Binary files /tmp/tmpwn_J0u/_srmOP_YoJ/r-cran-rrcov-1.5-2/build/vignette.rds and /tmp/tmpwn_J0u/RFmiVlckfe/r-cran-rrcov-1.5-5/build/vignette.rds differ
diff -Nru r-cran-rrcov-1.5-2/ChangeLog r-cran-rrcov-1.5-5/ChangeLog
--- r-cran-rrcov-1.5-2/ChangeLog	2020-01-14 15:55:07.000000000 +0000
+++ r-cran-rrcov-1.5-5/ChangeLog	2020-07-31 22:28:06.000000000 +0000
@@ -1,11 +1,23 @@
+2020-07-31  Valentin Todorov  <valentin.todorov@chello.at>
+    *
+    * DESCRIPTION (Version): 1.5-5
+    *
+    * R/PcaHubert.R - PcaHubert will crash if X is 1-dimensional and mcd=FALSE: fixed
+    * R/PcaProj.R - fixed a problem with wrong scores (reported by Matthieu Lesnoff <matthieu.lesnoff@gmail.com>)
+    * R/CovMve.R, CovSest.R - fixed a problem with nsamp="exact" or nsamp="best" 
+    *       (reported by Claudio Agostinelli) - these functions, differently from CovMcd, 
+    *       should not take non-numeric 'nsamp'
+    * tests/tlda.R, togk4.R: fixed 'noLD' issues in tlda.R and okg4.R
+    *
+
 2020-01-14  Valentin Todorov  <valentin.todorov@chello.at>
     *
     * DESCRIPTION (Version): 1.5-2
     *
     * R/getEllipse.R - Removed the dependence on packages 'cluster' and 'ellipse'
-    * R/Linda - added parameter 'control' to select the robust location and 
+    * R/Linda - added parameter 'control' to select the robust location and
     *   covariance estimator to use in LDA. Now any estimator derived from class 'Cov'
-    *   can be used, even such that are not in 'rrcov'. Return this parameter in the 
+    *   can be used, even such that are not in 'rrcov'. Return this parameter in the
     *   returned S4 object.
     *
     * R/Linda - return the location and covariance estimator used as 'covobj'. This
@@ -13,10 +25,10 @@
     *
     * R/Lda.R - fixed an issue in predict function
     *
-    * R/LdaClassic, R/Linda - use generalized inverse if the common covariance 
+    * R/LdaClassic, R/Linda - use generalized inverse if the common covariance
     *   matrix is singular.
     *
-    
+
 2019-11-25  Valentin Todorov  <valentin.todorov@chello.at>
     *
     * DESCRIPTION (Version): 1.4-9
diff -Nru r-cran-rrcov-1.5-2/debian/changelog r-cran-rrcov-1.5-5/debian/changelog
--- r-cran-rrcov-1.5-2/debian/changelog	2020-05-30 16:46:56.000000000 +0000
+++ r-cran-rrcov-1.5-5/debian/changelog	2020-09-04 09:44:04.000000000 +0000
@@ -1,8 +1,11 @@
-r-cran-rrcov (1.5-2-1build1) groovy; urgency=medium
+r-cran-rrcov (1.5-5-1) unstable; urgency=medium
 
-  * No-change rebuild against r-api-4.0
+  * Team upload.
+  * New upstream version
+  * debhelper-compat 13 (routine-update)
+  * Rules-Requires-Root: no (routine-update)
 
- -- Graham Inggs <ginggs@ubuntu.com>  Sat, 30 May 2020 16:46:56 +0000
+ -- Andreas Tille <tille@debian.org>  Fri, 04 Sep 2020 11:44:04 +0200
 
 r-cran-rrcov (1.5-2-1) unstable; urgency=medium
 
diff -Nru r-cran-rrcov-1.5-2/debian/control r-cran-rrcov-1.5-5/debian/control
--- r-cran-rrcov-1.5-2/debian/control	2020-01-24 10:51:41.000000000 +0000
+++ r-cran-rrcov-1.5-5/debian/control	2020-09-04 09:44:04.000000000 +0000
@@ -4,7 +4,7 @@
 Section: gnu-r
 Testsuite: autopkgtest-pkg-r
 Priority: optional
-Build-Depends: debhelper-compat (= 12),
+Build-Depends: debhelper-compat (= 13),
                dh-r,
                r-base-dev,
                r-cran-robustbase (>= 0.92.1),
@@ -15,6 +15,7 @@
 Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-cran-rrcov
 Vcs-Git: https://salsa.debian.org/r-pkg-team/r-cran-rrcov.git
 Homepage: https://cran.r-project.org/package=rrcov
+Rules-Requires-Root: no
 
 Package: r-cran-rrcov
 Architecture: any
diff -Nru r-cran-rrcov-1.5-2/DESCRIPTION r-cran-rrcov-1.5-5/DESCRIPTION
--- r-cran-rrcov-1.5-2/DESCRIPTION	2020-01-16 18:40:06.000000000 +0000
+++ r-cran-rrcov-1.5-5/DESCRIPTION	2020-08-03 15:30:02.000000000 +0000
@@ -1,9 +1,9 @@
 Package: rrcov
-Version: 1.5-2
-Date: 2020-01-14
-VersionNote: Released 1.4-9 on 2019-11-25 on CRAN
+Version: 1.5-5
+Date: 2020-07-31
+VersionNote: Released 1.5-2 on 2020-01-16 on CRAN
 Title: Scalable Robust Estimators with High Breakdown Point
-Author: Valentin Todorov <valentin.todorov@chello.at>
+Authors@R: c(person("Valentin", "Todorov", role = c("aut", "cre"), email = "valentin.todorov@chello.at", comment=c(ORCID = "0000-0003-4215-0245")))
 Description: Robust Location and Scatter Estimation and Robust
         Multivariate Analysis with High Breakdown Point:
         principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>),
@@ -20,6 +20,7 @@
 LazyLoad: yes
 License: GPL (>= 2)
 Repository: CRAN
-Packaged: 2020-01-14 16:21:56 UTC; todorovv
+Packaged: 2020-07-31 22:53:37 UTC; Share
 NeedsCompilation: yes
-Date/Publication: 2020-01-16 18:40:06 UTC
+Author: Valentin Todorov [aut, cre] (<https://orcid.org/0000-0003-4215-0245>)
+Date/Publication: 2020-08-03 15:30:02 UTC
diff -Nru r-cran-rrcov-1.5-2/inst/Citation r-cran-rrcov-1.5-5/inst/Citation
--- r-cran-rrcov-1.5-2/inst/Citation	1970-01-01 00:00:00.000000000 +0000
+++ r-cran-rrcov-1.5-5/inst/Citation	2016-09-06 10:48:49.000000000 +0000
@@ -0,0 +1,21 @@
+citHeader("To cite rrcov in publications use:") 
+
+citEntry(entry = "Article",
+  title        = "An Object-Oriented Framework for Robust Multivariate Analysis",
+  author       = personList(as.person("Valentin Todorov"),
+                   as.person("Peter Filzmoser")),
+  journal      = "Journal of Statistical Software",
+  year         = "2009",
+  volume       = "32",
+  number       = "3",
+  pages        = "1--47",
+  url          = "http://www.jstatsoft.org/v32/i03/",
+
+  textVersion  =
+  paste("Valentin Todorov, Peter Filzmoser (2009).",
+        "An Object-Oriented Framework for Robust Multivariate Analysis.",
+        "Journal of Statistical Software, 32(3), 1-47.",
+        "URL http://www.jstatsoft.org/v32/i03/.")
+)
+
+
diff -Nru r-cran-rrcov-1.5-2/inst/CITATION r-cran-rrcov-1.5-5/inst/CITATION
--- r-cran-rrcov-1.5-2/inst/CITATION	2016-09-06 10:48:49.000000000 +0000
+++ r-cran-rrcov-1.5-5/inst/CITATION	1970-01-01 00:00:00.000000000 +0000
@@ -1,21 +0,0 @@
-citHeader("To cite rrcov in publications use:") 
-
-citEntry(entry = "Article",
-  title        = "An Object-Oriented Framework for Robust Multivariate Analysis",
-  author       = personList(as.person("Valentin Todorov"),
-                   as.person("Peter Filzmoser")),
-  journal      = "Journal of Statistical Software",
-  year         = "2009",
-  volume       = "32",
-  number       = "3",
-  pages        = "1--47",
-  url          = "http://www.jstatsoft.org/v32/i03/",
-
-  textVersion  =
-  paste("Valentin Todorov, Peter Filzmoser (2009).",
-        "An Object-Oriented Framework for Robust Multivariate Analysis.",
-        "Journal of Statistical Software, 32(3), 1-47.",
-        "URL http://www.jstatsoft.org/v32/i03/.")
-)
-
-
Binary files /tmp/tmpwn_J0u/_srmOP_YoJ/r-cran-rrcov-1.5-2/inst/doc/rrcov.pdf and /tmp/tmpwn_J0u/RFmiVlckfe/r-cran-rrcov-1.5-5/inst/doc/rrcov.pdf differ
diff -Nru r-cran-rrcov-1.5-2/inst/NEWS.Rd r-cran-rrcov-1.5-5/inst/NEWS.Rd
--- r-cran-rrcov-1.5-2/inst/NEWS.Rd	2020-01-14 15:55:06.000000000 +0000
+++ r-cran-rrcov-1.5-5/inst/NEWS.Rd	2020-07-31 22:27:58.000000000 +0000
@@ -4,16 +4,33 @@
 \title{News for \R Package \pkg{rrcov}}
 \encoding{UTF-8}
 
