10.1007/BF00037939
+  https://doi.org/10.1007/BF00037939
 }
 
 \examples{
diff -Nru agridat-1.17/man/vargas.wheat1.Rd agridat-1.18/man/vargas.wheat1.Rd
--- agridat-1.17/man/vargas.wheat1.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/vargas.wheat1.Rd	2020-12-11 20:58:18.000000000 +0000
@@ -76,7 +76,7 @@
   Martha E Ramirez and Mike Talbot, 1998.
   Interpreting Genotype x Environment Interaction in Wheat by
   Partial Least Squares Regression, \emph{Crop Science}, 38, 679--689.
-  http://doi.org/10.2135/cropsci1998.0011183X003800030010x
+  https://doi.org/10.2135/cropsci1998.0011183X003800030010x
   
   Data provided by Jose Crossa.
 }
diff -Nru agridat-1.17/man/vargas.wheat2.Rd agridat-1.18/man/vargas.wheat2.Rd
--- agridat-1.17/man/vargas.wheat2.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/vargas.wheat2.Rd	2020-12-11 20:58:20.000000000 +0000
@@ -69,7 +69,7 @@
   Martha E Ramirez and Mike Talbot, 1998.
   Interpreting Genotype x Environment Interaction in Wheat by
   Partial Least Squares Regression, \emph{Crop Science}, 38, 679--689.
-  http://doi.org/10.2135/cropsci1998.0011183X003800030010x
+  https://doi.org/10.2135/cropsci1998.0011183X003800030010x
   
   Data provided by Jose Crossa.
 }
diff -Nru agridat-1.17/man/verbyla.lupin.Rd agridat-1.18/man/verbyla.lupin.Rd
--- agridat-1.17/man/verbyla.lupin.Rd	2020-07-29 12:36:27.000000000 +0000
+++ agridat-1.18/man/verbyla.lupin.Rd	2020-12-11 20:58:21.000000000 +0000
@@ -61,7 +61,7 @@
     Myallie \tab 1995\cr
   }
   
-  Data retrieved Oct 2010 from http://www.blackwellpublishers.co.uk/rss.
+  Data retrieved Oct 2010 from https://www.blackwellpublishers.co.uk/rss.
   (No longer available).
 
   Used with permission of Blackwell Publishing.
@@ -74,7 +74,7 @@
   The analysis of designed experiments and longitudinal data by using
   smoothing splines. 
   Appl. Statist., 48, 269--311.
-  http://doi.org/10.1111/1467-9876.00154
+  https://doi.org/10.1111/1467-9876.00154
 
   Arunas P. Verbyla and Brian R. Cullis and Michael G. Kenward
   and Sue J. Welham, (1997).
@@ -82,7 +82,7 @@
   smoothing splines.
   University of Adelaide, Department of Statistics, Research Report
   97/4.
-  http://http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.808
+  https://https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.808
 }
 
 \examples{
diff -Nru agridat-1.17/man/vold.longterm.Rd agridat-1.18/man/vold.longterm.Rd
--- agridat-1.17/man/vold.longterm.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/vold.longterm.Rd	2020-12-11 20:58:22.000000000 +0000
@@ -31,7 +31,7 @@
   Arild Vold (1998).
   A generalization of ordinary yield response functions.
   \emph{Ecological modelling}, 108, 227-236.
-  http://doi.org/10.1016/S0304-3800(98)00031-3
+  https://doi.org/10.1016/S0304-3800(98)00031-3
 }
 
 \references{
diff -Nru agridat-1.17/man/vsn.lupin3.Rd agridat-1.18/man/vsn.lupin3.Rd
--- agridat-1.17/man/vsn.lupin3.Rd	2020-07-29 20:21:08.000000000 +0000
+++ agridat-1.18/man/vsn.lupin3.Rd	2020-12-11 20:58:23.000000000 +0000
@@ -27,7 +27,7 @@
 
 \source{
   Multi-Environment Trials - Lupins.
-  http://www.vsni.co.uk/software/asreml/htmlhelp/asreml/xlupin.htm
+  https://www.vsni.co.uk/software/asreml/htmlhelp/asreml/xlupin.htm
 }
 
 \examples{
diff -Nru agridat-1.17/man/wallace.iowaland.Rd agridat-1.18/man/wallace.iowaland.Rd
--- agridat-1.17/man/wallace.iowaland.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/wallace.iowaland.Rd	2020-12-11 20:58:24.000000000 +0000
@@ -31,12 +31,12 @@
   H.A. Wallace (1926). Comparative Farm-Land Values in Iowa.
   \emph{The Journal of Land & Public Utility Economics}, 2, 385-392.
   Page 387-388.
-  http://doi.org/10.2307/3138610
+  https://doi.org/10.2307/3138610
 }
 
 \references{
   Larry Winner. Spatial Data Analysis.
-  http://www.stat.ufl.edu/~winner/data/iowaland.txt
+  https://www.stat.ufl.edu/~winner/data/iowaland.txt
 }
 
 \examples{
diff -Nru agridat-1.17/man/walsh.cottonprice.Rd agridat-1.18/man/walsh.cottonprice.Rd
--- agridat-1.17/man/walsh.cottonprice.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/walsh.cottonprice.Rd	2020-12-11 20:58:25.000000000 +0000
@@ -30,7 +30,7 @@
   R.M. Walsh (1944).
   Response to Price in Production of Cotton and Cottonseed,
   \emph{Journal of Farm Economics}, 26, 359-372.
-  http://doi.org/10.2307/1232237
+  https://doi.org/10.2307/1232237
 }
 
 \examples{
diff -Nru agridat-1.17/man/wassom.brome.uniformity.Rd agridat-1.18/man/wassom.brome.uniformity.Rd
--- agridat-1.17/man/wassom.brome.uniformity.Rd	2020-07-04 21:32:55.000000000 +0000
+++ agridat-1.18/man/wassom.brome.uniformity.Rd	2020-12-11 20:58:26.000000000 +0000
@@ -50,7 +50,7 @@
   Wassom and R.R. Kalton. (1953).
   Estimations of Optimum Plot Size Using Data from Bromegrass Uniformity
   Trials. Agricultural Experiment Station, Iowa State College, Bulletin 396.
-  http://lib.dr.iastate.edu/ag_researchbulletins/32/
+  https://lib.dr.iastate.edu/ag_researchbulletins/32/
 }
 
 \examples{
diff -Nru agridat-1.17/man/waynick.soil.Rd agridat-1.18/man/waynick.soil.Rd
--- agridat-1.17/man/waynick.soil.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/waynick.soil.Rd	2020-12-11 20:58:27.000000000 +0000
@@ -29,7 +29,7 @@
   Waynick, Dean, and Sharp, Leslie. (1918).
   Variability in soils and its significance to past and future soil investigations, I-II.
   University of California press.
-  http://archive.org/details/variabilityinsoi45wayn
+  https://archive.org/details/variabilityinsoi45wayn
 }
 
 \examples{
diff -Nru agridat-1.17/man/wedderburn.barley.Rd agridat-1.18/man/wedderburn.barley.Rd
--- agridat-1.17/man/wedderburn.barley.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/wedderburn.barley.Rd	2020-12-11 20:58:28.000000000 +0000
@@ -28,7 +28,7 @@
   Quasilikelihood functions, generalized linear models and the
   Gauss-Newton method.
   \emph{Biometrika}, 61, 439--47.
-  http://doi.org/10.2307/2334725
+  https://doi.org/10.2307/2334725
 
   Wedderburn credits the original data to an unpublished thesis by
   J. F. Jenkyn.
diff -Nru agridat-1.17/man/weiss.incblock.Rd agridat-1.18/man/weiss.incblock.Rd
--- agridat-1.17/man/weiss.incblock.Rd	2020-07-21 16:38:12.000000000 +0000
+++ agridat-1.18/man/weiss.incblock.Rd	2020-12-19 14:32:13.000000000 +0000
@@ -42,7 +42,7 @@
   Balanced Incomplete Block and Lattice Square Designs for Testing Yield
   Differences Among Large Numbers of Soybean Varieties.
   \emph{Agricultural Research Bulletins, Nos. 251-259}.
-  http://lib.dr.iastate.edu/ag_researchbulletins/24/
+  https://lib.dr.iastate.edu/ag_researchbulletins/24/
 }
 
 \examples{
@@ -62,18 +62,16 @@
 
   # asreml
   # Standard inc block analysis used by Weiss and Cox
-  libs(asreml)
-  if( utils::packageVersion("asreml") < "4") {
-    m1 <- asreml(yield ~ gen + block , data=dat)
-    predict(m1, data=dat, classify="gen")$predictions$pvals
+  libs(asreml) # asreml 4
+  m1 <- asreml(yield ~ gen + block , data=dat)
+  predict(m1, data=dat, classify="gen")$pvals
   
-    ## gen pred.value std.error est.stat
-    ## G01     24.59    0.8312  Estimable
-    ## G02     26.92    0.8312  Estimable
-    ## G03     32.62    0.8312  Estimable
-    ## G04     26.97    0.8312  Estimable
-    ## G05     26.02    0.8312  Estimable
-  }
+  ## gen pred.value std.error est.stat
+  ## G01     24.59    0.8312  Estimable
+  ## G02     26.92    0.8312  Estimable
+  ## G03     32.62    0.8312  Estimable
+  ## G04     26.97    0.8312  Estimable
+  ## G05     26.02    0.8312  Estimable
 
 }
 }
diff -Nru agridat-1.17/man/weiss.lattice.Rd agridat-1.18/man/weiss.lattice.Rd
--- agridat-1.17/man/weiss.lattice.Rd	2020-07-04 21:33:16.000000000 +0000
+++ agridat-1.18/man/weiss.lattice.Rd	2020-12-19 14:32:46.000000000 +0000
@@ -36,7 +36,7 @@
  Balanced Incomplete Block and Lattice Square Designs for Testing Yield
  Differences Among Large Numbers of Soybean Varieties.  Table 5.
  \emph{Agricultural Research Bulletins, Nos. 251-259}.
- http://lib.dr.iastate.edu/ag_researchbulletins/24/
+ https://lib.dr.iastate.edu/ag_researchbulletins/24/
 }
 
 \examples{
@@ -65,17 +65,11 @@
   
   # ----------
 
-  # asreml3 & asreml4
-  libs(asreml)
+  libs(asreml) # asreml4
   m2 <- asreml(yield ~ rep + rep:xf + rep:yf + gen, data=dat)
   # Weiss table 6 means
-  if( utils::packageVersion("asreml") < "4") {
-    anova(m2)
-    predict(m2, data=dat, classify="gen")$predictions$pvals
-  } else {
-    wald(m2)
-    predict(m2, data=dat, classify="gen")$pvals
-  }
+  wald(m2)
+  predict(m2, data=dat, classify="gen")$pvals
   
   ##  gen pred.value std.error  est.stat
   ##  G01      27.74     1.461 Estimable
diff -Nru agridat-1.17/man/wheatley.carrot.Rd agridat-1.18/man/wheatley.carrot.Rd
--- agridat-1.17/man/wheatley.carrot.Rd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/man/wheatley.carrot.Rd	2020-12-11 20:58:31.000000000 +0000
@@ -40,7 +40,7 @@
   larvae, and its practical applications.
   \emph{Annals of Applied Biology}, 100, 229-244.  Table 2.
   
-  http://doi.org/10.1111/j.1744-7348.1982.tb01935.x
+  https://doi.org/10.1111/j.1744-7348.1982.tb01935.x
 }
 
 \references{
diff -Nru agridat-1.17/man/wiebe.wheat.uniformity.Rd agridat-1.18/man/wiebe.wheat.uniformity.Rd
--- agridat-1.17/man/wiebe.wheat.uniformity.Rd	2020-07-04 21:33:26.000000000 +0000
+++ agridat-1.18/man/wiebe.wheat.uniformity.Rd	2020-12-11 20:58:32.000000000 +0000
@@ -38,14 +38,14 @@
   Variation and Correlation in Grain Yield among 1,500 Wheat
   Nursery Plots.
   Journal of Agricultural Research, 50, 331-357.
-  http://naldc.nal.usda.gov/download/IND43968632/PDF
+  https://naldc.nal.usda.gov/download/IND43968632/PDF
 }
 
 \references{
   D.A. Preece, 1981,
   Distributions of final digits in data,
   \emph{The Statistician}, 30, 31--60.
-  http://doi.org/10.2307/2987702
+  https://doi.org/10.2307/2987702
 
   Wilkinson et al. (1983).
   Nearest Neighbour (NN) Analysis of Field Experiments.
diff -Nru agridat-1.17/man/wiedemann.safflower.uniformity.Rd agridat-1.18/man/wiedemann.safflower.uniformity.Rd
--- agridat-1.17/man/wiedemann.safflower.uniformity.Rd	2020-07-04 21:33:35.000000000 +0000
+++ agridat-1.18/man/wiedemann.safflower.uniformity.Rd	2020-12-11 20:58:33.000000000 +0000
@@ -59,7 +59,7 @@
   Wiedemann, Alfred Max. 1962.
   Estimation of Optimum Plot Size and Shape for Use in Safflower Yield Trails. Table 5.
   All Graduate Theses and Dissertations. Paper 3600. Table 5.
-  http://digitalcommons.usu.edu/etd/3600
+  https://digitalcommons.usu.edu/etd/3600
 }
 
 \references{
diff -Nru agridat-1.17/man/williams.barley.uniformity.Rd agridat-1.18/man/williams.barley.uniformity.Rd
--- agridat-1.17/man/williams.barley.uniformity.Rd	2020-07-04 21:33:46.000000000 +0000
+++ agridat-1.18/man/williams.barley.uniformity.Rd	2020-12-11 20:58:35.000000000 +0000
@@ -34,7 +34,7 @@
   Williams, ER and Luckett, DJ. 1988.
   The use of uniformity data in the design and analysis of cotton and barley variety trials.
   Australian Journal of Agricultural Research, 39, 339-350.
-  http://doi.org/10.1071/AR9880339
+  https://doi.org/10.1071/AR9880339
 }
 \references{
   Maria Xose Rodriguez-Alvarez, Martin P. Boer, Fred A. van Eeuwijk, Paul
diff -Nru agridat-1.17/man/williams.cotton.uniformity.Rd agridat-1.18/man/williams.cotton.uniformity.Rd
--- agridat-1.17/man/williams.cotton.uniformity.Rd	2020-07-04 21:33:58.000000000 +0000
+++ agridat-1.18/man/williams.cotton.uniformity.Rd	2020-12-11 20:58:36.000000000 +0000
@@ -31,7 +31,7 @@
   Williams, ER and Luckett, DJ. 1988.
   The use of uniformity data in the design and analysis of cotton and barley variety trials.
   Australian Journal of Agricultural Research, 39, 339-350.
-  http://doi.org/10.1071/AR9880339
+  https://doi.org/10.1071/AR9880339
 }
 
 \examples{
diff -Nru agridat-1.17/man/williams.trees.Rd agridat-1.18/man/williams.trees.Rd
--- agridat-1.17/man/williams.trees.Rd	2019-11-22 17:17:11.000000000 +0000
+++ agridat-1.18/man/williams.trees.Rd	2020-12-11 20:58:37.000000000 +0000
@@ -33,7 +33,7 @@
   interaction in Thailand.  Chapter 14 of \emph{Trees for the
     Tropics: Growing Australian Multipurpose Trees and Shrubs in
     Developing Countries}. Pages 145--152.
-  http://aciar.gov.au/publication/MN010
+  https://aciar.gov.au/publication/MN010
 }
 
 \references{
diff -Nru agridat-1.17/man/yang.barley.Rd agridat-1.18/man/yang.barley.Rd
--- agridat-1.17/man/yang.barley.Rd	2019-11-22 17:17:31.000000000 +0000
+++ agridat-1.18/man/yang.barley.Rd	2020-12-11 20:58:38.000000000 +0000
@@ -50,7 +50,7 @@
   Rong-Cai Yang (2007).
   Mixed-Model Analysis of Crossover Genotype-Environment Interactions.
   Crop Science, 47, 1051-1062.
-  http://doi.org/10.2135/cropsci2006.09.0611
+  https://doi.org/10.2135/cropsci2006.09.0611
 }
 
 \references{
@@ -58,7 +58,7 @@
   Improved Statistical Inference for Graphical Description and
   Interpretation of Genotype x Environment Interaction.
   Crop Science, 53, 2400-2410.
-  http://doi.org/10.2135/cropsci2013.04.0218 
+  https://doi.org/10.2135/cropsci2013.04.0218 
 }
 
 \examples{
@@ -73,9 +73,9 @@
 
   ## For bootstrapping of a biplot, see the non-cran packages:
   ## 'bbplot' and 'distfree.cr'
-  ## http://statgen.ualberta.ca/index.html?open=software.html
-  ## install.packages("http://statgen.ualberta.ca/download/software/bbplot_1.0.zip")
-  ## install.packages("http://statgen.ualberta.ca/download/software/distfree.cr_1.5.zip")
+  ## https://statgen.ualberta.ca/index.html?open=software.html
+  ## install.packages("https://statgen.ualberta.ca/download/software/bbplot_1.0.zip")
+  ## install.packages("https://statgen.ualberta.ca/download/software/distfree.cr_1.5.zip")
 
   ## libs(SDMTools)
   ## libs(distfree.cr)
diff -Nru agridat-1.17/man/yan.winterwheat.Rd agridat-1.18/man/yan.winterwheat.Rd
--- agridat-1.17/man/yan.winterwheat.Rd	2020-07-05 17:04:14.000000000 +0000
+++ agridat-1.18/man/yan.winterwheat.Rd	2020-12-11 20:58:38.000000000 +0000
@@ -42,7 +42,7 @@
   Weikai Yan and Manjit S. Kang and Baoluo Ma and Sheila Woods, 2007,
   GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data,
   Crop Science, 2007, 47, 641--653.
-  http://doi.org/10.2135/cropsci2006.06.0374
+  https://doi.org/10.2135/cropsci2006.06.0374
 
 }
 
diff -Nru agridat-1.17/man/yates.oats.Rd agridat-1.18/man/yates.oats.Rd
--- agridat-1.17/man/yates.oats.Rd	2020-07-04 21:34:32.000000000 +0000
+++ agridat-1.18/man/yates.oats.Rd	2020-12-19 14:33:41.000000000 +0000
@@ -46,14 +46,14 @@
 