-\section{CHANGES in rrcov VERSION 1.4-7 (2018-11-14)}{
+\section{CHANGES in rrcov VERSION 1.5-5 (2020-07-31)}{
+  \subsection{BUG FIXES}{
+    \itemize{
+      \item PcaHubert will crash if X is 1-dimensional and mcd=FALSE: fixed.
+      \item Fixed 'noLD' issues in tlda.R and okg4.R
+      \item Fixed a problem with wrong scores in PcaProj() (reported by Matthieu Lesnoff <matthieu.lesnoff@gmail.com>)
+      \item Fixed a problem with nsamp="exact" or nsamp="best" in CovMve(), CovSest() (reported by Claudio Agostinelli) - 
+        these functions, differently from CovMcd, should not take non-numeric 'nsamp'
+    }
+  }
+}  
+\section{CHANGES in rrcov VERSION 1.5-2 (2020-01-14)}{
   \subsection{NEW FEATURES}{
     \itemize{
-      \item Added Minimum Regularized Covariance Determinant estimator (MRCD) 
-        (Boudt et al. 2018) - 
-        functions \code{CovMrcd()} and \code{CovControlMrcd()}
-        
-      \item  Added data set \code{octane}; data set \code{olitos} from package rrcovHD moved here.
-      
-      \item The 'pairs' plot is now available for classical covariance matrix
+      \item  Added parameter 'control' to Linda - to select the robust location and 
+        covariance estimator to use in LDA. Now any estimator derived from class 'Cov'
+        can be used, even such that are not in 'rrcov'. Return this parameter in the 
+        returned S4 object.
+      \item Linda returns now the location and covariance estimator used as 'covobj'. This
+        is useful for controlling cross-validation, for example.
+      \item Linda and LdaClassic use generalized inverse if the common covariance matrix is singular.
+    }
+  }
+  \subsection{BUG FIXES}{
+    \itemize{
+      \item Fixed an issue the 'predict' function.
+      \item Removed the dependence on packages 'cluster' and 'ellipse'.
     }
   }
 }
@@ -34,22 +51,16 @@
     }
   }
 }
-\section{CHANGES in rrcov VERSION 1.5-2 (2020-01-14)}{
+\section{CHANGES in rrcov VERSION 1.4-7 (2018-11-14)}{
   \subsection{NEW FEATURES}{
     \itemize{
-      \item  Added parameter 'control' to Linda - to select the robust location and 
-        covariance estimator to use in LDA. Now any estimator derived from class 'Cov'
-        can be used, even such that are not in 'rrcov'. Return this parameter in the 
-        returned S4 object.
-      \item Linda returns now the location and covariance estimator used as 'covobj'. This
-        is useful for controlling cross-validation, for example.
-      \item Linda and LdaClassic use generalized inverse if the common covariance matrix is singular.
-    }
-  }
-  \subsection{BUG FIXES}{
-    \itemize{
-      \item Fixed an issue the 'predict' function.
-      \item Removed the dependence on packages 'cluster' and 'ellipse'.
+      \item Added Minimum Regularized Covariance Determinant estimator (MRCD) 
+        (Boudt et al. 2018) - 
+        functions \code{CovMrcd()} and \code{CovControlMrcd()}
+        
+      \item  Added data set \code{octane}; data set \code{olitos} from package rrcovHD moved here.
+      
+      \item The 'pairs' plot is now available for classical covariance matrix
     }
   }
 }
diff -Nru r-cran-rrcov-1.5-2/man/Appalachia.Rd r-cran-rrcov-1.5-5/man/Appalachia.Rd
--- r-cran-rrcov-1.5-2/man/Appalachia.Rd	2016-09-06 10:48:49.000000000 +0000
+++ r-cran-rrcov-1.5-5/man/Appalachia.Rd	2020-07-28 21:01:55.000000000 +0000
@@ -32,10 +32,8 @@
 }
 \references{
     Neykov, N.M., Neytchev, P.N., Van Gelder, P.H.A.J.M. and Todorov V. (2007),
-    Robust detection of discordant sites in regional frequency analysis,
-    \emph{Water Resources Research}, 43, W06417, 
-    doi:10.1029/2006WR005322, 
-    \url{http://www.agu.org/pubs/crossref/2007/2006WR005322.shtml}
+        Robust detection of discordant sites in regional frequency analysis,
+        \emph{Water Resources Research}, 43, W06417, doi:10.1029/2006WR005322
 }
 \examples{
     data(Appalachia)
diff -Nru r-cran-rrcov-1.5-2/man/Cascades.Rd r-cran-rrcov-1.5-5/man/Cascades.Rd
--- r-cran-rrcov-1.5-2/man/Cascades.Rd	2016-09-06 10:48:49.000000000 +0000
+++ r-cran-rrcov-1.5-5/man/Cascades.Rd	2020-07-28 21:02:54.000000000 +0000
@@ -31,10 +31,8 @@
 }
 \references{
     Neykov, N.M., Neytchev, P.N., Van Gelder, P.H.A.J.M. and Todorov V. (2007),
-    Robust detection of discordant sites in regional frequency analysis,
-    \emph{Water Resources Research}, 43, W06417, 
-    doi:10.1029/2006WR005322, 
-    \url{http://www.agu.org/pubs/crossref/2007/2006WR005322.shtml}
+        Robust detection of discordant sites in regional frequency analysis,
+        \emph{Water Resources Research}, 43, W06417, doi:10.1029/2006WR005322
 }
 \examples{
     data(Cascades)
diff -Nru r-cran-rrcov-1.5-2/man/lmom32.Rd r-cran-rrcov-1.5-5/man/lmom32.Rd
--- r-cran-rrcov-1.5-2/man/lmom32.Rd	2016-09-06 10:48:49.000000000 +0000
+++ r-cran-rrcov-1.5-5/man/lmom32.Rd	2020-07-28 21:02:52.000000000 +0000
@@ -32,10 +32,8 @@
 }
 \references{
     Neykov, N.M., Neytchev, P.N., Van Gelder, P.H.A.J.M. and Todorov V. (2007),
-    Robust detection of discordant sites in regional frequency analysis,
-    \emph{Water Resources Research}, 43, W06417, 
-    doi:10.1029/2006WR005322, 
-    \url{http://www.agu.org/pubs/crossref/2007/2006WR005322.shtml}
+        Robust detection of discordant sites in regional frequency analysis,
+        \emph{Water Resources Research}, 43, W06417, doi:10.1029/2006WR005322
 }
 \examples{
     data(lmom32)
diff -Nru r-cran-rrcov-1.5-2/man/lmom33.Rd r-cran-rrcov-1.5-5/man/lmom33.Rd
--- r-cran-rrcov-1.5-2/man/lmom33.Rd	2016-09-06 10:48:49.000000000 +0000
+++ r-cran-rrcov-1.5-5/man/lmom33.Rd	2020-07-28 21:03:05.000000000 +0000
@@ -32,10 +32,8 @@
 }
 \references{
     Neykov, N.M., Neytchev, P.N., Van Gelder, P.H.A.J.M. and Todorov V. (2007),
-    Robust detection of discordant sites in regional frequency analysis,
-    \emph{Water Resources Research}, 43, W06417, 
-    doi:10.1029/2006WR005322, 
-    \url{http://www.agu.org/pubs/crossref/2007/2006WR005322.shtml}
+        Robust detection of discordant sites in regional frequency analysis,
+        \emph{Water Resources Research}, 43, W06417, doi:10.1029/2006WR005322
 }
 \examples{
     data(lmom33)
diff -Nru r-cran-rrcov-1.5-2/MD5 r-cran-rrcov-1.5-5/MD5
--- r-cran-rrcov-1.5-2/MD5	2020-01-16 18:40:07.000000000 +0000
+++ r-cran-rrcov-1.5-5/MD5	2020-08-03 15:30:03.000000000 +0000
@@ -1,5 +1,5 @@
-3e58c27a78717694ed6588866cff6485 *ChangeLog
-c71c126ed1f8ffd11cb2278a7cc9f78f *DESCRIPTION
+58f1a6886540ab687e6779903d16e83f *ChangeLog
+cffdc8b0aaa727f55630d082488ca227 *DESCRIPTION
 45bdb0c2375c6fdf31a4c222a649f551 *NAMESPACE
 ce701b02810d7e088273e45ada6b9010 *R/AllClasses.R
 2621b703a070a9cd1b9ca2b755e763d4 *R/AllGenerics.R
@@ -10,11 +10,11 @@
 f67189295e5bc982dc78ccfa6655e443 *R/CovMcd.R
 7e71f38495389ced9e788151c196fe4a *R/CovMest.R
 0bfe6fd9084c3f46acd7d80fd1f6aaad *R/CovMrcd.R
-9544d6ab30f55290886cffe34b6ea3b5 *R/CovMve.R
+e77a9543db99d9d22930bafdee807235 *R/CovMve.R
 e37a6bf70cfc6570731adafd7a6e8105 *R/CovOgk.R
 0c633130bb04693e28191981e929f732 *R/CovRobust.R
 574a90e594f83533862c70c3fb1e1ff9 *R/CovSde.R
-3c54ba7ac0de81bcb7773ae3cbf240a3 *R/CovSest.R
+906091b55297c88c4ecf65f1fe3ad3bc *R/CovSest.R
 68c34b29dbfcf037efb45906f24b0f2c *R/Lda.R
 6e9c08123c0cfb08471de32222065527 *R/LdaClassic.R
 79e8c4f4ace04e529a247dc34aaa8178 *R/LdaPP.R
@@ -23,9 +23,9 @@
 0d309301e37d516bef369d9f36cebfb9 *R/PcaClassic.R
 75d874c217f0135057c99e74465e6a52 *R/PcaCov.R
 139fcd1d9e94a6c479452fcf85652158 *R/PcaGrid.R
-5a06f0ce83acbf982fe3a9e75200bc86 *R/PcaHubert.R
+2c6263cc4b84502c86b28df8104a0663 *R/PcaHubert.R
 11689ed5e05d9c267bbb940dbaf9d312 *R/PcaLocantore.R
-cb1cc2658afac0ad584e9bfdbd4145dd *R/PcaProj.R
+84afdc34954efcd87bd04c10d1dac2a4 *R/PcaProj.R
 16c1b7d22de82739325c854e1237a0b4 *R/Qda.R
 182e11977a3de4ea9a69183eee31a082 *R/QdaClassic.R
 a1959e4b3c1dfe604f6af39d6aed6da5 *R/QdaCov.R
@@ -36,13 +36,13 @@
 25a5fe6f3c0bc42acd775034eb6f006d *R/detmrcd.R
 0c36abbd8796244b63400966be36ccd2 *R/dets.R
 649f519e49932382f68de71ef9cbe6ba *R/donostah.R
-2c32c789484733397237e62ae9bb6760 *R/plot-utils.R
+3797adf00cf06a422730c94fbe4f6a23 *R/plot-utils.R
 6731799ffde9689f52b53bd45a8273c2 *R/psibwt.R
 dcebffecd8cbf489cede75c6813eabed *R/psifun.R
 3762b8071c38856c5fba22f1f17797f3 *R/transform.R
 bd6c4b899751d5a2e3c456fec562af91 *R/utils.R
 5752f99eceda48e21d5743fe05c7c9b6 *R/zzz.R
-cc24b09b10214fb9e6c9a5ebf62ef0f8 *build/vignette.rds
+3b75cb342bcfa04bc61a94c202a2f4f9 *build/vignette.rds
 dc91e66b24be42e13e05e3350738de29 *data/Appalachia.rda
 256379df09041b35cf1a53bed3e12508 *data/Cascades.rda
 eb7908cee2761979204dfe7d874024d6 *data/OsloTransect.rda
@@ -62,18 +62,18 @@
 36a95bdc17395bffa23901ddc84f24f0 *data/soil.rda
 67b1c79477732c5d666d2d580c7259cc *data/un86.rda
 e7ad7ea33c9a5bd91d0c46dc35f07324 *data/wages.rda
-51596f6a6749b1ee72b6842750f159b8 *inst/CITATION
-12a680c422ccf7f8b1d95f68f55791d0 *inst/NEWS.Rd
+51596f6a6749b1ee72b6842750f159b8 *inst/Citation
+85a35231609652cdde1ad43bd08fb036 *inst/NEWS.Rd
 0cd7cf2b0f3e787248341b7eb7415973 *inst/doc/rrcov.R
 58ccdfd29ee1d1c42b759226e480a68c *inst/doc/rrcov.Rnw
-16f8c7a28ae70565b7f96db9c220f529 *inst/doc/rrcov.pdf
+9332f15a7825a738184c743cdfa50770 *inst/doc/rrcov.pdf
 0f5844b638361c83e2a90f4605d3c0a9 *inst/examples/bm/bm_lts.m
 ce8cc65f5ce8e9aedf1ac45c3f408022 *inst/examples/bm/bm_mcd.m
 1cb5f88839eb05cd065e67c2c3415252 *inst/examples/bm/bmlts.S
 3f9ec6b75cdbdee68324fe6907a0214d *inst/examples/bm/bmmcd.S
 f955d6936d3cde8a1bd825938601c0ae *inst/examples/test-ellipse.R
-f8797fb11a1081c4cab7610a31a36540 *man/Appalachia.Rd
-00f1a7e08ed0c0b4d9e033edde7696e9 *man/Cascades.Rd
+a0901c5ebf120b3a340374f70481839d *man/Appalachia.Rd
+5c54f00c5b70324f7f27d5aee624b7db *man/Cascades.Rd
 ea3270717d98ab23f80fd65fa5057c93 *man/Cov-class.Rd
 bc81fb026d0aff35f38e64207dab50a4 *man/CovClassic-class.Rd
 64ed005bab64d405da0b528892f2e2f0 *man/CovClassic.Rd
@@ -162,8 +162,8 @@
 cd32b6c3686ec967354fc017a406626b *man/getLoadings-methods.Rd
 19c9220469d7db644eb7f885179cdb41 *man/hemophilia.Rd
 37cb7c180bf6563504366391de15c8cc *man/isSingular.Rd
-ecb227b0175ac8201e93c194637f425f *man/lmom32.Rd
-1ff419d2ca28076ea6461fa07698bf8b *man/lmom33.Rd
+db0e5e9ff9798192bf162c122f29ef33 *man/lmom32.Rd
+e95c14bf85f44bfb576e46b9f9a27a44 *man/lmom33.Rd
 deb2edecbfed78502752bbf1dcd6bb5a *man/maryo.Rd
 93b3d8c62a7190909c7decb1b1b1aa9c *man/octane.Rd
 06670e7a6424ba39012a590e33eb558d *man/olitos.Rd
@@ -185,11 +185,11 @@
 c1e732bb49768c40a41f3d196ed921d2 *src/fast-mve.c
 ccf4857d53fdada76dfd725ef84d9683 *src/fsada.f
 c6d3916e13986bd0e2bc0ea73bf278a3 *src/rrcov_init.c
-b4aaedc4fce50b5ec8b420c789ff6c1a *src/sest.c
+ff85fbb3251f152021ad1adab2c37007 *src/sest.c
 ce128c182f1d80545b9d52713dfd288d *tests/thubert.R
 1e2270032cf419f3f4791e341d5f4814 *tests/thubert.Rout.save
-9cac3a2e6e170e167cbf8de9c7c19edf *tests/tlda.R
-f3469fe82bd70b8c6fbaca6f0239d770 *tests/tlda.Rout.save
+d15b8520f3b8e1e2d220e1d87815d138 *tests/tlda.R
+e5a65a90adad1eeade3f5334eac3082f *tests/tlda.Rout.save
 b438bcce32bc72667d5f353a77e32655 *tests/tldapp.R
 439f62ef842f1025f2b8f1f98bb1be9d *tests/tldapp.Rout.save
 81f8599fa2320b93acc1e48740f26f8b *tests/tmcd4.R
@@ -198,8 +198,8 @@
 d5ff4f4b4772061f3ca09d7b8d38161b *tests/tmest4.Rout.save
 543589de1df4a8c5b354ea5d5ee2e24e *tests/tmve4.R
 dc6c9dd3fd406040a54a837b446079b0 *tests/tmve4.Rout.save
-2a1e5911d4a26366e9b85d36921bca12 *tests/togk4.R
-ab8ec7cc06f8af195f662a7554de148f *tests/togk4.Rout.save
+40ae348baf8b55ebcf802ece9a489b59 *tests/togk4.R
+3234b08fe1a41dee866b18f991bef344 *tests/togk4.Rout.save
 7ed52ca35c24fabd3aca1bac54afcc21 *tests/tqda.R
 2e476434e296c3aeda6be662671aeabb *tests/tqda.Rout.save
 0c3037618a637218348efe2ee6c03a3c *tests/tsde.R
diff -Nru r-cran-rrcov-1.5-2/R/CovMve.R r-cran-rrcov-1.5-5/R/CovMve.R
--- r-cran-rrcov-1.5-2/R/CovMve.R	2016-09-06 10:48:49.000000000 +0000
+++ r-cran-rrcov-1.5-5/R/CovMve.R	2020-07-31 20:46:55.000000000 +0000
@@ -34,6 +34,10 @@
 
     ##   Options "best" and "exact" for nsamp
     ##   nsamp will be further analized in the wrapper .fastmcd()
+    ## VT::31.07.2020 - fix an old bug - MVA cannot handle nsamp="best" or "exact"
+
+    if(!missing(nsamp) && !is.numeric(nsamp))
+        stop("Number of trials nsamp must be a positive number!")
     if(!missing(nsamp) && is.numeric(nsamp) && nsamp <= 0)
         stop("Invalid number of trials nsamp = ",nsamp, "!")
 
diff -Nru r-cran-rrcov-1.5-2/R/CovSest.R r-cran-rrcov-1.5-5/R/CovSest.R
--- r-cran-rrcov-1.5-2/R/CovSest.R	2016-09-06 10:48:49.000000000 +0000
+++ r-cran-rrcov-1.5-5/R/CovSest.R	2020-07-31 20:49:26.000000000 +0000
@@ -50,6 +50,11 @@
         assign(".Random.seed", seed, envir=.GlobalEnv)
     }
 
+    if(!missing(nsamp) && !is.numeric(nsamp))
+        stop("Number of trials nsamp must be a positive number!")
+    if(!missing(nsamp) && is.numeric(nsamp) && nsamp <= 0)
+        stop("Invalid number of trials nsamp = ",nsamp, "!")
+
     if(is.data.frame(x))
         x <- data.matrix(x)
     else if (!is.matrix(x))
@@ -1115,7 +1120,7 @@
     sig <- median(dis)
     a <- sig/100
     ff <- fespro(a, p, dis, alpha, delta)
-    while(ff < 0) 
+    while(ff < 0)
     {
         a <- a/10
         ff <- fespro(a, p, dis, alpha, delta)
@@ -1123,7 +1128,7 @@
 
     b <- sig*10
     ff <- fespro(b, p, dis, alpha, delta)
-    while(ff>0) 
+    while(ff>0)
     {
         b <- b*10
         ff <- fespro(b, p, dis, alpha, delta)
@@ -1270,7 +1275,7 @@
             cat("\nit, decre1:",it,decre1, "\n")
             print(des1)
         }
-        if(decre1 <= tol) 
+        if(decre1 <= tol)
         {
             grad <- gradien(x, mu1, v1, mumin, vmin, sigmin, dismin, sig1, dis1, alpha)
             mu2 <- grad$mu
@@ -1292,8 +1297,8 @@
                 sigmin <- sig2
             }
         }
-        
-        if(decre1 > tol) 
+
+        if(decre1 > tol)
         {
             decre <- decre1
             mumin <- mu1
diff -Nru r-cran-rrcov-1.5-2/R/PcaHubert.R r-cran-rrcov-1.5-5/R/PcaHubert.R
--- r-cran-rrcov-1.5-2/R/PcaHubert.R	2016-09-06 10:48:49.000000000 +0000
+++ r-cran-rrcov-1.5-5/R/PcaHubert.R	2020-04-24 21:58:40.000000000 +0000
@@ -285,7 +285,8 @@
         }
 
         H0 <- order(apply(Z, 1, max))           # n x 1 - the outlyingnesses of all n points
-        Xh <- X[H0[1:h], ]                      # the h data points with smallest outlyingness
+        Xh <- X[H0[1:h],,drop=FALSE]            # the h data points with smallest outlyingness.
+                                                # VT::24.04.2020 Keep Xh as a mtrix, otherwise .classPC will not work.
         Xh.svd <- .classPC(Xh)
         kmax <- min(Xh.svd$rank, kmax)
         if(trace)
diff -Nru r-cran-rrcov-1.5-2/R/PcaProj.R r-cran-rrcov-1.5-5/R/PcaProj.R
--- r-cran-rrcov-1.5-2/R/PcaProj.R	2016-09-06 10:48:49.000000000 +0000
+++ r-cran-rrcov-1.5-5/R/PcaProj.R	2020-07-31 22:15:58.000000000 +0000
@@ -78,12 +78,16 @@
     }
     out <- PCAproj(x, k, scale=scale, ...)
 
-    scores <- predict(out)
     center   <- out$center
     scale <- out$scale
     sdev     <- out$sdev
-    scores   <- as.matrix(scores[, 1:k])
     loadings <- as.matrix(out$loadings[, 1:k])
+
+## VT::31.07.2020
+##    scores <- predict(out)
+##    scores   <- as.matrix(scores[, 1:k])
+    scores <- (x- matrix(rep(center, nrow(x)), nrow = nrow(x), byrow = TRUE)) %*% loadings
+
     eigenvalues  <- (sdev^2)[1:k]
 
 ######################################################################
diff -Nru r-cran-rrcov-1.5-2/R/plot-utils.R r-cran-rrcov-1.5-5/R/plot-utils.R
--- r-cran-rrcov-1.5-2/R/plot-utils.R	2020-01-14 15:17:30.000000000 +0000
+++ r-cran-rrcov-1.5-5/R/plot-utils.R	2020-06-23 11:44:56.000000000 +0000
@@ -69,7 +69,7 @@
                                      pos = "topright",
                                      txt = c("robust", "classical"),
                                      col = c("red", "blue"),
-                                     lty = c("solid", "dotted"),
+                                     lty = c("solid", "solid"),
                                      pch = c(1,24)))
 
 
@@ -169,7 +169,7 @@
 
 .qqplot <- function(x, p, cutoff, classic=FALSE, id.n,
     main=eval(substitute(expression(paste(chi^2, " QQ-Plot")))),
-    xlab=eval(substitute(expression(paste("Sqrt of the quantiles of the ", chi^2, " distribution")))),
+    xlab=eval(substitute(expression(paste("Sqrt of the quantiles of the ", chi[p]^2, " distribution")), list(p=p))),
     ylab=paste(if(classic) "Mahalanobis" else "Robust", "distance"),
     labs=1:length(x),
     ...)
@@ -227,7 +227,7 @@
     main = "Tolerance ellipse (97.5%)",
     xlab = "",
     ylab = "",
-    labs,
+    labs, legend.position="bottomright",
     ...)
 {
 
@@ -303,7 +303,7 @@
     if(classic){
         points(z2, type = "l", lty=leg@lty[2], col=leg@col[2])
         if(leg@leg)
-            legend("bottomright", leg@txt, pch=leg@pch, lty=leg@lty, col=leg@col)
+            legend(legend.position, leg@txt, pch=leg@pch, lty=leg@lty, col=leg@col)
     }
 
     invisible()
@@ -612,7 +612,7 @@
 ##
 .xyqqchi2 <- function(obj, cutoff,
     main=eval(substitute(expression(paste(chi^2, " QQ-Plot")))),
-    xlab=eval(substitute(expression(paste("Sqrt of the quantiles of the ", chi^2, " distribution")))),
+    xlab=eval(substitute(expression(paste("Sqrt of the quantiles of the ", chi[p]^2, " distribution")), list(p=p))),
     ylab="Mahalanobis distance",
     col="darkred",
     labs,
diff -Nru r-cran-rrcov-1.5-2/src/sest.c r-cran-rrcov-1.5-5/src/sest.c
--- r-cran-rrcov-1.5-2/src/sest.c	2018-11-14 17:25:36.000000000 +0000
+++ r-cran-rrcov-1.5-5/src/sest.c	2020-07-31 22:31:15.000000000 +0000
@@ -521,15 +521,17 @@
 double *work = (double *) R_alloc(2*pp, sizeof(double));		
 int *pivot = (int *) R_alloc(pp, sizeof(int));
 
-   // compute the mean, put submatrix into xw, center its columns
-   for(j=0; j<pp; j++) 
-   {
-      mean[j] = 0.0;
-      for(i=0; i<nnind; i++) 
-         mean[j] += (xw[i+j*nnind] = x[indices[i]][j]) / (double) nnind;
-	 for(i=0; i<nnind; i++) 
-	    xw[i+j*nnind] -= mean[j];
-   }
+    // compute the mean, put submatrix into xw, center its columns
+    for(j=0; j<pp; j++) 
+    {
+        mean[j] = 0.0;
+        for(i=0; i<nnind; i++) {
+            mean[j] += (xw[i+j*nnind] = x[indices[i]][j]) / (double) nnind;
+        }
+        for(i=0; i<nnind; i++) {
+            xw[i+j*nnind] -= mean[j];
+        }
+    }
 
    // QR decomposition of the submatrix
    F77_CALL(dqrdc2)(xw, nind, nind, p, &tol, rank, qraux, pivot, work);
diff -Nru r-cran-rrcov-1.5-2/tests/tlda.R r-cran-rrcov-1.5-5/tests/tlda.R
--- r-cran-rrcov-1.5-2/tests/tlda.R	2020-01-14 16:04:57.000000000 +0000
+++ r-cran-rrcov-1.5-5/tests/tlda.R	2020-07-28 14:01:03.000000000 +0000
@@ -36,8 +36,11 @@
     data(soil)
     soil1983 <- soil[soil$D == 0, -2]       # only 1983, remove column D (always 0)
 
+    ## This will not work within the function, of course
+    ##  - comment it out
     ## IGNORE_RDIFF_BEGIN
-    show(rlda <- Linda(F~., data=soil1983, method=method))
+    rlda <- Linda(F~., data=soil1983, method=method)
+    ##  show(rlda)
     ## IGNORE_RDIFF_END
     show(predict(rlda))
 
@@ -76,7 +79,8 @@
     if(method != "mcdA")
     {
         ## IGNORE_RDIFF_BEGIN
-        show(rlda <- Linda(Species~., data=fish, method=method, l1med=TRUE))
+        rlda <- Linda(Species~., data=fish, method=method, l1med=TRUE)
+        ## show(rlda)
         ## IGNORE_RDIFF_END
         show(predict(rlda))
     }
@@ -91,7 +95,8 @@
     if(method != "mcdA")
     {
         ## IGNORE_RDIFF_BEGIN
-        show(rlda <- Linda(grp~., data=olitos, method=method, l1med=TRUE))
+        rlda <- Linda(grp~., data=olitos, method=method, l1med=TRUE)
+        ## show(rlda)
         ## IGNORE_RDIFF_END
         show(predict(rlda))
     }
diff -Nru r-cran-rrcov-1.5-2/tests/tlda.Rout.save r-cran-rrcov-1.5-5/tests/tlda.Rout.save
--- r-cran-rrcov-1.5-2/tests/tlda.Rout.save	2020-01-14 16:11:03.000000000 +0000
+++ r-cran-rrcov-1.5-5/tests/tlda.Rout.save	2020-07-28 14:21:27.000000000 +0000
@@ -1,5 +1,5 @@
 
-R Under development (unstable) (2020-01-08 r77640) -- "Unsuffered Consequences"
+R version 4.0.2 (2020-06-22) -- "Taking Off Again"
 Copyright (C) 2020 The R Foundation for Statistical Computing
 Platform: x86_64-w64-mingw32/x64 (64-bit)
 
@@ -53,8 +53,11 @@
 +     data(soil)
 +     soil1983 <- soil[soil$D == 0, -2]       # only 1983, remove column D (always 0)
 + 
++     ## This will not work within the function, of course
++     ##  - comment it out
 +     ## IGNORE_RDIFF_BEGIN
-+     show(rlda <- Linda(F~., data=soil1983, method=method))
++     rlda <- Linda(F~., data=soil1983, method=method)
++     ##  show(rlda)
 +     ## IGNORE_RDIFF_END
 +     show(predict(rlda))
 + 
@@ -93,7 +96,8 @@
 +     if(method != "mcdA")
 +     {
 +         ## IGNORE_RDIFF_BEGIN
-+         show(rlda <- Linda(Species~., data=fish, method=method, l1med=TRUE))
++         rlda <- Linda(Species~., data=fish, method=method, l1med=TRUE)
++         ## show(rlda)
 +         ## IGNORE_RDIFF_END
 +         show(predict(rlda))
 +     }
@@ -108,7 +112,8 @@
 +     if(method != "mcdA")
 +     {
 +         ## IGNORE_RDIFF_BEGIN
-+         show(rlda <- Linda(grp~., data=olitos, method=method, l1med=TRUE))
++         rlda <- Linda(grp~., data=olitos, method=method, l1med=TRUE)
++         ## show(rlda)
 +         ## IGNORE_RDIFF_END
 +         show(predict(rlda))
 +     }
@@ -256,36 +261,6 @@
    Yes 0.279 0.721
 
 Data:  Forest soils
-Call:
-Linda(F ~ ., data = soil1983, method = method)
-
-Prior Probabilities of Groups:
-      1       2       3 
-0.18966 0.39655 0.41379 
-
-Group means:
-       Al     Ca     Mg      K     Na
-1  97.588  9.515 2.0288 4.8438 1.4250
-2 125.531 12.662 2.5344 8.3044 1.0063
-3 146.020 20.093 2.3427 9.2293 1.0327
-
-Within-groups Covariance Matrix:
-          Al       Ca       Mg        K      Na
-Al 10290.311 814.4770 137.7378 427.9014 78.6332
-Ca   814.477 137.5707  11.1133  45.9755  1.1929
-Mg   137.738  11.1133   2.4744   7.1536  1.1148
-K    427.901  45.9755   7.1536  26.6947  2.1072
-Na    78.633   1.1929   1.1148   2.1072  1.8993
-
-Linear Coeficients:
-         Al       Ca       Mg        K      Na
-1 -0.015036 0.098323  1.50272 -0.20104 0.65207
-2 -0.011314 0.014336  0.79365  0.23427 0.26346
-3 -0.010075 0.108029 -0.23845  0.33268 0.66382
-
-Constants:
-      1       2       3 
--2.8987 -2.4166 -2.8307 
                           