 \source{
   Report for 1931. Rothamsted Experiment Station. Page 143.
-  http://www.era.rothamsted.ac.uk/eradoc/article/ResReport1931-141-159
+  https://www.era.rothamsted.ac.uk/eradoc/article/ResReport1931-141-159
 }
 
 \references{
   Yates, Frank (1935) Complex experiments,
   \emph{Journal of the Royal Statistical Society Suppl}
   2, 181-247. Figure 2.
-  http://doi.org/10.2307/2983638
+  https://doi.org/10.2307/2983638
 }
 
 \examples{
@@ -118,7 +118,7 @@
   # asreml r 4 has a bug with asreml( factor(nitro))
   dat2$nitrof <- factor(dat2$nitro)
 
-  # --- asreml3 & asreml4  ---
+  # --- asreml4  ---
   libs(asreml)
   m5a <- asreml(yield ~ nitrof + gen,
                 random = ~ block + block:gen, data=dat2)
@@ -127,11 +127,7 @@
   vc(m5a)
 
   emmeans::emmeans(m5l, "gen")
-  if( utils::packageVersion("asreml") < "4") {
-    predict(m5a, data=dat2, classify="gen")$predictions$pvals
-  } else {
-    predict(m5a, data=dat2, classify="gen")$pvals
-  }
+  predict(m5a, data=dat2, classify="gen")$pvals
 
   # ----------
 
diff -Nru agridat-1.17/MD5 agridat-1.18/MD5
--- agridat-1.17/MD5	2020-08-03 10:10:06.000000000 +0000
+++ agridat-1.18/MD5	2021-01-12 09:40:21.000000000 +0000
@@ -1,9 +1,9 @@
-150996aa47413238d9b938d4646a76db *DESCRIPTION
+75fc3af33aeb2236a279c7209c3f4ceb *DESCRIPTION
 15dd706d1ecb09d4f5f3816b5e3e237e *LICENSE.note
 42ca102d6452e8a4fb6114673efd1f24 *NAMESPACE
-ae18211d5eb0bd47cd9feaeb8057a097 *NEWS.md
+c01071123806058237767be66e2d2f99 *NEWS.md
 8345eca43e6ddee8ace8e52d60a1d4ed *R/libs.R
-5168d4670414a9e5c24f7629a6de5d48 *build/vignette.rds
+6e162e9eb2a1b4c3237e55f8b9a4401e *build/vignette.rds
 a61bc7d766d1b849769e00e4a6d460e9 *data/aastveit.barley.covs.txt
 98fdcee0ccce4bd4e70142b654913b7f *data/aastveit.barley.height.txt
 a0f97b9f3982f09f76d4610f31b9bade *data/acorsi.grayleafspot.txt
@@ -78,6 +78,7 @@
 8977152b2bed718ea6ac7e452bf6bd78 *data/crossa.wheat.txt
 4cd836f660654bdc34bd4bdbfe2da44f *data/crowder.seeds.txt
 e1ad4bdf88c5bb59f801aa58692c1b9d *data/cullis.earlygen.txt
+5b55532a2e0db201792b2541c7d4e87f *data/damesa.maize.txt
 466d49b64aa6c1d1ec2223500b9b1d3d *data/darwin.maize.txt
 598a1db09531df77c7e70bd9ea98b1a9 *data/dasilva.maize.txt
 6ccf45d6debbe06d6761ac65548223fa *data/davidian.soybean.txt
@@ -166,6 +167,7 @@
 1b94239fa117af8c26438cbaa49afd45 *data/jansen.apple.txt
 0cfb8cc39c482cb53fc4d1b9f13caac6 *data/jansen.carrot.txt
 12e0a62e2d54de1487e4107b635927a4 *data/jansen.strawberry.txt
+38dee3501868f6c156f9b3959b62a7cd *data/jayaraman.bamboo.txt
 05e7929dc1742e646670eeceb6d5c8d8 *data/jenkyn.mildew.txt
 35dd0b96d24b7983f8fd0e87fdc7b91d *data/john.alpha.txt
 799a1f08419681251c241a24e94596c5 *data/johnson.blight.txt
@@ -236,6 +238,7 @@
 a440b01bc3864053bb867a6ac47f1658 *data/moore.springcauliflower.uniformity.txt
 f8a249703dade757e5bab5264da53e73 *data/moore.sweetcorn.uniformity.txt
 8d8fb9f42eca1cc90646eeb5bc1030b6 *data/nagai.strawberry.uniformity.txt
+7572696cf785c3c71e3d7054e458efef *data/nair.turmeric.uniformity.txt
 5f77b83b6c8b54329450a32b34c41f1b *data/narain.sorghum.uniformity.txt
 7e9124d902e03a634b23dc8e9e3b162a *data/nass.barley.txt
 0aa4630b7eff70306df926737ec1a8e8 *data/nass.corn.txt
@@ -278,6 +281,7 @@
 c8830d26c218db3849645414ec79eb17 *data/sawyer.multi.uniformity.txt
 6ab417f3f2f5c840bfe7c819901a87a1 *data/sayer.sugarcane.uniformity.txt
 d5bef918689bb7b5181286641d4c67a1 *data/senshu.rice.txt
+f55ceba1e76dc89660317a4521a9733c *data/shafi.tomato.uniformity.txt
 5899d700afc33efa3d1f138a1c7a2029 *data/shafii.rapeseed.txt
 c9148eafda4ff2cffd3b54fff8f7139e *data/shaw.oats.txt
 8c2a212579198d47fabeb47532dda898 *data/silva.cotton.txt
@@ -340,127 +344,128 @@
 5516ab2a7e8746c82ee2798a4f307bd7 *data/yates.missing.txt
 9217e117bcc1ba550c92c4faec5dff17 *data/yates.oats.txt
 ede96a4d5744c5bfcd26f2495bba4fbd *data/zuidhof.broiler.txt
-957c17fcc7b375b37e8f093de9551ad2 *inst/doc/agridat_data.Rmd
-015a8a4a195b9e3296a2934b3e79caac *inst/doc/agridat_data.html
-2aeab64d1dfdc01c3bb79fb2927d6858 *inst/doc/agridat_examples.R
-e5a7af32b5802ac25af1dd83a0d1418a *inst/doc/agridat_examples.Rmd
-758bf8eb345ee9ff75d3178ca23bc86b *inst/doc/agridat_examples.html
-c95849530c1eff2bab842cbe19e33ce1 *inst/doc/agridat_intro.Rmd
-668834f4b1a40cb78e1257b352d40e7a *inst/doc/agridat_intro.html
+fe5ab5a5fc345f90cb61091174028ef6 *inst/doc/agridat_data.Rmd
+d567705a4156364c08ba25bf6ecb07ad *inst/doc/agridat_data.html
+124ed066330d38f27af3e90be17d0790 *inst/doc/agridat_examples.R
+4b7134b4c9ff36729cd04f08c856f4a8 *inst/doc/agridat_examples.Rmd
+393779d558d19542815b3e78ee4de9e6 *inst/doc/agridat_examples.html
+f1471c46bd8c11117ceb070881007b45 *inst/doc/agridat_intro.Rmd
+d7ada776b0d3709a78bf5c5493d5cb72 *inst/doc/agridat_intro.html
 54ba18ba9f06de9cbddfd276023643ba *inst/files/gartner.corn.dbf
 c742bee3d4edfc2948a2ad08de1790a5 *inst/files/gartner.corn.prj
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 07a3d24a4cd518c5cdc681bccf872f5f *inst/files/gartner.corn.shx
 b479b7623189b08cb27c3fe417a92d98 *inst/files/theobald.covariate.jag
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diff -Nru agridat-1.17/NEWS.md agridat-1.18/NEWS.md
--- agridat-1.17/NEWS.md	2020-07-30 18:43:13.000000000 +0000
+++ agridat-1.18/NEWS.md	2021-01-11 23:07:13.000000000 +0000
@@ -1,3 +1,10 @@
+# agridat 1.18 (Jan 2021)
+
+## New data
+
+damesa.maize, jayaraman.bamboo, nair.turmeric.uniformity, shafi.tomato.uniformity
+
+
 # agridat 1.17 - Jul 2020
 
 ## New data
@@ -31,7 +38,7 @@
 
 ## New data
 
-ansari.wheat.uniformity, baker.wheat.uniformity, bancroft.peanut.uniformity, bose.multi.uniformity, christidis.cotton.uniformity, correa.soybean.uniformity, davies.pasture.uniformity, eden.tea.uniformity, hutchinson.cotton.uniformity, igue.sugarcane.uniformity, kulkarni.sorghum.uniformity, lander.multi.uniformity, lord.rice.uniformity, magistad.pineapple.uniformity, nagai.strawberry.uniformity, narain.sorghum.unifomity, robinson.peanut.uniformity, sayer.sugarcane.uniformity, strickland.apple.uniformity, strickland.grape.uniformity, strickland.peach.uniformity, strickland.tomato.uniformity
+ansari.wheat.uniformity, baker.wheat.uniformity, bancroft.peanut.uniformity, bose.multi.uniformity, christidis.cotton.uniformity, correa.soybean.uniformity, davies.pasture.uniformity, eden.tea.uniformity, hutchinson.cotton.uniformity, igue.sugarcane.uniformity, kulkarni.sorghum.uniformity, lander.multi.uniformity, lord.rice.uniformity, magistad.pineapple.uniformity, nagai.strawberry.uniformity, narain.sorghum.uniformity, robinson.peanut.uniformity, sayer.sugarcane.uniformity, strickland.apple.uniformity, strickland.grape.uniformity, strickland.peach.uniformity, strickland.tomato.uniformity
 
 dasilva.maize, mead.turnip
 
diff -Nru agridat-1.17/vignettes/agridat.bib agridat-1.18/vignettes/agridat.bib
--- agridat-1.17/vignettes/agridat.bib	2019-10-29 17:06:45.000000000 +0000
+++ agridat-1.18/vignettes/agridat.bib	2020-12-11 20:42:38.000000000 +0000
@@ -61,7 +61,7 @@
   pages={129--144},
   year={1997},
   doi={10.1111/1467-9574.00044},
-  url={http://onlinelibrary.wiley.com/doi/10.1111/1467-9574.00044/full}
+  url={https://onlinelibrary.wiley.com/doi/10.1111/1467-9574.00044/full}
 }
 