 Apparent error rate 0.3966
 
@@ -584,36 +559,6 @@
    Yes 0.338 0.662
 
 Data:  Forest soils
-Call:
-Linda(F ~ ., data = soil1983, method = method)
-
-Prior Probabilities of Groups:
-      1       2       3 
-0.18966 0.39655 0.41379 
-
-Group means:
-       Al      Ca     Mg      K      Na
-1  94.017  8.7693 2.0645 4.8409 1.38034
-2 121.961 11.9168 2.5701 8.3015 0.96159
-3 142.450 19.3469 2.3784 9.2264 0.98801
-
-Within-groups Covariance Matrix:
-         Al        Ca       Mg        K       Na
-Al 5577.543 402.99291 87.24346 272.0090 33.51195
-Ca  402.993  69.16394  7.02779  27.5314 -0.10027
-Mg   87.243   7.02779  1.72469   5.2026  0.58145
-K   272.009  27.53139  5.20264  18.6054  1.22367
-Na   33.512  -0.10027  0.58145   1.2237  0.73272
-
-Linear Coeficients:
-          Al       Ca       Mg        K      Na
-1 -0.0216522 0.247417  2.37117 -0.58409 2.00180
-2 -0.0142803 0.064941  0.94536  0.24041 0.82269
-3 -0.0051346 0.311146 -1.77318  0.45712 2.26953
-
-Constants:
-      1       2       3 
--4.1450 -3.0493 -4.6478 
                           
 Apparent error rate 0.3448
 
@@ -771,45 +716,6 @@
      O 0 1
 
 Data:  fish
-Call:
-Linda(Species ~ ., data = fish, method = method, l1med = TRUE)
-
-Prior Probabilities of Groups:
-       1        2        3        4        5        6        7 
-0.215190 0.037975 0.126582 0.069620 0.088608 0.107595 0.354430 
-
-Group means:
-    Weight Length1 Length2 Length3  Height  Width
-1 595.6580  29.892  32.639  37.822 15.1297 5.2245
-2 426.2876  26.488  28.988  31.888  9.4082 5.2931
-3 128.7053  19.863  21.447  23.963  6.2497 3.5184
-4 127.1961  18.776  20.486  23.028  9.2399 3.1031
-5  -7.7908  10.513  11.077  12.307  1.9496 1.1613
-6 492.2095  39.290  41.818  44.803  6.5847 4.3818
-7 188.2288  21.970  23.960  25.411  6.4408 3.9196
-
-Within-groups Covariance Matrix:
-           Weight  Length1  Length2  Length3   Height    Width
-Weight  10307.256 311.1217 327.4531 348.5864 111.9344 65.18676
-Length1   311.122  12.4736  13.0429  13.8128   3.9868  2.23104
-Length2   327.453  13.0429  13.6888  14.5200   4.2226  2.35475
-Length3   348.586  13.8128  14.5200  15.4620   4.5272  2.51917
-Height    111.934   3.9868   4.2226   4.5272   1.5967  0.77020
-Width      65.187   2.2310   2.3547   2.5192   0.7702  0.50506
-
-Linear Coeficients:
-    Weight   Length1  Length2  Length3   Height     Width
-1 -0.14016   4.66132 -52.6510  47.4146  16.4622  -8.28255
-2 -0.17831 -17.90956  10.5348   9.2671   2.4405  13.54607
-3 -0.21829  -3.39328 -11.2809  17.5360   1.6745  12.70410
-4 -0.25676  -0.62855  -9.3172  10.3249  16.3429   9.07821
-5 -0.10288   1.43667 -12.4904  14.5200  -3.4014   0.22846
-6 -0.03936 -25.70770  12.1247  26.1500 -24.0771 -22.92857
-7 -0.22506 -22.26278  36.3775 -10.7537   1.6280  16.70299
-
-Constants:
-       1        2        3        4        5        6        7 
--169.767  -75.846  -71.040  -93.469  -27.365 -197.327  -72.443 
                           
 Apparent error rate 0.0886
 
@@ -881,182 +787,6 @@
   Eritrean 0.143    0.857
 
 Data:  olitos
-Call:
-Linda(grp ~ ., data = olitos, method = method, l1med = TRUE)
-
-Prior Probabilities of Groups:
-       1        2        3        4 
-0.416667 0.208333 0.283333 0.091667 
-
-Group means:
-       X1     X2      X3        X4     X5      X6     X7     X8      X9     X10
-1 0.61656 61.906 0.13686 0.0081090 12.267 0.80489 2.3462 6.3270 0.72945 0.52233
-2 0.45078 61.687 0.15394 0.0085591 12.482 0.78669 2.0993 6.0396 0.61587 0.40528
-3 0.80394 61.864 0.18131 0.0111781 13.705 1.07615 2.1611 7.1020 0.61887 0.44040
-4 1.54761 61.523 0.16626 0.0126077 13.034 0.94291 2.0852 6.7778 0.81447 0.61730
-      X11    X12    X13     X14    X15    X16    X17    X18    X19    X20
-1 0.44540 241.32 3.3226 0.96723 89.616 95.582 1.9662 15.597 27.183 40.032
-2 0.35486 260.70 3.3327 0.87368 91.883 95.748 3.3486 10.677 23.204 47.470
-3 0.37729 248.27 3.0123 1.04047 89.480 95.847 3.2502 16.575 31.863 36.731
-4 0.53571 273.62 3.2936 1.56869 89.478 95.082 1.8318 12.703 25.293 42.923
-     X21    X22     X23    X24    X25
-1 17.166 66.150 0.38562 2.8982 82.490
-2 18.715 53.161 0.49669 1.4147 73.939
-3 14.893 53.741 0.26415 4.3469 82.398
-4 19.151 58.156 0.29248 9.0577 84.658
-
-Within-groups Covariance Matrix:
-             X1          X2          X3          X4          X5          X6
-X1   1.0075e-01 -0.01558851  1.9653e-03  2.9589e-04 -0.01678073  7.5468e-04
-X2  -1.5589e-02  0.03774722  3.9639e-04 -1.3962e-04 -0.00833081  5.3858e-04
-X3   1.9653e-03  0.00039639  7.8972e-04  4.4363e-05 -0.00267972  2.3537e-04
-X4   2.9589e-04 -0.00013962  4.4363e-05  1.3073e-05 -0.00078407  8.5879e-05
-X5  -1.6781e-02 -0.00833081 -2.6797e-03 -7.8407e-04  0.51505366  2.8378e-02
-X6   7.5468e-04  0.00053858  2.3537e-04  8.5879e-05  0.02837750  1.1153e-02
-X7   3.0868e-03  0.00470723 -6.9139e-04 -1.3441e-04  0.00880946 -3.6488e-03
-X8   7.1794e-03  0.00961786  4.5009e-03 -5.0919e-04  0.13367484 -1.4867e-02
-X9   5.8926e-05  0.00188753 -1.0991e-04 -1.8427e-04  0.02940887 -2.9770e-03
-X10  4.6811e-03  0.00133752 -2.4714e-04 -1.1014e-04  0.00677281 -5.0715e-04
-X11 -3.1876e-03  0.00334658 -1.3911e-04 -1.1625e-04 -0.00342804 -1.9757e-03
-X12  7.4459e-01 -0.01395916  2.5029e-01  4.0035e-02 -8.81185765  2.6367e-01
-X13 -9.0527e-03  0.00037205 -8.3614e-04 -2.0133e-04  0.01736112 -7.0723e-04
-X14  5.5920e-02  0.00238741  1.2112e-03  2.8022e-04  0.00426822  4.7915e-03
-X15  2.5077e-02 -0.04378063 -1.3704e-03 -5.6372e-04  0.22138752 -3.8430e-02
-X16 -4.9743e-02 -0.00578575 -1.1361e-04 -1.9542e-04  0.03208921 -4.3213e-03
-X17  2.4926e-02 -0.01902778  7.4078e-03  6.3535e-04  0.13388265 -1.5674e-02
-X18  4.3537e-01 -0.06532118  1.1496e-02  1.9759e-03 -0.93912182 -6.2638e-02
-X19  1.4354e-01 -0.03403202 -6.1778e-03 -2.7771e-03  1.01570784  6.4485e-02
-X20 -4.7327e-01  0.12579554  2.0555e-02  2.3470e-03 -0.29362394 -3.8608e-02
-X21 -1.0441e-01 -0.02566435 -2.5838e-02 -1.5441e-03  0.21654588  3.6622e-02
-X22  3.0771e-01 -0.00852777 -6.6009e-02  2.8499e-03 -3.01570657  4.1747e-02
-X23 -6.5152e-03 -0.02455614  7.3912e-04 -6.3861e-05  0.04763930  4.6270e-03
-X24  1.3012e-02 -0.02148568  1.8808e-03  3.2722e-06 -0.17355656  5.1221e-02
-X25  4.0052e-01 -0.07965637 -1.1004e-02  3.8341e-03 -1.12813808 -5.6574e-02
-             X7          X8          X9         X10         X11        X12
-X1   0.00308676  0.00717938  5.8926e-05  0.00468110 -0.00318758   0.744590
-X2   0.00470723  0.00961786  1.8875e-03  0.00133752  0.00334658  -0.013959
-X3  -0.00069139  0.00450086 -1.0991e-04 -0.00024714 -0.00013911   0.250293
-X4  -0.00013441 -0.00050919 -1.8427e-04 -0.00011014 -0.00011625   0.040035
-X5   0.00880946  0.13367484  2.9409e-02  0.00677281 -0.00342804  -8.811858
-X6  -0.00364879 -0.01486678 -2.9770e-03 -0.00050715 -0.00197571   0.263669
-X7   0.01860202  0.02084255  6.1650e-03  0.00620821  0.00258873  -0.996138
-X8   0.02084255  0.43761650  2.8359e-02  0.00024653 -0.00887803  -3.003400
-X9   0.00616501  0.02835917  2.2814e-02  0.01099041  0.00739075  -0.897231
-X10  0.00620821  0.00024653  1.0990e-02  0.01429493  0.00745539  -0.099134
-X11  0.00258873 -0.00887803  7.3908e-03  0.00745539  0.01230347  -0.054913
-X12 -0.99613784 -3.00339973 -8.9723e-01 -0.09913367 -0.05491279 637.249036
-X13  0.00695659 -0.02774334  2.4374e-03  0.00266226  0.00305983  -2.226230
-X14  0.00192086 -0.03672969  5.9712e-03  0.00345789 -0.00254399   0.720595
-X15  0.02298077  0.14264842  7.0473e-02  0.01979711  0.02731600  -5.219577
-X16 -0.00630934  0.08475976  3.1722e-03 -0.00052350  0.00366847   0.755587
-X17 -0.00967837  0.14048414  5.9395e-03 -0.00819101 -0.01247029  -0.200470
-X18  0.02097609  0.09538331 -3.8574e-02 -0.00501225 -0.04697676  16.831103
-X19  0.11225492  0.54860260  6.5086e-02  0.04802989 -0.03462261 -29.903351
-X20 -0.07048230 -0.15379415  4.1964e-02 -0.03301947  0.07710836  23.673715
-X21 -0.06378517 -0.48947297 -6.8125e-02 -0.01061014  0.00385171 -10.510261
-X22 -0.11529920 -2.30363771 -2.3368e-01 -0.06896527  0.06494587  23.914418
-X23 -0.01364284 -0.04196183  4.8457e-03  0.00264627  0.00135742  -3.659631
-X24 -0.06684020 -0.48307193 -1.3904e-01 -0.00772448 -0.05149164  22.680721
-X25  0.13300210  0.23478052 -1.0210e-01 -0.01157060 -0.20230086  25.458870
-            X13         X14         X15         X16         X17        X18
-X1  -0.00905269  0.05591992  0.02507716 -0.04974285  0.02492615  0.4353743
-X2   0.00037205  0.00238741 -0.04378063 -0.00578575 -0.01902778 -0.0653212
-X3  -0.00083614  0.00121118 -0.00137041 -0.00011361  0.00740783  0.0114962
-X4  -0.00020133  0.00028022 -0.00056372 -0.00019542  0.00063535  0.0019759
-X5   0.01736112  0.00426822  0.22138752  0.03208921  0.13388265 -0.9391218
-X6  -0.00070723  0.00479150 -0.03842956 -0.00432129 -0.01567448 -0.0626381
-X7   0.00695659  0.00192086  0.02298077 -0.00630934 -0.00967837  0.0209761
-X8  -0.02774334 -0.03672969  0.14264842  0.08475976  0.14048414  0.0953833
-X9   0.00243742  0.00597120  0.07047310  0.00317221  0.00593954 -0.0385744
-X10  0.00266226  0.00345789  0.01979711 -0.00052350 -0.00819101 -0.0050123
-X11  0.00305983 -0.00254399  0.02731600  0.00366847 -0.01247029 -0.0469768
-X12 -2.22622981  0.72059459 -5.21957741  0.75558726 -0.20047022 16.8311031
-X13  0.11108327 -0.01567907 -0.16752185 -0.11529652 -0.04544999 -0.2552620
-X14 -0.01567907  0.09994108 -0.01569824 -0.07015906  0.02720200  0.2503806
-X15 -0.16752185 -0.01569824  2.00392037  0.37160795  0.62715042  0.7967627
-X16 -0.11529652 -0.07015906  0.37160795  0.27323468  0.19210016  0.1549772
-X17 -0.04544999  0.02720200  0.62715042  0.19210016  1.23501579  0.4138355
-X18 -0.25526201  0.25038063  0.79676269  0.15497723  0.41383545 10.3070627
-X19  0.24155488 -0.08664877  0.19326403 -0.14513626  0.52955109  2.5196441
-X20 -0.11964239 -0.26108797 -0.21426592  0.43565119 -0.28114580 -8.4471850
-X21  0.13275742  0.09850232 -0.77055987 -0.44676392 -0.66024342 -4.3822959
-X22  0.10341083  0.28327916 -3.68412649 -1.24107699 -6.14923068  4.2346310
-X23  0.04231795 -0.00981335  0.01340726 -0.08799677 -0.04630938 -0.2590000
-X24 -0.01552074 -0.07346550  0.27008407  0.13238579 -0.13664092  2.8673671
-X25 -1.31348704  0.72203060  0.33499610  1.01362410 -0.53993979 11.4867022
-            X19        X20         X21         X22         X23         X24
-X1    0.1435431  -0.473272  -0.1044050   0.3077074 -6.5152e-03  1.3012e-02
-X2   -0.0340320   0.125796  -0.0256643  -0.0085278 -2.4556e-02 -2.1486e-02
-X3   -0.0061778   0.020555  -0.0258375  -0.0660089  7.3912e-04  1.8808e-03
-X4   -0.0027771   0.002347  -0.0015441   0.0028499 -6.3861e-05  3.2722e-06
-X5    1.0157078  -0.293624   0.2165459  -3.0157066  4.7639e-02 -1.7356e-01
-X6    0.0644851  -0.038608   0.0366216   0.0417473  4.6270e-03  5.1221e-02
-X7    0.1122549  -0.070482  -0.0637852  -0.1152992 -1.3643e-02 -6.6840e-02
-X8    0.5486026  -0.153794  -0.4894730  -2.3036377 -4.1962e-02 -4.8307e-01
-X9    0.0650863   0.041964  -0.0681251  -0.2336808  4.8457e-03 -1.3904e-01
-X10   0.0480299  -0.033019  -0.0106101  -0.0689653  2.6463e-03 -7.7245e-03
-X11  -0.0346226   0.077108   0.0038517   0.0649459  1.3574e-03 -5.1492e-02
-X12 -29.9033513  23.673715 -10.5102611  23.9144176 -3.6596e+00  2.2681e+01
-X13   0.2415549  -0.119642   0.1327574   0.1034108  4.2318e-02 -1.5521e-02
-X14  -0.0866488  -0.261088   0.0985023   0.2832792 -9.8133e-03 -7.3466e-02
-X15   0.1932640  -0.214266  -0.7705599  -3.6841265  1.3407e-02  2.7008e-01
-X16  -0.1451363   0.435651  -0.4467639  -1.2410770 -8.7997e-02  1.3239e-01
-X17   0.5295511  -0.281146  -0.6602434  -6.1492307 -4.6309e-02 -1.3664e-01
-X18   2.5196441  -8.447185  -4.3822959   4.2346310 -2.5900e-01  2.8674e+00
-X19  14.5564984 -12.266488  -4.8140511  -7.1199778  5.6512e-01 -3.6612e+00
-X20 -12.2664877  21.197239  -0.4800891  -2.3537488 -8.1626e-01 -2.4633e+00
-X21  -4.8140511  -0.480089   9.6825218   5.2593408  5.1455e-01  3.2295e+00
-X22  -7.1199778  -2.353749   5.2593408  89.5162661  2.1312e-01 -1.1447e+01
-X23   0.5651189  -0.816263   0.5145496   0.2131155  3.4460e-01 -1.4205e-01
-X24  -3.6611682  -2.463317   3.2295104 -11.4467578 -1.4205e-01  2.1281e+01
-X25 -12.0846229   1.596400  -1.0406194   4.3940585 -2.2439e+00  9.8644e+00
-            X25
-X1    0.4005158
-X2   -0.0796564
-X3   -0.0110045
-X4    0.0038341
-X5   -1.1281381
-X6   -0.0565740
-X7    0.1330021
-X8    0.2347805
-X9   -0.1021017
-X10  -0.0115706
-X11  -0.2023009
-X12  25.4588697
-X13  -1.3134870
-X14   0.7220306
-X15   0.3349961
-X16   1.0136241
-X17  -0.5399398
-X18  11.4867022
-X19 -12.0846229
-X20   1.5964001
-X21  -1.0406194
-X22   4.3940585
-X23  -2.2438922
-X24   9.8643698
-X25  71.0469583
-
-Linear Coeficients:
-       X1     X2     X3     X4      X5      X6     X7     X8      X9    X10
-1 -114.98 584.26 3539.5 162830 -84.213 -1950.9 1946.3 47.889 -2806.1 1069.4
-2 -118.16 576.10 3572.0 162272 -86.893 -1944.1 1932.6 47.549 -2821.2 1080.6
-3 -119.84 576.68 3592.6 162772 -80.727 -1927.3 1942.2 49.663 -2815.1 1074.4
-4 -105.85 575.31 3494.3 163269 -86.611 -1950.6 1922.4 53.594 -2812.1 1070.4
-     X11     X12    X13    X14     X15    X16     X17   X18   X19   X20   X21
-1 3879.0 -9.4031 3709.5 2525.6 -218.27 3533.2 -486.04 48511 48726 48579 48653
-2 3865.7 -9.3867 3711.4 2527.8 -215.40 3529.9 -486.68 48538 48754 48607 48681
-3 3895.8 -9.4594 3696.4 2517.6 -218.75 3522.9 -483.99 48562 48777 48630 48703
-4 3909.2 -9.3699 3718.0 2537.3 -217.73 3536.7 -487.05 48554 48772 48624 48698
-      X22     X23    X24    X25
-1 -5.4754 -174.12 66.147 39.613
-2 -5.7252 -176.47 65.782 39.574
-3 -5.4008 -177.56 66.351 39.555
-4 -5.4476 -174.58 66.861 39.958
-
-Constants:
-       1        2        3        4 
--2617094 -2619257 -2620690 -2621479 
                         