 @article{laffont2013genotype,
diff -Nru agridat-1.17/vignettes/agridat_data.Rmd agridat-1.18/vignettes/agridat_data.Rmd
--- agridat-1.17/vignettes/agridat_data.Rmd	2020-07-30 20:40:22.000000000 +0000
+++ agridat-1.18/vignettes/agridat_data.Rmd	2021-01-11 21:49:28.000000000 +0000
@@ -13,7 +13,7 @@
 # Books
 
 ### _Die Landwirtschaftlichen Versuchs-Stations_
-http://catalog.hathitrust.org/Record/000549685
+https://catalog.hathitrust.org/Record/000549685
 
 Full view of research station reports 1859-1920. In German.
 
@@ -30,8 +30,8 @@
 ```
 
 ### D. Bayisa (2010). _Application of Spatial Mixed Model in Agricultural Field Experiment_.
-Master thesis. Department of Statistics, Addis Ababa University.
 
+Master thesis. Department of Statistics, Addis Ababa University.
 One dataset from wheat, RCB, with field coordinates.
 
 
@@ -51,15 +51,16 @@
 279 maize covariate, yield & plant count, 4 rep, 32 obs
 ```
 
+
 ### Peter Diggle, Patrick Heagerty, Kung-Yee Liang, Scott Zeger. _Analysis of Longitudinal Data_.
-http://faculty.washington.edu/heagerty/Books/AnalysisLongitudinal/datasets.html
+https://faculty.washington.edu/heagerty/Books/AnalysisLongitudinal/datasets.html
 
 Pig weight data is found in `SemiPar::pig.weights`
 
 Sitka spruce data is found in: `geepack::spruce`
 
 Milk protein data is found in: `nlme::Milk`.  A thorough description of this data can be found in Molenberghs & Kenward, _Missing Data in Clinical Studies_, p. 377.
-Original source: A. P. Verbyla and B. R. Cullis, Modelling in Repeated Measures Experiments. http://www.jstor.org/stable/2347384 
+Original source: A. P. Verbyla and B. R. Cullis, Modelling in Repeated Measures Experiments. https://www.jstor.org/stable/2347384 
 
 
 ### Federer, Walt (1955). _Experimental Design_.
@@ -83,7 +84,7 @@
 
 
 ### Galwey, N.W. (2014). _Introduction to Mixed Modelling_, 2nd ed.
-http://www.wiley.com/WileyCDA/WileyTitle/productCd-1119945496.html
+https://www.wiley.com/WileyCDA/WileyTitle/productCd-1119945496.html
 
 ```
  2  83 variety x nitro split-plot - agridat::yates.oats
@@ -110,11 +111,11 @@
 
 ### Kwanchai A. Gomez & Gomez (1984). _Statistical Procedures for Agricultural Research_.
 
-Extensive collection of datasets from rice experiments.
+Extensive collection of datasets from rice experiments. Many added to agridat.
 
 
 ### Cyril H. Goulden, _Methods of Statistical Analysis_.
-First edition: http://archive.org/details/methodsofstatist031744mbp 
+First edition: https://archive.org/details/methodsofstatist031744mbp 
 
 ```
  18 Uniformity trial - agridat::goulden.barley.uniformity 
@@ -325,7 +326,7 @@
 
 ### Oliver Schabenberger and Francis J. Pierce. _Contemporary Statistical Models for the Plant and Soil Sciences_.
 
-Many datasets
+Many datasets. Some added to agridat.
 
 
 ### S. J. Welham et al. (2015). _Statistical Methods In Biology_.
@@ -334,6 +335,7 @@
 
 
 ### Pesticides in the Nation's Streams and Ground Water, 1992-2001
+
 Extensive data for detection of pesticides in water samples.  See Appendix 5 and Appendix 6 of the supporting info.
 https://water.usgs.gov/nawqa/pnsp/pubs/circ1291/supporting_info.php
 
@@ -345,16 +347,15 @@
 https://data.nal.usda.gov/about-ag-data-commons
 https://data.nal.usda.gov/search/type/dataset
 
-### CyVerse Data Commons
 
-http://datacommons.cyverse.org/
+### CyVerse Data Commons
 
-http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated
+https://datacommons.cyverse.org/
+https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated
 
 
 
 ### DataDryad
-http://datadryad.org/
 
 
 ### Harvard Dataverse
@@ -364,15 +365,14 @@
 https://dataverse.harvard.edu/dataverse/RiceResearch
 
 ### Nature Scientific Data
-http://www.nature.com/sdata/
+https://www.nature.com/sdata/
 
 
 ### Open Data Journal for Agricultural Research
-http://library.wur.nl/ojs/index.php/odjar/
+https://library.wur.nl/ojs/index.php/odjar/
 
 
 ### Plant Genomics and Phenomics Research Data Repository
-https://edal-pgp.ipk-gatersleben.de/
 
 
 ### Wolfram Data Repository 
@@ -382,7 +382,7 @@
 # Journals - Bulletins
 
 ### Iowa State Agricultural Research Bulletins
-http://lib.dr.iastate.edu/ag_researchbulletins/
+https://lib.dr.iastate.edu/ag_researchbulletins/
 ```
 Vol 26/ 281. Cox: Analysis of Lattice and Triple Lattice.
 Page 11: Lattice, 81 hybs, 4 reps 
@@ -430,14 +430,14 @@
 
 **Xavier, Alencar et al.**.
 Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population,
-http://www.g3journal.org/content/8/2/519
+https://www.g3journal.org/content/8/2/519
 
 Data are in the SoyNAM and NAM packages.
 
 **Barrero, Ivan D. et al**. (2013).
 A multi-environment trial analysis shows slight grain yield improvement in Texas commercial maize.
 Field Crops Research, 149, Pages 167-176.
-http://doi.org/10.1016/j.fcr.2013.04.017
+https://doi.org/10.1016/j.fcr.2013.04.017
 
 This is a large (14500 records), multi-year, multi-location, 10-trait data. Sent a note encouraging the authors to formally publish the data.
 Source: http://maizeandgenetics.tamu.edu/CTP/CTP.html
@@ -446,14 +446,14 @@
 **Cleveland, M.A. and John M. Hickey, Selma Forni** (2012).
 A Common Dataset for Genomic Analysis of Livestock Populations.
 G3, 2, 429-435.
-http://doi.org/10.1534/g3.111.001453
+https://doi.org/10.1534/g3.111.001453
 
 The supplemental information for this paper contains data for 3534 pigs with high-density genotypes (50000 SNPs), and a pedigree including parents and grandparents of the animals.
 
 
 **Daillant-Spinnler** (1996). Relationships between perceived sensory properties and major preference directions of 12 variaties of apples from the southern hemisphere.
 Food Quality and Preference, 7(2), 113-126.
-http://dx.doi.org/10.1016/0950-3293(95)00043-7
+https://dx.doi.org/10.1016/0950-3293(95)00043-7
 
 The data are in `ClustVarLV::apples_sh$pref` and `ClustVarLV::apples_sh$senso`
 12 apple varieties, 43 traits, 60 consumers
@@ -481,7 +481,7 @@
 **Monteverde et al**
 Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (Oryza sativa L.) Grown in Subtropical Areas
 https://doi.org/10.1534/g3.119.400064 
-https://gsajournals.figshare.com/articles/Supplemental_Material_for_Monteverde_et_al_2019/7685636
+https://gsajournals.figshare.com/articles/dataset/Supplemental_Material_for_Monteverde_et_al_2019/7685636
 
 Supplemental information contains phenotypic data and markers and environmental covariates for PLS analysis.
 
@@ -503,7 +503,7 @@
 
 **Klumper & Qaim** (2015).
 A Meta-Analysis of the Impacts of Genetically Modified Crops.
-http://doi.org/10.1371/journal.pone.0111629
+https://doi.org/10.1371/journal.pone.0111629
 
 Nice meta-analysis dataset.  Published data only include differences, not standard-errors.
 See the comments on PLOS article for some peculiarities in the data.
@@ -511,7 +511,7 @@
 
 **Lado, B. et al.** (2013). 
 *Increased Genomic Prediction Accuracy in Wheat Breeding Through Spatial Adjustment of Field Trial Data*.  G3, 3, 2105-2114.
-http://doi.org/10.1534/g3.113.007807
+https://doi.org/10.1534/g3.113.007807
 
 Has a large haplotype dataset (83 MB) and two-year phenotype data with multiple traits.
 
@@ -519,7 +519,7 @@
 **Payne, Roger** (2015). 
 The Design and Analysis of Long-Term Rotation Experiments.
 Agronomy Journal, 107, 772-784.
-http://doi.org/10.2134/agronj2012.0411
+https://doi.org/10.2134/agronj2012.0411
 
 The data and R code appeared in the paper. Free access, but closed copyright.
 
@@ -527,7 +527,7 @@
 **Snedecor, George and E. S. Haber** (1946).
 Statistical Methods For an Incomplete Experiment on a Perennial Crop.
 Biometrics Bulletin, 2, 61-67.
-http://doi.org/10.2307/3001959
+https://doi.org/10.2307/3001959
 
 Harvest of asparagus over 10 years, three cutting dates per year, 6 blocks.
 
@@ -535,20 +535,20 @@
 **Technow, Frank, et al.** (2014).
 Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize.
 August 1, 2014 vol. 197 no. 4 1343-1355.
-http://doi.org/10.1534/genetics.114.165860
+https://doi.org/10.1534/genetics.114.165860
 
 Genotype and phenotype data appears in the sommer package.
 
 
 **Tian, Ting** (2015).
 Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data.
-http://doi.org/10.1371/journal.pone.0144370
+https://doi.org/10.1371/journal.pone.0144370
 
 Uses `agridat::australia.soybean` data and one other real dataset with 4 traits that are not identified.  All data and code available.
 
 
 **Randall J. Wisser et al.** (2011). 
-Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene. PNAS.  http://doi.org/10.1073/pnas.1011739108
+Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene. PNAS.  https://doi.org/10.1073/pnas.1011739108
 
 The supplement contains genotype data, but no phenotype data.
 
@@ -656,6 +656,11 @@
 
 Data `gRbase::carcass`: thickness of meat and fat on slaughter pigs
 
+
+### lmDiallel
+https://github.com/OnofriAndreaPG/lmDiallel/tree/master/data
+
+
 ### lmtest
 
 Data `lmtest::ChickEgg` time series of annual chicken and egg production in the United States 1930-1983.
@@ -667,10 +672,10 @@
 
 
 ### nlraa
-http://r-forge.r-project.org/R/?group_id=1599
 
 Miguez. Non-linear models in agriculture.
 `nlraa::sm` = `agridat::miguez.biomass`
+Vignettes and functions for working with (non)linear mixed models
 
    
 ### nlme
@@ -712,14 +717,27 @@
 
 Data: h2. Modest-sized GxE experiment in potato Data: cornHybrid. Yield/PLTHT for 100 hybrids from 20 inbred * 20 inbred, 4 locs. Phenotype and relationship matrix.
 
-Data: wheatLines  CIMMYT wheat data for 599 lines. Phenotype and relationship data.
+Data: 
+```
+data(DT_wheat) #  CIMMYT wheat data
+DT_wheat # 599 varieties, yield in 4 envts
+GT_wheat # 599 varieties, 1279 markers coded -1,1
+```
 
 Data: RICE
 
 Data: FDdata taken from agridat::bond.diallel
 
-Data: Technow_data. AF=Additive Flint. AD=Additive Dent. MF=Marker Flint. MD=Marker Dent. pheno=phenotype data for 1254 hybrids (GY=yield, GM=moisture).  This data is from Technow et al:
-http://www.genetics.org/content/197/4/1343.supplemental
+Data:
+```
+data(DT_technow) # From http://www.genetics.org/content/197/4/1343.supplemental
+DT <- DT_technow  # 1254 hybs, parents, GY=yield, GM=moisture
+Md <- Md_technow  # 123 dent parents, 35478 markers
+Mf <- Mf_technow  # 86 flint parents, 37478 markers
+Ad <- Ad_technow  # 123 x 123 A matrix 
+Af <- Af_technow  # 86 x 85 A matrix
+```
+
 
 
 ### SoyNAM - Soybean nested association mapping 
@@ -766,12 +784,7 @@
 # Web sites
 
 ### ARS oat trials
-http://www.ars.usda.gov/Main/docs.htm?docid=8419&page=4
-
-
-### BETYdb
-https://www.betydb.org/
-Biofuel Ecophysiological Traits and Yields Database
+https://www.ars.usda.gov/Main/docs.htm?docid=8419&page=4
 
 
 ### CIMMYT Research Data
@@ -782,7 +795,7 @@
 ### Genomes To Fields (G2F) 
 https://www.genomes2fields.org/publications/
 
-http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3
+https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3
     
 Very large GxE data here for 2014 and 2015. Hybrid & inbred phenotype data, weather data, genomic data.  Very nice.
 
@@ -796,7 +809,7 @@
 
 
 ### Google dataset search
-https://toolbox.google.com/datasetsearch
+https://datasetsearch.research.google.com/
 
 
 ### Grain genes
@@ -804,7 +817,7 @@
 1. https://wheat.pw.usda.gov/ggpages/HxT/
 The Harrington x TR306 Barley Mapping Population. The genotype and phenotype data comes from Mapmaker, but seems to be in a slightly non-standard format; 145 DH lines, 217 markers, 25 env, 1 rep.
 
-2. https://wheat.pw.usda.gov/ggpages/SxM. This data is agridat::steptoe.morex.
+2. https://wheat.pw.usda.gov/ggpages/SxM/ . This data is agridat::steptoe.morex.
 
 
 
@@ -813,10 +826,6 @@
 Data File : Raw data from each ear analyzed each year of the Illinois long-term selection experiment for oil and protein in corn (1896-2004)
 
 
-### Illinois Corn Hybrid Variety Trials
-http://vt.cropsci.illinois.edu/corn.html
-
-
 ### ILRI International Livestock Research Institute
 
 Case study 4 is a nice diallel example with sheep data.
@@ -837,8 +846,6 @@
 http://www.era.rothamsted.ac.uk/index.php
 Data from Broadbalk and other long-term experiments.
 
-Twitter: https://twitter.com/eRA_Curator
-
 Github draft data: https://github.com/Rothamsted-Ecoinformatics/YieldbookDatasetDrafts
 
 
@@ -885,15 +892,15 @@
 
 
 ### Terra-Ref
-http://terraref.org/
+https://terraref.org/
 
 Sensor observations, plant phenotypes, derived traits, genetic and genomic data.
 Beta version until Nov 2018.
 
 
 ### USDA National Agricultural Statistics Service
-http://www.nass.usda.gov
-http://quickstats.nass.usda.gov/
+https://www.nass.usda.gov
+https://quickstats.nass.usda.gov/
 
 Group:       Field Crops
 Commodity:   Corn
diff -Nru agridat-1.17/vignettes/agridat_examples.Rmd agridat-1.18/vignettes/agridat_examples.Rmd
--- agridat-1.17/vignettes/agridat_examples.Rmd	2019-11-25 18:11:28.000000000 +0000
+++ agridat-1.18/vignettes/agridat_examples.Rmd	2020-12-11 20:13:19.000000000 +0000
@@ -22,23 +22,21 @@
 knitr::opts_chunk$set(echo=FALSE, fig.height = 5, fig.width = 5)
 options(width=90)
 ```
-This exhibit of agricultural data uses the following packages.
-
-```{r packs, eval=TRUE, message=FALSE, echo=TRUE}
-library("agridat")
-library("desplot")
-library("gge")
-library("HH")
-library("lattice")
-library("latticeExtra")
-library("mapproj")
-library("maps")
-library("reshape2")
-```
+This exhibit of agricultural data uses the following packages: 
+`agridat`,
+`desplot`,
+`gge`,
+`HH`,
+`lattice`,
+`latticeExtra`,
+`mapproj`,
+`maps`,
+`reshape2`.
 
 # Potato blight incidence over space and time
 
 ```{r lee1, eval=TRUE, fig.height=7.5, fig.width=5}
+library(agridat)
 data(lee.potatoblight)
 dat <- lee.potatoblight
 # Note the progression to lower scores as time passes in each year
@@ -52,11 +50,12 @@
          rep(0,5),1,1,1,1,1,
          rep(0,5),1,1,1,1,1,
          rep(0,5),1,1,1,1,1)
-require(desplot)
-desplot(y ~ col*row|date, dat,
-        main="lee.potatoblight", #col.regions=RedGrayBlue,
-        between=list(y=.3), strip.cex =.6,
-        layout=c(10,11), skip=as.logical(skp))
+if(require("desplot")){
+  desplot(dat, y ~ col*row|date,
+          main="lee.potatoblight", #col.regions=RedGrayBlue,
+          between=list(y=.3), strip.cex =.6,
+          layout=c(10,11), skip=as.logical(skp))
+}
 ```
 
 @lee2009random analyzed a large dataset to evaluate the resistance of potato varieties to blight.  This data contains evaluations of a changing set of varieties every two years, evaluated in 5 blocks, repeatedly throughout the growing season to track the progress of the disease.  Each panel shows a field map on the given date, with a separate row of panels for each year.
@@ -65,18 +64,21 @@
 