 Apparent error rate 0.15
 
@@ -1214,36 +944,6 @@
    Yes 0.324 0.676
 
 Data:  Forest soils
-Call:
-Linda(F ~ ., data = soil1983, method = method)
-
-Prior Probabilities of Groups:
-      1       2       3 
-0.18966 0.39655 0.41379 
-
-Group means:
-       Al      Ca     Mg      K      Na
-1  64.117  7.9967 1.5450 3.9600 0.89333
-2 125.869 11.9769 2.6531 8.6756 1.00125
-3 142.650 19.2450 2.3429 8.9007 1.10643
-
-Within-groups Covariance Matrix:
-         Al        Ca       Mg         K       Na
-Al 3022.734 237.23886 45.22952 156.12659 16.64305
-Ca  237.239  43.57847  4.08960  16.96110 -0.22626
-Mg   45.230   4.08960  0.89797   2.97554  0.28453
-K   156.127  16.96110  2.97554  11.26046  0.64452
-Na   16.643  -0.22626  0.28453   0.64452  0.40939
-
-Linear Coeficients:
-         Al       Ca      Mg         K     Na
-1 -0.031062 0.361286  4.6083 -1.104355 2.1804
-2 -0.016899 0.074875  3.0648  0.024569 1.0053
-3 -0.006418 0.555117 -2.1890  0.381863 4.1906
-
-Constants:
-      1       2       3 
--4.4585 -4.9853 -7.2197 
                           
 Apparent error rate 0.3276
 
@@ -1401,45 +1101,6 @@
      O 0 1
 
 Data:  fish
-Call:
-Linda(Species ~ ., data = fish, method = method, l1med = TRUE)
-
-Prior Probabilities of Groups:
-       1        2        3        4        5        6        7 
-0.215190 0.037975 0.126582 0.069620 0.088608 0.107595 0.354430 
-
-Group means:
-    Weight Length1 Length2 Length3  Height  Width
-1 602.8421  30.242  32.968  38.279 15.1147 5.3605
-2 282.0000  24.433  26.833  29.600  8.4346 4.3926
-3 150.5000  20.300  21.944  24.531  6.5338 3.5796
-4 154.8182  18.727  20.345  22.791  8.9624 3.2207
-5   9.7417  10.883  11.467  12.592  2.0939 1.2346
-6 508.2500  41.300  44.000  46.925  7.2142 4.6369
-7 160.5000  21.166  23.128  24.534  6.2186 3.7232
-
-Within-groups Covariance Matrix:
-          Weight  Length1  Length2  Length3   Height    Width
-Weight  5489.837 182.2663 193.0905 205.7724 66.13661 34.10995
-Length1  182.266   7.6894   8.0638   8.5532  2.49205  1.30543
-Length2  193.090   8.0638   8.4916   9.0194  2.65007  1.38633
-Length3  205.772   8.5532   9.0194   9.6183  2.84968  1.48728
-Height    66.137   2.4921   2.6501   2.8497  1.01153  0.45513
-Width     34.110   1.3054   1.3863   1.4873  0.45513  0.26690
-
-Linear Coeficients:
-    Weight  Length1   Length2 Length3   Height    Width
-1 -0.23792  -5.4202 -71.45249  78.781  21.4602 -27.4539
-2 -0.37698 -33.9495  21.23223  19.473   3.3998   6.0906
-3 -0.37975 -14.7737  -7.70833  29.179   1.8817   8.4347
-4 -0.43710  -8.9415  -0.20863  11.940  23.9955   5.2911
-5 -0.19610  -6.7323  -6.69841  20.882  -5.4728  -9.6234
-6 -0.17657 -54.1758  28.62914  51.313 -38.8746 -63.4321
-7 -0.42543 -35.5939  58.10668 -15.372   3.9328  19.5463
-
-Constants:
-      1       2       3       4       5       6       7 
--266.44 -136.15 -118.11 -135.10  -46.23 -385.10 -122.21 
                           