 
 ```{r lee2, eval=TRUE}
+library(agridat)
 # 1983 only.  I.Hardy succumbs quickly
 dat <- lee.potatoblight
 dat$dd <- as.Date(dat$date)
 d83 <- droplevels(subset(dat, year==1983))
-foo <- xyplot(y ~ dd|gen, d83, group=rep,
-       xlab="Date", ylab="Blight resistance score",
-       main="lee.potatoblight 1983", as.table=TRUE,
-       par.settings=list(
-         superpose.symbol=list(col=c("black","red","royalblue","#009900","dark orange"),
-           pch=c("1","2","3","4","5"))),
-       scales=list(alternating=FALSE, x=list(rot=90, cex=.7)))
-foo + xyplot(y ~ dd|gen, d83, subset=year==1983, type='smooth', col='gray80')
+if(require("latticeExtra")){
+  foo <- xyplot(y ~ dd|gen, d83, group=rep,
+         xlab="Date", ylab="Blight resistance score",
+         main="lee.potatoblight 1983", as.table=TRUE,
+         par.settings=list(
+           superpose.symbol=list(col=c("black","red","royalblue","#009900","dark orange"),
+             pch=c("1","2","3","4","5"))),
+         scales=list(alternating=FALSE, x=list(rot=90, cex=.7)))
+  foo + xyplot(y ~ dd|gen, d83, subset=year==1983, type='smooth', col='gray80')
+}
 ```
 
 In 1983, 20 varieties were evaluated in 5 blocks (shown by colored numbers) throughout the growing season for disease resistance. Resistance scores start at 9 for all varieties (shown in panels).  As the growing season progresses, the 'I.HARDY' variety succumbs quickly to blight, while 'IWA' succumbs steadily, and '064.1' resists blight until near the end of the season.
@@ -88,6 +90,7 @@
 # An informative prior
 
 ```{r harrison, eval=TRUE, fig.height=6}
+library(agridat)
 data(harrison.priors)
 d1 <- subset(harrison.priors, substance=="daidzein")
 d1 <- d1[ , c("source","number","min","max")]
@@ -102,32 +105,33 @@
   out <- rbind(out, data.frame(source=d1[ii,'source'], vals=vals))
 }
 out <- droplevels(out) # Extra levels exist in d1
-foo0 <- dotplot(source ~ vals, out, 
-                main="harrison.priors", xlab="Daidzein level",
+if(require("latticeExtra")) {
+  foo0 <- dotplot(source ~ vals, out, 
+                  main="harrison.priors", xlab="Daidzein level",
+                  panel=function(x,y,...){
+                    panel.dotplot(x,y,...)
+                    #browser()
+                    # Minimum for each row
+                    x2l <- tapply(x, y, min)
+                    x2r <- tapply(x, y, max)
+                    y2 <- tapply(y, y, "[", 1)
+                    panel.xyplot(x2l, y2, pch=16, cex=1.5, col="navy")
+                    panel.xyplot(x2r, y2, pch=16, cex=1.5, col="navy")
+                  },
+                  # Hack.  Add blanks for extra space on graph
+                  ylim=c(levels(out$source),"","","","prior","Constructed","",""))
+  
+  # Now calculate parameters for a common lognormal distribution
+  mu0 <- mean(log(out$vals))
+  sd0 <- sd(log(out$vals))
+  xvals <- seq(0,2000, length=100)
+  library("latticeExtra")
+  foo0 + xyplot((19+4000*dlnorm(xvals, mu0, sd0))~xvals, type='l',
                 panel=function(x,y,...){
-                  panel.dotplot(x,y,...)
-                  #browser()
-                  # Minimum for each row
-                  x2l <- tapply(x, y, min)
-                  x2r <- tapply(x, y, max)
-                  y2 <- tapply(y, y, "[", 1)
-                  panel.xyplot(x2l, y2, pch=16, cex=1.5, col="navy")
-                  panel.xyplot(x2r, y2, pch=16, cex=1.5, col="navy")
-                },
-                # Hack.  Add blanks for extra space on graph
-                ylim=c(levels(out$source),"","","","prior","Constructed","",""))
-
-# Now calculate parameters for a common lognormal distribution
-mu0 <- mean(log(out$vals))
-sd0 <- sd(log(out$vals))
-xvals <- seq(0,2000, length=100)
-library("latticeExtra")
-foo0 + xyplot((19+4000*dlnorm(xvals, mu0, sd0))~xvals, type='l',
-              panel=function(x,y,...){
-                panel.xyplot(x,y,...)
-                panel.abline(h=19, col="gray90")
-              })
-
+                  panel.xyplot(x,y,...)
+                  panel.abline(h=19, col="gray90")
+                })
+}
 ```
 
 @harrison2012bayesian used a Bayesian approach to model daidzein levels in soybean samples.  From 18 previous publications, they extracted the published minimum and maximum daidzein levels, and the number of samples tested.  Each line in the dotplot shows large, dark dots for one published minimum and maximum.  The small dots are imputed using a lognormal distribution.
@@ -142,6 +146,7 @@
 # Data densities for a binomial GLM
 
 ```{r mead, eval=TRUE}
+library(agridat)
 data(mead.germination)
 dat <- mead.germination
 # dat <- transform(dat, concf=factor(conc))
@@ -160,40 +165,39 @@
 newb <- expand.grid(temp=c('T1','T2','T3','T4'), logconc=log(c(0,.1,1,10)+.01))
 newb$pct <- predict(m6, new=newb, type='response')
 # Binomial density
-foob <- xyplot(pct~logconc |temp, newb,
-               xlim=c(-5.5, 4.5), ylim=c(-2, 53), as.table=TRUE,
-               xlab="Log concentration",
-               ylab="Seeds germinating (out of 50).  Binomial density.",
-               main="mead.germination", #layout=c(4,1),
-               panel=function(x,y,...){
-                 for(ix in 1:4){
-                   quan <- qbinom(c(.025, .975), size=50, prob=y[ix])
-                   yval <- seq(min(quan), max(quan), by=1)
-                   off <- x[ix]
-                   xl <- off + rep(0, length(yval))
-                   # Constant multiuplier of 8 chosen by trial and error
-                   xr <- off + 8 * dbinom(yval, size=50, prob=y[ix])
-                   panel.segments(xl,yval,xr, yval, cex=.35, lwd=3, col="gray70")
-                 }
-               })
-
-
-# Add mean response line with equally-spaced points on the log scale
-newl <- expand.grid(temp=c('T1','T2','T3','T4'),
-                   logconc=seq(log(.01), log(10.01), length=50))
-newl$pct <- predict(m6, new=newl, type='response')
-# Logistic curve
-fool <- xyplot(pct~logconc|temp, newl,
-               panel=function(x,y,...){
-                 panel.points(x, 50*y, type='l', col='blue')
-               })
-
-
-# Data points last, on top of everything
-food <- xyplot(germ~logconc|temp, dat, layout=c(4,1),
-       ylab="Seeds germinating (out of 50)", cex=1.5, pch=20, col='black')
-foob + fool + food
-
+if(require("latticeExtra")){
+  foob <- xyplot(pct~logconc |temp, newb,
+                 xlim=c(-5.5, 4.5), ylim=c(-2, 53), as.table=TRUE,
+                 xlab="Log concentration",
+                 ylab="Seeds germinating (out of 50).  Binomial density.",
+                 main="mead.germination", #layout=c(4,1),
+                 panel=function(x,y,...){
+                   for(ix in 1:4){
+                     quan <- qbinom(c(.025, .975), size=50, prob=y[ix])
+                     yval <- seq(min(quan), max(quan), by=1)
+                     off <- x[ix]
+                     xl <- off + rep(0, length(yval))
+                     # Constant multiuplier of 8 chosen by trial and error
+                     xr <- off + 8 * dbinom(yval, size=50, prob=y[ix])
+                     panel.segments(xl,yval,xr, yval, cex=.35, lwd=3, col="gray70")
+                   }
+                 })
+  
+  # Add mean response line with equally-spaced points on the log scale
+  newl <- expand.grid(temp=c('T1','T2','T3','T4'),
+                     logconc=seq(log(.01), log(10.01), length=50))
+  newl$pct <- predict(m6, new=newl, type='response')
+  # Logistic curve
+  fool <- xyplot(pct~logconc|temp, newl,
+                 panel=function(x,y,...){
+                   panel.points(x, 50*y, type='l', col='blue')
+                 })
+  
+  # Data points last, on top of everything
+  food <- xyplot(germ~logconc|temp, dat, layout=c(4,1),
+         ylab="Seeds germinating (out of 50)", cex=1.5, pch=20, col='black')
+  foob + fool + food
+}
 ```
 
 @mead2002statistical present data for germination of seeds under four temperatures (T1-T4) and four chemical concentrations.  For each of the 4*4=16 treatments, 50 seeds were tested in each of four reps.  In the graphic, each point is one rep. The blue line is a fitted curve from a GLM with Temperature as a factor and log concentration as a covariate.  The gray lines show the central 95 percent of the binomial density at that position.
@@ -205,14 +209,16 @@
 # Verification of experiment layout
 
 ```{r gomez, eval=TRUE}
+library(agridat)
 data(gomez.stripsplitplot)
 dat <- gomez.stripsplitplot
 
 # Layout
-require(desplot)
-desplot(gen~col*row, dat,
-        out1=rep, col=nitro, text=planting, cex=1,
-        main="gomez.stripsplitplot")
+if(require("desplot")){
+  desplot(dat, gen~col*row,
+          out1=rep, col=nitro, text=planting, cex=1,
+          main="gomez.stripsplitplot")
+}
 ```
 