 Apparent error rate 0.0316
 
@@ -1511,182 +1172,6 @@
   Eritrean 0.143    0.857
 
 Data:  olitos
-Call:
-Linda(grp ~ ., data = olitos, method = method, l1med = TRUE)
-
-Prior Probabilities of Groups:
-       1        2        3        4 
-0.416667 0.208333 0.283333 0.091667 
-
-Group means:
-       X1     X2      X3        X4     X5      X6     X7     X8      X9     X10
-1 0.64294 61.921 0.13791 0.0081515 12.248 0.80324 2.3245 6.3305 0.73445 0.51021
-2 0.46111 61.683 0.15479 0.0093684 12.438 0.78374 2.1369 6.0727 0.63216 0.45216
-3 0.81050 61.874 0.17840 0.0099500 13.900 1.07350 2.1825 7.2095 0.62950 0.44100
-4 1.31500 61.449 0.16260 0.0126000 12.757 0.99300 2.0330 6.2830 0.69700 0.52900
-      X11    X12    X13     X14    X15    X16    X17    X18    X19    X20
-1 0.43142 243.62 3.3090 0.98597 89.735 95.558 2.0217 15.591 27.181 39.963
-2 0.38058 260.69 3.3727 0.88426 91.844 95.704 3.4858 11.064 23.417 47.387
-3 0.38900 242.41 3.0110 1.07600 89.299 95.847 3.0565 16.396 32.557 35.792
-4 0.48900 279.81 3.1830 1.29900 89.477 95.499 1.5950 12.853 23.303 44.931
-     X21    X22     X23     X24    X25
-1 17.294 66.468 0.48324  2.3235 81.471
-2 18.160 52.869 0.38111  1.6337 74.808
-3 15.281 53.653 0.28300  4.2515 82.525
-4 18.939 58.287 0.03300 12.2430 88.203
-
-Within-groups Covariance Matrix:
-             X1          X2          X3          X4          X5          X6
-X1   0.08192984 -1.4804e-02  1.6222e-03  2.5574e-04 -0.01926070  0.00143293
-X2  -0.01480374  3.0815e-02  4.1084e-04 -8.8101e-05 -0.01270958  0.00042495
-X3   0.00162220  4.1084e-04  6.6120e-04  3.3939e-05 -0.00180448  0.00024347
-X4   0.00025574 -8.8101e-05  3.3939e-05  9.7372e-06 -0.00047911  0.00008244
-X5  -0.01926070 -1.2710e-02 -1.8045e-03 -4.7911e-04  0.40752640  0.02398390
-X6   0.00143293  4.2495e-04  2.4347e-04  8.2440e-05  0.02398390  0.00923965
-X7   0.00184589  3.3208e-03 -5.2715e-04 -9.7685e-05  0.00323853 -0.00310095
-X8  -0.00228054  3.9423e-03  3.9439e-03 -3.6048e-04  0.09316455 -0.01139272
-X9  -0.00199165  6.1845e-04 -8.5603e-05 -1.4661e-04  0.02106528 -0.00223360
-X10  0.00257748  6.0951e-04 -2.1561e-04 -9.6934e-05  0.00359901 -0.00017581
-X11 -0.00340244  2.6140e-03 -1.1022e-04 -9.7441e-05 -0.00468098 -0.00154221
-X12  0.72257320  5.6434e-02  2.0568e-01  3.1016e-02 -6.91486269  0.21413113
-X13 -0.00930585  8.2797e-04 -8.3881e-04 -2.0912e-04  0.01706489  0.00011381
-X14  0.04303799 -1.8159e-04  1.0473e-03  2.6788e-04 -0.00576272  0.00484686
-X15  0.02062031 -3.6173e-02 -1.5239e-03 -5.8544e-04  0.20597410 -0.03232787
-X16 -0.03561929 -4.2594e-03  1.8684e-04 -1.0880e-04  0.02798146 -0.00570779
-X17  0.01711872 -1.5022e-02  5.8357e-03  3.8079e-04  0.13128713 -0.01182319
-X18  0.37133025 -6.9297e-02  1.1156e-02  2.0110e-03 -0.84907264 -0.05901073
-X19  0.08525820 -6.8412e-02 -2.4834e-03 -1.3360e-03  0.67816743  0.05310675
-X20 -0.36324847  1.6458e-01  1.2916e-02  3.5649e-04  0.02189381 -0.02559493
-X21 -0.09342145 -2.6850e-02 -2.1599e-02 -1.0308e-03  0.14908644  0.03146858
-X22  0.26007045 -2.2426e-02 -5.5114e-02  2.8727e-03 -2.62480736  0.03052551
-X23 -0.01063391 -2.3502e-02  5.9722e-04 -1.4575e-05  0.03270833  0.00471201
-X24  0.06866746  5.0890e-03  2.2764e-03 -1.4651e-04 -0.07352365  0.03340318
-X25  0.40646770 -5.3335e-02 -7.0022e-03  3.4926e-03 -0.94794120 -0.06422767
-             X7          X8          X9         X10         X11        X12
-X1   1.8459e-03 -0.00228054 -1.9916e-03  2.5775e-03 -3.4024e-03   0.722573
-X2   3.3208e-03  0.00394227  6.1845e-04  6.0951e-04  2.6140e-03   0.056434
-X3  -5.2715e-04  0.00394388 -8.5603e-05 -2.1561e-04 -1.1022e-04   0.205684
-X4  -9.7685e-05 -0.00036048 -1.4661e-04 -9.6934e-05 -9.7441e-05   0.031016
-X5   3.2385e-03  0.09316455  2.1065e-02  3.5990e-03 -4.6810e-03  -6.914863
-X6  -3.1009e-03 -0.01139272 -2.2336e-03 -1.7581e-04 -1.5422e-03   0.214131
-X7   1.4626e-02  0.01416383  4.4486e-03  4.2667e-03  1.5223e-03  -0.747339
-X8   1.4164e-02  0.35090522  1.9188e-02 -3.5397e-03 -9.8703e-03  -2.122989
-X9   4.4486e-03  0.01918824  1.8282e-02  8.4147e-03  5.7831e-03  -0.689536
-X10  4.2667e-03 -0.00353975  8.4147e-03  1.0979e-02  5.6659e-03  -0.024538
-X11  1.5223e-03 -0.00987030  5.7831e-03  5.6659e-03  1.0045e-02   0.010425
-X12 -7.4734e-01 -2.12298901 -6.8954e-01 -2.4538e-02  1.0425e-02 528.021699
-X13  5.6182e-03 -0.02617066  1.4205e-03  1.2657e-03  2.0122e-03  -1.880820
-X14  1.4474e-04 -0.04271352  2.4885e-03  1.1360e-03 -3.1714e-03   0.793783
-X15  2.3251e-02  0.12988364  6.1204e-02  1.8438e-02  2.5089e-02  -4.937326
-X16 -4.7036e-03  0.08431243  5.6934e-03  2.0999e-03  4.4307e-03   0.559833
-X17 -7.3926e-03  0.11992315  3.8683e-03 -9.2427e-03 -1.1110e-02  -0.529966
-X18  5.9929e-03  0.06325323 -3.8778e-02 -1.1760e-02 -4.3369e-02  14.665332
-X19  6.4989e-02  0.33281344  2.5819e-02  1.9600e-02 -4.2651e-02 -22.404179
-X20 -1.9352e-02  0.03831912  7.2476e-02 -4.6512e-03  8.0268e-02  16.067296
-X21 -5.1676e-02 -0.43417424 -5.9466e-02 -3.2553e-03  5.7380e-03  -8.334793
-X22 -1.0003e-01 -1.97988438 -2.0294e-01 -5.6006e-02  5.8615e-02  20.456167
-X23 -1.0828e-02 -0.04493945  2.1193e-03  2.5431e-03  1.6178e-03  -3.069866
-X24 -5.1168e-02 -0.29807074 -9.1349e-02  7.5238e-03 -3.8252e-02  18.339747
-X25  9.9946e-02  0.29267421 -6.4705e-02 -7.2911e-03 -1.7524e-01  21.956647
-            X13         X14         X15         X16         X17        X18
-X1  -0.00930585  0.04303799  0.02062031 -0.03561929  0.01711872  0.3713302
-X2   0.00082797 -0.00018159 -0.03617337 -0.00425936 -0.01502219 -0.0692965
-X3  -0.00083881  0.00104734 -0.00152393  0.00018684  0.00583572  0.0111562
-X4  -0.00020912  0.00026788 -0.00058544 -0.00010880  0.00038079  0.0020110
-X5   0.01706489 -0.00576272  0.20597410  0.02798146  0.13128713 -0.8490726
-X6   0.00011381  0.00484686 -0.03232787 -0.00570779 -0.01182319 -0.0590107
-X7   0.00561818  0.00014474  0.02325121 -0.00470365 -0.00739263  0.0059929
-X8  -0.02617066 -0.04271352  0.12988364  0.08431243  0.11992315  0.0632532
-X9   0.00142053  0.00248849  0.06120418  0.00569339  0.00386827 -0.0387778
-X10  0.00126574  0.00113596  0.01843799  0.00209990 -0.00924273 -0.0117601
-X11  0.00201218 -0.00317144  0.02508867  0.00443067 -0.01110991 -0.0433685
-X12 -1.88082048  0.79378336 -4.93732591  0.55983317 -0.52996566 14.6653317
-X13  0.09251619 -0.01474724 -0.14362180 -0.09332066 -0.04401368 -0.2050390
-X14 -0.01474724  0.07935056 -0.01141736 -0.05297175  0.02142245  0.2064316
-X15 -0.14362180 -0.01141736  1.69199646  0.32096806  0.52025752  0.7029083
-X16 -0.09332066 -0.05297175  0.32096806  0.21779836  0.17675411  0.1056873
-X17 -0.04401368  0.02142245  0.52025752  0.17675411  1.02863233  0.3693946
-X18 -0.20503902  0.20643158  0.70290826  0.10568728  0.36939462  8.4907734
-X19  0.21803216 -0.13770133  0.25126309 -0.11319404  0.51609127  1.7133521
-X20 -0.13288099 -0.14448419 -0.30575093  0.39025679 -0.35944745 -6.5189315
-X21  0.11987895  0.07586182 -0.64857504 -0.38269413 -0.52646722 -3.6869334
-X22  0.10834603  0.23361478 -3.14523066 -1.08673427 -5.21678265  3.4097756
-X23  0.03635360 -0.01412392  0.00659318 -0.06858385 -0.04017227 -0.2118403
-X24  0.00218340  0.00325933  0.25430828  0.02288744 -0.07006603  2.4332616
-X25 -1.09814009  0.67901476  0.37604041  0.74315024 -0.37538529  9.4971076
-            X19         X20        X21        X22         X23         X24
-X1    0.0852582 -0.36324847 -0.0934214  0.2600705 -1.0634e-02  0.06866746
-X2   -0.0684123  0.16457798 -0.0268500 -0.0224258 -2.3502e-02  0.00508904
-X3   -0.0024834  0.01291620 -0.0215995 -0.0551143  5.9722e-04  0.00227645
-X4   -0.0013360  0.00035649 -0.0010308  0.0028727 -1.4575e-05 -0.00014651
-X5    0.6781674  0.02189381  0.1490864 -2.6248074  3.2708e-02 -0.07352365
-X6    0.0531067 -0.02559493  0.0314686  0.0305255  4.7120e-03  0.03340318
-X7    0.0649890 -0.01935190 -0.0516765 -0.1000276 -1.0828e-02 -0.05116827
-X8    0.3328134  0.03831912 -0.4341742 -1.9798844 -4.4939e-02 -0.29807074
-X9    0.0258192  0.07247619 -0.0594655 -0.2029397  2.1193e-03 -0.09134914
-X10   0.0196003 -0.00465122 -0.0032553 -0.0560061  2.5431e-03  0.00752375
-X11  -0.0426508  0.08026811  0.0057380  0.0586147  1.6178e-03 -0.03825213
-X12 -22.4041788 16.06729645 -8.3347928 20.4561669 -3.0699e+00 18.33974674
-X13   0.2180322 -0.13288099  0.1198789  0.1083460  3.6354e-02  0.00218340
-X14  -0.1377013 -0.14448419  0.0758618  0.2336148 -1.4124e-02  0.00325933
-X15   0.2512631 -0.30575093 -0.6485750 -3.1452307  6.5932e-03  0.25430828
-X16  -0.1131940  0.39025679 -0.3826941 -1.0867343 -6.8584e-02  0.02288744
-X17   0.5160913 -0.35944745 -0.5264672 -5.2167826 -4.0172e-02 -0.07006603
-X18   1.7133521 -6.51893147 -3.6869334  3.4097756 -2.1184e-01  2.43326162
-X19  11.1556891 -8.64959122 -4.2242019 -6.4452276  4.3858e-01 -2.60113574
-X20  -8.6495912 15.39607917 -0.2206486 -1.3792663 -6.3755e-01 -2.71395816
-X21  -4.2242019 -0.22064859  8.1314122  4.4195292  4.1056e-01  2.88241171
-X22  -6.4452276 -1.37926631  4.4195292 76.1419157  1.5632e-01 -9.66199793
-X23   0.4385776 -0.63754801  0.4105624  0.1563179  2.8153e-01 -0.03108501
-X24  -2.6011357 -2.71395816  2.8824117 -9.6619979 -3.1085e-02 17.29935324
-X25 -10.2482366  1.44006845 -0.6829641  3.7677482 -1.7867e+00  7.37053118
-            X25
-X1    0.4064677
-X2   -0.0533349
-X3   -0.0070022
-X4    0.0034926
-X5   -0.9479412
-X6   -0.0642277
-X7    0.0999460
-X8    0.2926742
-X9   -0.0647053
-X10  -0.0072911
-X11  -0.1752351
-X12  21.9566466
-X13  -1.0981401
-X14   0.6790148
-X15   0.3760404
-X16   0.7431502
-X17  -0.3753853
-X18   9.4971076
-X19 -10.2482366
-X20   1.4400685
-X21  -0.6829641
-X22   3.7677482
-X23  -1.7866646
-X24   7.3705312
-X25  58.9835178
-
-Linear Coeficients:
-      X1     X2     X3      X4      X5    X6    X7      X8      X9   X10    X11
-1 1515.4 3105.1 170261 -757983 -118.08 17863 19830 -9609.6 -173.38 12947 -14834
-2 1507.5 3088.1 170294 -758357 -124.37 17881 19807 -9608.9 -196.14 12975 -14852
-3 1511.7 3099.7 170320 -758136 -112.61 17883 19827 -9607.6 -182.20 12946 -14813
-4 1523.8 3087.4 170210 -757574 -123.78 17877 19799 -9603.5 -180.18 12945 -14804
-     X12    X13    X14    X15     X16     X17     X18     X19     X20     X21
-1 74.898 -10999 -20401 309.54 -3263.7 -1666.4 2807654 2805955 2804668 2808095
-2 74.839 -11003 -20405 314.29 -3277.4 -1665.6 2807622 2805923 2804636 2808063
-3 74.865 -11011 -20407 308.39 -3271.2 -1664.7 2807685 2805986 2804698 2808125
-4 74.876 -10992 -20391 310.95 -3263.1 -1667.1 2807388 2805692 2804404 2807830
-      X22     X23     X24     X25
-1 -711.39 -5316.5 -1443.7 -358.28
-2 -711.64 -5321.7 -1443.9 -358.35
-3 -711.31 -5319.2 -1443.5 -358.33
-4 -711.36 -5318.3 -1442.6 -357.88
-
-Constants:
-         1          2          3          4 
--140244417 -140239143 -140246342 -140217059 
                           
 Apparent error rate 0.1667
 
@@ -1844,36 +1329,6 @@
    Yes 0.426 0.574
 
 Data:  Forest soils
-Call:
-Linda(F ~ ., data = soil1983, method = method)
-
-Prior Probabilities of Groups:
-      1       2       3 
-0.18966 0.39655 0.41379 
-
-Group means:
-       Al      Ca     Mg      K      Na
-1  55.889  6.8776 1.4328 3.6186 0.80101
-2 113.714 10.4451 2.5170 7.9269 0.99434
-3 131.297 17.5642 2.1886 8.0061 1.36351
-
-Within-groups Covariance Matrix:
-         Al       Ca       Mg       K       Na
-Al 2958.984 146.3848 35.67352 97.1776 33.21281
-Ca  146.385  40.0363  2.52523  9.2872  1.04648
-Mg   35.674   2.5252  0.64662  1.8290  0.46809
-K    97.178   9.2872  1.82900  6.7493  1.32264
-Na   33.213   1.0465  0.46809  1.3226  0.66313
-
-Linear Coeficients:
-          Al        Ca       Mg        K        Na
-1 -0.0261119  0.082137  4.56023 -0.44898  0.062663
-2 -0.0019118 -0.095129  4.55347  0.63502 -2.735430
-3  0.0163719  0.217866 -0.51718  0.89379 -0.525272
-
-Constants:
-      1       2       3 
--3.6951 -7.2068 -6.5243 
                           
 Apparent error rate 0.3448
 
@@ -2031,45 +1486,6 @@
      O 0.5 0.5
 
 Data:  fish
-Call:
-Linda(Species ~ ., data = fish, method = method, l1med = TRUE)
-
-Prior Probabilities of Groups:
-       1        2        3        4        5        6        7 
-0.215190 0.037975 0.126582 0.069620 0.088608 0.107595 0.354430 
-
-Group means:
-   Weight Length1 Length2 Length3  Height  Width
-1 591.024  30.005  32.749  37.938 15.1797 5.2172
-2 421.653  26.601  29.097  32.004  9.4581 5.2858
-3 124.071  19.976  21.557  24.080  6.2996 3.5111
-4 122.562  18.889  20.596  23.145  9.2898 3.0958
-5 -12.425  10.626  11.187  12.424  1.9996 1.1540
-6 487.575  39.404  41.928  44.920  6.6346 4.3744
-7 183.595  22.083  24.069  25.528  6.4907 3.9123
-
-Within-groups Covariance Matrix:
-          Weight  Length1  Length2  Length3   Height    Width
-Weight  7021.920 177.0032 185.0010 190.9862 58.39791 30.69782
-Length1  177.003   8.8556   7.9392   8.2293  2.15028  1.18984
-Length2  185.001   7.9392   9.6269   8.5715  2.25648  1.24249
-Length3  190.986   8.2293   8.5715  10.4566  2.35952  1.29491
-Height    58.398   2.1503   2.2565   2.3595  0.97108  0.34335
-Width     30.698   1.1898   1.2425   1.2949  0.34335  0.27910
-
-Linear Coeficients:
-     Weight  Length1  Length2 Length3  Height    Width
-1 -0.110611 -1.28881 -0.31778  1.9242 19.4737   4.8834
-2 -0.106313 -0.38220  0.59314  1.4971  6.9843  14.0826
-3 -0.137209  0.31728  0.83616  1.6815  4.9135   8.7503
-4 -0.156884 -0.22387  0.35526  1.1772 14.0539   4.9689
-5 -0.078642  0.87330  0.95699  1.3532 -0.2385  -1.1836
-6 -0.086181  2.71710  3.38858  4.0720 -8.0594 -10.4936
-7 -0.129742  0.49725  1.24860  1.3288  3.8019   9.7672
-
-Constants:
-       1        2        3        4        5        6        7 
--141.352  -78.607  -56.819  -81.189  -20.389 -147.558  -58.049 
                           
 Apparent error rate 0.2532
 
@@ -2141,182 +1557,6 @@
   Eritrean 0.143    0.857
 
 Data:  olitos
-Call:
-Linda(grp ~ ., data = olitos, method = method, l1med = TRUE)
-
-Prior Probabilities of Groups:
-       1        2        3        4 
-0.416667 0.208333 0.283333 0.091667 
-
-Group means:
-       X1     X2      X3        X4     X5      X6     X7     X8      X9     X10
-1 0.57848 61.931 0.13613 0.0080943 12.266 0.79998 2.3538 6.4229 0.73425 0.51653
-2 0.41271 61.712 0.15321 0.0085444 12.481 0.78179 2.1069 6.1355 0.62067 0.39948
-3 0.76587 61.890 0.18058 0.0111634 13.704 1.07124 2.1687 7.1978 0.62366 0.43460
-4 1.50953 61.549 0.16553 0.0125930 13.033 0.93800 2.0928 6.8736 0.81927 0.61150
-      X11    X12    X13     X14    X15    X16    X17    X18    X19    X20
-1 0.44425 239.60 3.3012 0.97354 89.669 95.628 2.0787 15.485 26.808 40.526
-2 0.35371 258.98 3.3113 0.88000 91.936 95.794 3.4611 10.565 22.829 47.964
-3 0.37614 246.55 2.9909 1.04678 89.533 95.893 3.3628 16.463 31.488 37.225
-4 0.53457 271.90 3.2722 1.57500 89.531 95.127 1.9444 12.591 24.918 43.417
-     X21    X22     X23     X24    X25
-1 17.049 67.055 0.24789 1.62677 83.307
-2 18.598 54.067 0.35896 0.14332 74.757
-3 14.776 54.647 0.12642 3.07548 83.215
-4 19.034 59.061 0.15475 7.78631 85.475
-
-Within-groups Covariance Matrix:
-             X1          X2          X3          X4          X5          X6
-X1   0.07026643  2.9284e-03  1.2919e-03  1.3476e-04 -3.8439e-02 -3.5355e-03
-X2   0.00292844  3.6549e-02  6.7508e-04 -6.1733e-05  6.3577e-04  3.0376e-03
-X3   0.00129186  6.7508e-04  8.4730e-04  3.7256e-05 -4.4628e-03  4.0193e-05
-X4   0.00013476 -6.1733e-05  3.7256e-05  1.4259e-05 -1.2395e-03  2.4965e-05
-X5  -0.03843914  6.3577e-04 -4.4628e-03 -1.2395e-03  5.8239e-01  2.5651e-02
-X6  -0.00353547  3.0376e-03  4.0193e-05  2.4965e-05  2.5651e-02  1.0992e-02
-X7   0.00295072  3.8053e-03 -7.2169e-04 -1.5508e-04  1.6478e-02 -1.5194e-03
-X8   0.00283960  8.9169e-03  5.4856e-03 -7.9035e-04  1.4644e-01 -7.4050e-03
-X9   0.00244694 -1.8112e-03 -9.4468e-04 -2.4174e-04  2.9039e-02 -3.8780e-03
-X10  0.00290299  9.4062e-04 -8.5175e-04 -1.7682e-04  8.4395e-03 -1.6380e-03
-X11 -0.00091611  1.1171e-03 -5.7218e-04 -1.2099e-04 -7.7390e-03 -3.3685e-03
-X12  0.58429553 -5.4768e-01  2.5298e-01  4.5444e-02 -1.0894e+01 -1.1460e-01
-X13 -0.00833286 -9.3956e-04 -5.5959e-04 -2.4798e-04  4.9798e-02  4.0008e-03
-X14  0.04805180  6.5173e-03 -7.4565e-05  1.8539e-04 -2.9817e-02  1.3576e-03
-X15  0.04572154 -2.8174e-02 -2.9003e-03 -6.2952e-04  1.4268e-01 -3.6566e-02
-X16 -0.03658370 -6.1189e-03 -1.9759e-04 -1.7575e-04  2.6851e-02 -7.6461e-03
-X17  0.03805157 -7.0554e-03  6.4130e-03  3.4303e-04  5.2033e-02 -1.0451e-02
-X18  0.39218833  6.2615e-03  1.6451e-02  1.9191e-03 -9.0412e-01 -6.7846e-02
-X19  0.05426436 -3.6340e-03 -6.2620e-03 -4.9357e-03  1.3242e+00  7.6692e-02
-X20 -0.30408813 -3.9107e-02  1.3762e-02  4.4475e-03 -5.1457e-01 -7.4021e-02
-X21 -0.10811309  2.6891e-02 -2.3111e-02 -1.2136e-03  3.0975e-02  5.2871e-02
-X22  0.09001377  1.2997e-01 -4.2299e-02  5.2076e-03 -3.0387e+00  1.0615e-01
-X23 -0.00761313 -1.4307e-02  8.3945e-04 -2.3034e-04  2.1582e-02  5.2286e-03
-X24  0.13591519  1.3978e-02  5.6393e-03  9.4096e-04 -1.2509e-01 -3.2760e-02
-X25  0.31404705 -1.0808e-01 -1.3938e-02  4.8760e-03 -1.3501e+00 -8.5281e-02
-             X7          X8          X9         X10         X11        X12
-X1   0.00295072  0.00283960  0.00244694  0.00290299 -0.00091611   0.584296
-X2   0.00380528  0.00891694 -0.00181117  0.00094062  0.00111715  -0.547680
-X3  -0.00072169  0.00548559 -0.00094468 -0.00085175 -0.00057218   0.252982
-X4  -0.00015508 -0.00079035 -0.00024174 -0.00017682 -0.00012099   0.045444
-X5   0.01647814  0.14644485  0.02903909  0.00843948 -0.00773898 -10.893614
-X6  -0.00151939 -0.00740505 -0.00387802 -0.00163802 -0.00336853  -0.114599
-X7   0.01695204  0.00253165  0.00632792  0.00640651  0.00446549  -1.035402
-X8   0.00253165  0.38285298  0.02882781 -0.00030976 -0.00654781  -3.332953
-X9   0.00632792  0.02882781  0.02240626  0.00881582  0.00632909  -1.452543
-X10  0.00640651 -0.00030976  0.00881582  0.01369050  0.00811528  -0.361155
-X11  0.00446549 -0.00654781  0.00632909  0.00811528  0.01456446  -0.167014
-X12 -1.03540231 -3.33295326 -1.45254268 -0.36115513 -0.16701360 689.274282
-X13  0.00689350  0.00542624  0.00404233  0.00414138  0.00041255  -2.924782
-X14  0.00621576 -0.05229536  0.00142731  0.00082734 -0.00452817   0.341211
-X15  0.02466410  0.06811830  0.07848539  0.01871338  0.03135574  -5.726635
-X16 -0.00801723  0.05383160  0.00733279  0.00047709  0.01068577   1.918601
-X17 -0.01502768  0.09494255 -0.01530594 -0.02579079 -0.01787611   1.678793
-X18  0.00319394  0.03511581  0.04608812  0.00930804  0.03049907  25.936956
-X19  0.06954050  0.66370101  0.10250938  0.04587327  0.02537178 -19.790264
-X20 -0.05734134 -0.36252643 -0.05842137 -0.04140852  0.00891549  13.263963
-X21 -0.00443007 -0.35306087 -0.07758298 -0.00475145 -0.04847910 -15.502539
-X22 -0.09079409 -2.04886495 -0.13676322  0.00408904  0.00333079  21.482628
-X23 -0.00546682  0.02999962 -0.00014424 -0.00203100 -0.00358038  -2.076408
-X24  0.00915803 -0.04631992 -0.01726963  0.04453148  0.02002928  12.389094
-X25  0.06458498 -0.82093402  0.00524037  0.03127067 -0.10902905  28.428140
-            X13         X14         X15         X16         X17        X18
-X1  -0.00833286  4.8052e-02  0.04572154 -0.03658370  0.03805157  0.3921883
-X2  -0.00093956  6.5173e-03 -0.02817364 -0.00611893 -0.00705538  0.0062615
-X3  -0.00055959 -7.4565e-05 -0.00290027 -0.00019759  0.00641298  0.0164505
-X4  -0.00024798  1.8539e-04 -0.00062952 -0.00017575  0.00034303  0.0019191
-X5   0.04979790 -2.9817e-02  0.14268105  0.02685101  0.05203277 -0.9041211
-X6   0.00400079  1.3576e-03 -0.03656605 -0.00764608 -0.01045057 -0.0678461
-X7   0.00689350  6.2158e-03  0.02466410 -0.00801723 -0.01502768  0.0031939
-X8   0.00542624 -5.2295e-02  0.06811830  0.05383160  0.09494255  0.0351158
-X9   0.00404233  1.4273e-03  0.07848539  0.00733279 -0.01530594  0.0460881
-X10  0.00414138  8.2734e-04  0.01871338  0.00047709 -0.02579079  0.0093080
-X11  0.00041255 -4.5282e-03  0.03135574  0.01068577 -0.01787611  0.0304991
-X12 -2.92478158  3.4121e-01 -5.72663473  1.91860092  1.67879350 25.9369562
-X13  0.09908088 -1.8617e-02 -0.13543555 -0.08706918  0.00944870 -0.3892263
-X14 -0.01861681  9.4223e-02  0.01207116 -0.05453281 -0.01519205  0.3535993
-X15 -0.13543555  1.2071e-02  2.08454277  0.29655848  0.32606490  1.6484168
-X16 -0.08706918 -5.4533e-02  0.29655848  0.22083608  0.06069037  0.2261266
-X17  0.00944870 -1.5192e-02  0.32606490  0.06069037  0.88775362  0.4522207
-X18 -0.38922628  3.5360e-01  1.64841683  0.22612662  0.45222070 10.4713538
-X19  0.04326037 -8.2725e-03  0.94607953  0.17250537  0.95458304  2.6822507
-X20  0.00272135 -2.9863e-01 -1.16979571  0.20852467 -0.60386264 -6.9263518
-X21  0.28093392 -1.3563e-02 -1.04259061 -0.48624333 -0.67626710 -4.4006632
-X22  0.04371283  4.5321e-01 -2.16057206 -0.79996996 -3.91936562  4.9565736
-X23  0.02760512 -2.3806e-02  0.05880522 -0.01804190  0.05015312  0.0269735
-X24  0.05697178  8.1769e-02  0.14261149 -0.22939011  0.03628105  0.7701978
-X25 -0.82089435  6.9160e-01  0.07352628  0.13351475 -1.89861638  7.1687089
-            X19         X20         X21         X22         X23         X24
-X1    0.0542644  -0.3040881  -0.1081131   0.0900138 -0.00761313  0.13591519
-X2   -0.0036340  -0.0391066   0.0268914   0.1299713 -0.01430732  0.01397844
-X3   -0.0062620   0.0137618  -0.0231109  -0.0422988  0.00083945  0.00563934
-X4   -0.0049357   0.0044475  -0.0012136   0.0052076 -0.00023034  0.00094096
-X5    1.3241996  -0.5145715   0.0309752  -3.0386542  0.02158221 -0.12508773
-X6    0.0766920  -0.0740215   0.0528713   0.1061510  0.00522861 -0.03276002
-X7    0.0695405  -0.0573413  -0.0044301  -0.0907941 -0.00546682  0.00915803
-X8    0.6637010  -0.3625264  -0.3530609  -2.0488649  0.02999962 -0.04631992
-X9    0.1025094  -0.0584214  -0.0775830  -0.1367632 -0.00014424 -0.01726963
-X10   0.0458733  -0.0414085  -0.0047514   0.0040890 -0.00203100  0.04453148
-X11   0.0253718   0.0089155  -0.0484791   0.0033308 -0.00358038  0.02002928
-X12 -19.7902637  13.2639629 -15.5025392  21.4826279 -2.07640767 12.38909430
-X13   0.0432604   0.0027213   0.2809339   0.0437128  0.02760512  0.05697178
-X14  -0.0082725  -0.2986295  -0.0135628   0.4532089 -0.02380556  0.08176911
-X15   0.9460795  -1.1697957  -1.0425906  -2.1605721  0.05880522  0.14261149
-X16   0.1725054   0.2085247  -0.4862433  -0.7999700 -0.01804190 -0.22939011
-X17   0.9545830  -0.6038626  -0.6762671  -3.9193656  0.05015312  0.03628105
-X18   2.6822507  -6.9263518  -4.4006632   4.9565736  0.02697346  0.77019784
-X19  14.8579414 -11.8541120  -3.6895829 -11.8840576  0.49735330  0.01268716
-X20 -11.8541120  20.8729303  -0.7401364  -2.8422653 -0.75267097 -0.64317299
-X21  -3.6895829  -0.7401364   8.9371606   9.4530745  0.22554362 -0.00364040
-X22 -11.8840576  -2.8422653   9.4530745  86.4457994  0.46878911 -5.55596815
-X23   0.4973533  -0.7526710   0.2255436   0.4687891  0.17338427 -0.15581540
-X24   0.0126872  -0.6431730  -0.0036404  -5.5559681 -0.15581540  4.81128515
-X25  -9.4946373   4.5516053  -2.2135389  14.7807456 -0.99001453  0.37871730
-           X25
-X1   0.3140470
-X2  -0.1080785
-X3  -0.0139383
-X4   0.0048760
-X5  -1.3500531
-X6  -0.0852815
-X7   0.0645850
-X8  -0.8209340
-X9   0.0052404
-X10  0.0312707
-X11 -0.1090291
-X12 28.4281395
-X13 -0.8208944
-X14  0.6916019
-X15  0.0735263
-X16  0.1335148
-X17 -1.8986164
-X18  7.1687089
-X19 -9.4946373
-X20  4.5516053
-X21 -2.2135389
-X22 14.7807456
-X23 -0.9900145
-X24  0.3787173
-X25 50.2533098
-
-Linear Coeficients:
-      X1     X2      X3    X4      X5      X6      X7      X8     X9     X10
-1 573.13 2468.2 -3379.4 49561 -45.199 -847.37 -157.11 -34.826 233.53 -576.80
-2 571.33 2466.2 -3367.5 49153 -45.272 -848.95 -173.89 -35.290 226.18 -572.51
-3 575.18 2462.3 -3340.7 49755 -42.298 -819.14 -165.34 -32.634 231.53 -576.78
-4 590.25 2457.2 -3380.4 49895 -42.380 -825.02 -176.70 -30.494 240.59 -582.13
-      X11    X12    X13    X14     X15    X16     X17     X18    X19    X20
-1 -546.64 5.7635 1942.9 896.61 0.95013 1690.3 -174.73 -42.305 24.935 1.4662
-2 -548.95 5.8500 1944.7 900.72 3.79510 1686.3 -174.61 -42.693 24.825 1.7814
-3 -531.83 5.7329 1937.4 896.08 0.39263 1689.9 -172.75 -42.195 24.887 1.4270
-4 -515.52 5.8465 1943.8 903.98 1.18144 1692.2 -174.52 -43.452 25.033 1.7620
-     X21     X22    X23     X24    X25
-1 26.117 -12.001 523.28  9.6027 24.342
-2 26.381 -12.216 523.15  8.6470 24.244
-3 26.221 -11.937 521.44 10.1353 24.447
-4 26.538 -11.913 525.80 10.7363 24.560
-
-Constants:
-      1       2       3       4 
--161427 -161153 -161045 -161095 
                          