 @gomez1984statistical provide data for an experiment with 3 reps, 6 genotypes, 3 levels of nitrogen and 2 planting dates.  The experiment layout is putatively a ''split strip-plot''.  To verify the design, the `desplot` package is used for plotting the design of field experiments.
@@ -224,19 +230,21 @@
 # Visualizing main effects, two-way interactions
 
 ```{r gomez2, eval=TRUE}
+library(agridat)
 data(gomez.splitsplit)
 dat <- gomez.splitsplit
 dat$nitrogen <- factor(dat$nitro)
-require(HH)
-#position(dat$rep) <- position(dat$management) <-
-#  position(dat$gen) <- c(10,70,130)
-#position(dat$nitrogen) <- c(0,50,80,110,140)
-interaction2wt(yield~rep+nitrogen+management+gen, data=dat,
-               main="gomez.splitsplit",
-               x.between=0, y.between=0,
-               relation=list(x="free", y="same"),
-               rot=c(90,0), xlab="",
-               par.strip.text.input=list(cex=.8))
+if(require("HH")){
+  #position(dat$rep) <- position(dat$management) <-
+  #  position(dat$gen) <- c(10,70,130)
+  #position(dat$nitrogen) <- c(0,50,80,110,140)
+  interaction2wt(yield~rep+nitrogen+management+gen, data=dat,
+                 main="gomez.splitsplit",
+                 x.between=0, y.between=0,
+                 relation=list(x="free", y="same"),
+                 rot=c(90,0), xlab="",
+                 par.strip.text.input=list(cex=.8))
+}
 ```
 
 @heiberger2004statistical provide an interesting way to use lattice graphics to visualize the main effects (using boxplots) and interactions (using interaction plots) in data.  Rice yield is plotted versus replication, nitrogen, management type, and genotype variety.  Box plots show minor differences between reps, increaing yield due to nitrogen, high yield from intensive management, and large differences between varieties.
@@ -266,6 +274,7 @@
 # Mosaic plot of potato damage from harvesting
 
 ```{r keen, eval=TRUE, fig.width=7, fig.height=7.5}
+library(agridat)
 data(keen.potatodamage)
 dat <- keen.potatodamage
 
@@ -288,51 +297,53 @@
 # Yield vs covariate for lattice::barley
 
 ```{r wright, eval=TRUE}
+library(agridat)
 data(minnesota.barley.yield)
 data(minnesota.barley.weather)
 dat <- minnesota.barley.yield
 datw <- minnesota.barley.weather
 
 # Weather trends over time
-library("latticeExtra")
-#useOuterStrips(xyplot(cdd~mo|year*site, datw, groups=year,
-#main="minnesota.barley", xlab="month", ylab="Cooling degree days",
-#subset=(mo > 3 & mo < 10), scales=list(alternating=FALSE),
-#type='l', auto.key=list(columns=5)))
-
-# Total cooling/heating/precip in Apr-Aug for each site/yr
-ww <- subset(datw, mo>=4 & mo<=8)
-ww <- aggregate(cbind(cdd,hdd,precip)~site+year, data=ww, sum)
-
-# Average yield per each site/env
-yy <- aggregate(yield~site+year, dat, mean)
-
-minn <- merge(ww, yy)
-
-
-# Higher yields generally associated with cooler temps, more precip
-library("reshape2")
-me <- melt(minn, id.var=c('site','year'))
-mey <- subset(me, variable=="yield")
-mey <- mey[,c('site','year','value')]
-names(mey) <- c('site','year','y')
-mec <- subset(me, variable!="yield")
-names(mec) <- c('site','year','covar','x')
-mecy <- merge(mec, mey)
-mecy$yr <- factor(mecy$year)
-oldpar <- tpg <- trellis.par.get()
-tpg$superpose.symbol$pch <- substring(levels(mecy$yr),4) # Last digit of year
-trellis.par.set(tpg)
-foo <- xyplot(y~x|covar*site, data=mecy, groups=yr, cex=1, ylim=c(5,65),
-              xlab="Weather covariate", ylab="Barley yield",
-              main="minnesota.barley",
-              panel=function(x,y,...) {
-                panel.lmline(x,y,..., col="gray")
-                panel.superpose(x,y,...)
-              },
-              scales=list(x=list(relation="free")))
-foo <- useOuterStrips(foo, strip.left = strip.custom(par.strip.text=list(cex=.7)))
-combineLimits(foo, margin.x=2L)
+if(require("latticeExtra")){
+  #useOuterStrips(xyplot(cdd~mo|year*site, datw, groups=year,
+  #main="minnesota.barley", xlab="month", ylab="Cooling degree days",
+  #subset=(mo > 3 & mo < 10), scales=list(alternating=FALSE),
+  #type='l', auto.key=list(columns=5)))
+    
+  # Total cooling/heating/precip in Apr-Aug for each site/yr
+  ww <- subset(datw, mo>=4 & mo<=8)
+  ww <- aggregate(cbind(cdd,hdd,precip)~site+year, data=ww, sum)
+    
+  # Average yield per each site/env
+  yy <- aggregate(yield~site+year, dat, mean)
+    
+  minn <- merge(ww, yy)
+    
+  
+  # Higher yields generally associated with cooler temps, more precip
+  library("reshape2")
+  me <- melt(minn, id.var=c('site','year'))
+  mey <- subset(me, variable=="yield")
+  mey <- mey[,c('site','year','value')]
+  names(mey) <- c('site','year','y')
+  mec <- subset(me, variable!="yield")
+  names(mec) <- c('site','year','covar','x')
+  mecy <- merge(mec, mey)
+  mecy$yr <- factor(mecy$year)
+  oldpar <- tpg <- trellis.par.get()
+  tpg$superpose.symbol$pch <- substring(levels(mecy$yr),4) # Last digit of year
+  trellis.par.set(tpg)
+  foo <- xyplot(y~x|covar*site, data=mecy, groups=yr, cex=1, ylim=c(5,65),
+                xlab="Weather covariate", ylab="Barley yield",
+                main="minnesota.barley",
+                panel=function(x,y,...) {
+                  panel.lmline(x,y,..., col="gray")
+                  panel.superpose(x,y,...)
+                },
+                scales=list(x=list(relation="free")))
+  foo <- useOuterStrips(foo, strip.left = strip.custom(par.strip.text=list(cex=.7)))
+  combineLimits(foo, margin.x=2L)
+}
 ```
 
 @wright2013revisiting investigated the `lattice::barley` data.  The original two years of data were extended to 10 years (from original source documents), and supplemented with weather covariates for the 6 locations and 10 years.  Each panel shows a scatterplot and regression for average location yield verses the weather covariate.  Horizontal strips are for locations, vertical strips are for covariates: cdd = Cooling Degree Days, hdd = Heating Degree Days, precip = Precipitation).  Higher values of heating imply cooler weather.  Each plotting symbol is the last digit of the year (1927-1936) for that location.
@@ -344,14 +355,15 @@
 # GGE biplot
 
 ```{r crossa, eval=FALSE, message=FALSE}
-
+library(agridat)
 # Specify env.group as column in data frame
 data(crossa.wheat)
 dat2 <- crossa.wheat
-require(gge)
-m4 <- gge(yield~gen*loc, dat2, env.group=locgroup, scale=FALSE)
-# plot(m4)
-biplot(m4, lab.env=TRUE, main="crossa.wheat")
+if(require("gge")){
+  m4 <- gge(yield~gen*loc, dat2, env.group=locgroup, scale=FALSE)
+  # plot(m4)
+  biplot(m4, lab.env=TRUE, main="crossa.wheat")
+}
 ```
 
 @laffont2013genotype developed a variation of the GGE (genotype plus genotype-by-environment) biplot to include auxiliary information about a block/group of environments.  Each location is classified into one of two mega-environments (colored).  The mosaic plots partition variation simultaneously by principal component axis and source (genotype, genotype-by-block, residual).
@@ -363,24 +375,25 @@
 # Nebraska farming income choropleth
 
 ```{r nebr1, eval=TRUE}
-library("maps")
-library("mapproj")
-library("latticeExtra")
 
+library(agridat)
 data(nebraska.farmincome)
 dat <- nebraska.farmincome
 dat$stco <- paste0('nebraska,', dat$county)
 dat <- transform(dat, crop=crop/1000, animal=animal/1000)
 
-# Raw, county-wide incomes.  Note the outlier Cuming county
-redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-mapplot(stco ~ crop + animal, data = dat,
-        scales = list(draw = FALSE),
-        main="nebraska.farmincome",
-        xlab="", ylab="Income ($1000) per county",
-        colramp=redblue,
-        map = map('county', 'nebraska', plot = FALSE, fill = TRUE,
-                  projection = "mercator"))
+if(require("maps") & require("mapproj") & require("latticeExtra")){
+
+  # Raw, county-wide incomes.  Note the outlier Cuming county
+  redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
+  mapplot(stco ~ crop + animal, data = dat,
+          scales = list(draw = FALSE),
+          main="nebraska.farmincome",
+          xlab="", ylab="Income ($1000) per county",
+          colramp=redblue,
+          map = map('county', 'nebraska', plot = FALSE, fill = TRUE,
+                    projection = "mercator"))
+}
 
 ```
 
@@ -396,6 +409,7 @@
 # Now scale to income/mile^2
 dat <- transform(dat, crop.rate=crop/area, animal.rate=animal/area)
 # And use manual breakpoints.
+if(require("maps") & require("mapproj") & require("latticeExtra")){
 mapplot(stco ~ crop.rate + animal.rate, data = dat,
         scales = list(draw = FALSE),
         main="nebraska.farmincome",
@@ -409,9 +423,10 @@
         #breaks=classIntervals(na.omit(c(dat$crop.rate, dat$animal.rate)), n=7, style='fisher')$brks
         breaks=c(0,.049, .108, .178, .230, .519, .958, 1.31)
         )
+}
 ```
 
-Because counties are different sizes, the second graphic uses an income rate per square mile.  Because of the outlier, it might be smart to use percentile break points, but doing so hides the outlier.  Instead, the break points are calulated using a method called Fisher-Jenks.  These break points show both the outlier and the spatial patterns.  It is now easy to see that northwest (Sandhills) Nebraska has low farming income, especially for crops.  Counties with missing data are white, which is easily distinguished from gray.
+Because counties are different sizes, the second graphic uses an income rate per square mile.  Because of the outlier, it might be smart to use percentile break points, but doing so hides the outlier.  Instead, the break points are calculated using a method called Fisher-Jenks.  These break points show both the outlier and the spatial patterns.  It is now easy to see that northwest (Sandhills) Nebraska has low farming income, especially for crops.  Counties with missing data are white, which is easily distinguished from gray.
 
 Where are farm incomes highest?  Why?
 
@@ -420,72 +435,72 @@
 # Las Rosas yield monitor
 
 ```{r lasrosas, eval=TRUE, fig.height=7.5}
-
+library(agridat)
 data(lasrosas.corn)
 dat <- lasrosas.corn
-library("latticeExtra")
 
 # yield map
 redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
-foo1 <- levelplot(yield ~ long*lat|factor(year), data=dat,
-          aspect=1, layout=c(2,1),
-          main="lasrosas.corn grain yield (qu/ha)", xlab="Longitude", ylab="Latitude",
-          scales=list(alternating=FALSE),
-          prepanel = prepanel.default.xyplot,
-          panel = panel.levelplot.points,
-          type = c("p", "g"), col.regions=redblue)
-
-# Experiment design...shows problems in 2001
-dat <- lasrosas.corn
-
-xl <- range(dat$long)
-yl <- range(dat$lat)
-
-sseq=matrix(c(
-  35, 0.9, 0.5,  # brown
-  35, 0.8, 0.6,
-  35, 0.7, 0.7,
-  35, 0.6, 0.8,
-  35, 0.5, .9,
-  35, 0.4, 1,
-  80, 0.9, 0.5,  # green
-  80, 0.8, 0.6,
-  80, 0.7, 0.7,
-  80, 0.6, 0.8,
-  80, 0.5, 0.9,
-  80, 0.4, 1,
-  190, 0.9, 0.5,  # blue
-  190, 0.8, 0.6,
-  190, 0.7, 0.7,
-  190, 0.6, 0.8,
-  190, 0.5, 0.9,
-  190, 0.4, 1
-  ), ncol=3, byrow=TRUE)
-sseq <- hsv(sseq[,1]/360, sseq[,2], sseq[,3])
-
-dat$repnf <- factor(paste(dat$rep,dat$nf))
-# levels(dat$repnf) # check the order
-#dat <- transform(dat, col=as.character(sseq[as.numeric(factor(paste(dat$rep,dat$nf)))]))
-
-# By default, manual specification of col/pch does not work with multiple panels.
-# Define a custom panel function to make it work
-mypanel <- function(x,y,...,subscripts,col,pch) {
-  panel.xyplot(x,y,col=col[subscripts],pch=pch[subscripts], ...)
-}
-
-foo2 <- xyplot(lat~long|factor(year), data=dat,
-               aspect=1, layout=c(2,1),
-               xlim=xl, ylim=yl, cex=0.9,
-               main="lasrosas.corn experiment design", xlab="", ylab="",
-               scales=list(alternating=FALSE),
-               col=sseq[dat$repnf],
-               #pch=levels(dat$topo)[dat$topo],
-               pch=c('-','+','/','\\')[dat$topo],
-               panel=mypanel)
-
-plot(foo1, split = c(1, 1, 1, 2))
-plot(foo2, split = c(1, 2, 1, 2), newpage = FALSE)
-
+if(require("latticeExtra")){
+  foo1 <- levelplot(yield ~ long*lat|factor(year), data=dat,
+            aspect=1, layout=c(2,1),
+            main="lasrosas.corn grain yield (qu/ha)", xlab="Longitude", ylab="Latitude",
+            scales=list(alternating=FALSE),
+            prepanel = prepanel.default.xyplot,
+            panel = panel.levelplot.points,
+            type = c("p", "g"), col.regions=redblue)
+  
+  # Experiment design...shows problems in 2001
+  dat <- lasrosas.corn
+  
+  xl <- range(dat$long)
+  yl <- range(dat$lat)
+
+  sseq=matrix(c(
+    35, 0.9, 0.5,  # brown
+    35, 0.8, 0.6,
+    35, 0.7, 0.7,
+    35, 0.6, 0.8,
+    35, 0.5, .9,
+    35, 0.4, 1,
+    80, 0.9, 0.5,  # green
+    80, 0.8, 0.6,
+    80, 0.7, 0.7,
+    80, 0.6, 0.8,
+    80, 0.5, 0.9,
+    80, 0.4, 1,
+    190, 0.9, 0.5,  # blue
+    190, 0.8, 0.6,
+    190, 0.7, 0.7,
+    190, 0.6, 0.8,
+    190, 0.5, 0.9,
+    190, 0.4, 1
+    ), ncol=3, byrow=TRUE)
+  sseq <- hsv(sseq[,1]/360, sseq[,2], sseq[,3])
+  
+  dat$repnf <- factor(paste(dat$rep,dat$nf))
+  # levels(dat$repnf) # check the order
+  #dat <- transform(dat, col=as.character(sseq[as.numeric(factor(paste(dat$rep,dat$nf)))]))
+  
+  # By default, manual specification of col/pch does not work with multiple panels.
+  # Define a custom panel function to make it work
+  mypanel <- function(x,y,...,subscripts,col,pch) {
+    panel.xyplot(x,y,col=col[subscripts],pch=pch[subscripts], ...)
+  }
+  
+  foo2 <- xyplot(lat~long|factor(year), data=dat,
+                 aspect=1, layout=c(2,1),
+                 xlim=xl, ylim=yl, cex=0.9,
+                 main="lasrosas.corn experiment design", xlab="", ylab="",
+                 scales=list(alternating=FALSE),
+                 col=sseq[dat$repnf],
+                 #pch=levels(dat$topo)[dat$topo],
+                 pch=c('-','+','/','\\')[dat$topo],
+                 panel=mypanel)
+  
+  plot(foo1, split = c(1, 1, 1, 2))
+  plot(foo2, split = c(1, 2, 1, 2), newpage = FALSE)
+}
 ```
 
 @anselin2004spatial and @lambert2004comparison looked at yield monitor data collected from a corn field in Argentina in 1999 and 2001, to see how yield was affected by field topography and nitrogen fertilizer.  The figures here show heatmaps for the yield each year, and also the experiment design (colors are reps, shades of color are nitrogen level, plotting character is topography).
@@ -497,6 +512,7 @@
 # Time series of corn yields by state
 
 ```{r nass, eval=TRUE, fig.height=8}
+library(agridat)
 data(nass.corn)
 dat <- nass.corn
 dat$acres <- dat$acres/1000000
@@ -509,6 +525,7 @@
 dat <- subset(dat, state != "California")
 dat <- droplevels(subset(dat, is.element(state, keep)))
 # Acres of corn grown each year
+require("lattice")
 xyplot(acres ~ year|state, dat, type='l', as.table=TRUE,
        layout=c(6,5),
        strip=strip.custom(par.strip.text=list(cex=.5)),
diff -Nru agridat-1.17/vignettes/agridat_intro.Rmd agridat-1.18/vignettes/agridat_intro.Rmd
--- agridat-1.17/vignettes/agridat_intro.Rmd	2019-10-30 12:25:11.000000000 +0000
+++ agridat-1.18/vignettes/agridat_intro.Rmd	2020-12-11 21:32:39.000000000 +0000
@@ -28,7 +28,7 @@
 
 White and van Evert (2008) present some guidelines for publication of data.
 
-Some of the examples use the `asreml` package since it is the _only_ R tool for fitting mixed models with complex variance structures to large datasets, and the best option for modelling AR1xAR1 residual variance structures.  Commercial use of `asreml` requires a license: http://www.vsni.co.uk/downloads/asreml.
+Some of the examples use the `asreml` package since it is the _only_ R tool for fitting mixed models with complex variance structures to large datasets, and the best option for modelling AR1xAR1 residual variance structures.  Commercial use of `asreml` requires a license: https://www.vsni.co.uk/downloads/asreml.
 
 # Comments on the package structure
 
@@ -54,8 +54,8 @@
 J. White and Frits van Evert. (2008).
 Publishing Agronomic Data.
 Agronomy Journal, 100, 1396-1400.
-http://doi.org/10.2134/agronj2008.0080F
+https://doi.org/10.2134/agronj2008.0080F
 
 Stephen Wolfram (2017).
 Launching the Wolfram Data Repository: Data Publishing that Really Works.
-http://blog.stephenwolfram.com/2017/04/launching-the-wolfram-data-repository-data-publishing-that-really-works/
+https://writings.stephenwolfram.com/2017/04/launching-the-wolfram-data-repository-data-publishing-that-really-works/