 Apparent error rate 0.125
 
@@ -2474,36 +1714,6 @@
    Yes 0.324 0.676
 
 Data:  Forest soils
-Call:
-Linda(F ~ ., data = soil1983, method = method)
-
-Prior Probabilities of Groups:
-      1       2       3 
-0.18966 0.39655 0.41379 
-
-Group means:
-      Al      Ca     Mg      K      Na
-1 108.44  9.4222 2.0253 4.9449 2.12259
-2 116.62 11.6676 2.5472 8.0291 0.90617
-3 152.37 21.3165 2.4082 9.1044 1.26106
-
-Within-groups Covariance Matrix:
-         Al       Ca       Mg         K       Na
-Al 3457.444 288.7174 46.58105 143.46734 30.38338
-Ca  288.717  49.3144  4.15531  14.27911  1.36330
-Mg   46.581   4.1553  0.84148   2.43501  0.42092
-K   143.467  14.2791  2.43501   8.46015  0.94133
-Na   30.383   1.3633  0.42092   0.94133  0.98376
-
-Linear Coeficients:
-         Al        Ca      Mg        K       Na
-1 -0.018827  0.085421  3.3014 -0.36380 1.556268
-2 -0.034441 -0.015588  2.8924  0.71779 0.082066
-3 -0.018761  0.283078 -1.9584  1.36929 0.996724
-
-Constants:
-      1       2       3 
--5.1395 -5.4282 -6.9739 
                           
 Apparent error rate 0.3621
 
@@ -2661,45 +1871,6 @@
      O 0 1
 
 Data:  fish
-Call:
-Linda(Species ~ ., data = fish, method = method, l1med = TRUE)
-
-Prior Probabilities of Groups:
-       1        2        3        4        5        6        7 
-0.215190 0.037975 0.126582 0.069620 0.088608 0.107595 0.354430 
-
-Group means:
-     Weight Length1 Length2 Length3  Height  Width
-1 604.09773  30.037  32.797  37.989 15.1981 5.2843
-2 434.72736  26.633  29.145  32.055  9.4765 5.3528
-3 137.14510  20.008  21.605  24.131  6.3181 3.5781
-4 135.63586  18.921  20.644  23.196  9.3083 3.1628
-5   0.64897  10.658  11.235  12.475  2.0180 1.2210
-6 500.64921  39.436  41.976  44.971  6.6530 4.4415
-7 196.66853  22.115  24.117  25.579  6.5092 3.9793
-
-Within-groups Covariance Matrix:
-           Weight  Length1  Length2  Length3    Height    Width
-Weight  14221.727 349.8353 371.8026 397.1191 140.25559 71.62696
-Length1   349.835  11.1615  11.8294  12.5746   4.02800  2.10646
-Length2   371.803  11.8294  12.5752  13.3701   4.30843  2.24877
-Length3   397.119  12.5746  13.3701  14.2710   4.61999  2.40091
-Height    140.256   4.0280   4.3084   4.6200   1.71309  0.81013
-Width      71.627   2.1065   2.2488   2.4009   0.81013  0.46889
-
-Linear Coeficients:
-     Weight   Length1   Length2 Length3    Height    Width
-1 -0.201933  -8.25890 -41.35965 49.9172  20.92722 -14.1765
-2 -0.154022 -19.63121   0.30591 22.4222  -0.99062   8.5696
-3 -0.173978  -6.36037 -15.28433 25.9220  -0.90874   4.9226
-4 -0.228442   0.49431 -15.74204 16.6223  19.91038  -4.5945
-5 -0.083215  -1.10063 -10.71731 16.7346  -5.27024  -4.9222
-6 -0.068646 -31.04688  13.48390 35.5823 -32.92237 -30.5472
-7 -0.164017 -15.98384  17.43554  3.6729  -1.78311   6.0019
-
-Constants:
-       1        2        3        4        5        6        7 
--207.998  -90.447  -80.095 -107.543  -32.384 -278.597  -71.533 
                           
 Apparent error rate 0.0063
 
@@ -2771,182 +1942,6 @@
   Eritrean 0.143    0.857
 
 Data:  olitos
-Call:
-Linda(grp ~ ., data = olitos, method = method, l1med = TRUE)
-
-Prior Probabilities of Groups:
-       1        2        3        4 
-0.416667 0.208333 0.283333 0.091667 
-
-Group means:
-       X1     X2      X3        X4     X5      X6     X7     X8      X9     X10
-1 0.61861 61.922 0.13793 0.0083075 12.253 0.81044 2.3641 6.3750 0.73489 0.52449
-2 0.45284 61.703 0.15501 0.0087576 12.468 0.79224 2.1172 6.0876 0.62131 0.40745
-3 0.80600 61.881 0.18238 0.0113765 13.691 1.08170 2.1789 7.1500 0.62430 0.44256
-4 1.54967 61.540 0.16732 0.0128062 13.020 0.94846 2.1030 6.8258 0.81991 0.61946
-      X11    X12    X13     X14    X15    X16    X17    X18    X19    X20
-1 0.44590 240.69 3.3364 0.95830 89.569 95.571 1.9855 15.559 27.352 39.999
-2 0.35536 260.06 3.3465 0.86475 91.836 95.737 3.3679 10.638 23.373 47.437
-3 0.37779 247.63 3.0262 1.03154 89.433 95.836 3.2695 16.537 32.031 36.698
-4 0.53622 272.98 3.3075 1.55975 89.431 95.070 1.8511 12.665 25.461 42.890
-     X21    X22     X23     X24    X25
-1 16.988 66.652 0.33734 2.20245 82.572
-2 18.537 53.664 0.44841 0.71899 74.021
-3 14.715 54.244 0.21587 3.65115 82.480
-4 18.973 58.658 0.24420 8.36199 84.740
-
-Within-groups Covariance Matrix:
-             X1          X2          X3          X4          X5          X6
-X1   0.08518798 -1.1336e-02  1.8674e-03  2.2709e-04 -0.00508315  1.7836e-03
-X2  -0.01133553  2.9635e-02  1.8809e-04 -8.2283e-05 -0.01035519  1.4735e-03
-X3   0.00186743  1.8809e-04  6.4758e-04  3.3877e-05 -0.00239966  3.8739e-04
-X4   0.00022709 -8.2283e-05  3.3877e-05  1.0835e-05 -0.00073837  6.3682e-05
-X5  -0.00508315 -1.0355e-02 -2.3997e-03 -7.3837e-04  0.39704593  2.2137e-02
-X6   0.00178360  1.4735e-03  3.8739e-04  6.3682e-05  0.02213718  9.9768e-03
-X7   0.00645183  2.3590e-03 -3.4285e-04 -1.2447e-04  0.01296102 -5.2697e-04
-X8   0.00134749  1.0696e-02  3.7951e-03 -5.0283e-04  0.11513582 -6.6970e-03
-X9   0.00102085 -8.8293e-04 -2.8776e-04 -1.2836e-04  0.01691681 -2.4783e-03
-X10  0.00419224 -1.1052e-04 -2.4367e-04 -7.0647e-05  0.00124181 -5.6708e-04
-X11  0.00056695  2.9419e-04 -1.5733e-04 -6.9493e-05 -0.00646781 -1.8709e-03
-X12  0.98807884 -8.2295e-03  2.0612e-01  3.3653e-02 -7.57783847  1.4494e-01
-X13 -0.00751626 -6.5848e-04 -3.8564e-04 -1.7521e-04  0.02322166  2.0231e-03
-X14  0.04802392 -3.2287e-03  1.0404e-03  2.4042e-04  0.00788301  3.5487e-03
-X15  0.03109355 -2.4574e-02 -1.4418e-03 -5.8214e-04  0.10594069 -3.2826e-02
-X16 -0.03932736  2.9713e-03 -3.1606e-04 -1.8113e-04  0.00380470 -6.5231e-03
-X17  0.03376886 -6.5312e-03  7.0526e-03  4.8600e-04  0.05558135 -6.1967e-03
-X18  0.33887744  4.2358e-03  1.2588e-02  1.7861e-03 -0.72572130 -5.9309e-02
-X19  0.16316870 -5.3538e-02 -5.7031e-03 -2.4236e-03  0.81149714  5.8455e-02
-X20 -0.43482885  2.7970e-02  9.8134e-03  1.6396e-03 -0.22794773 -5.1371e-02
-X21 -0.04043629  1.4039e-02 -1.6035e-02 -8.4389e-04  0.10159764  4.5183e-02
-X22  0.20048477 -7.2205e-03 -4.7628e-02  1.8697e-03 -1.91088299  1.0407e-01
-X23  0.01046549 -2.3270e-02  6.7694e-04 -1.4813e-05  0.01958510  7.3121e-03
-X24 -0.00755487  7.9564e-02  8.4759e-03  3.4738e-04 -0.25109569 -1.9427e-02
-X25  0.23353447  5.5149e-02 -2.6966e-03  3.5776e-03 -0.86491352 -7.7184e-02
-             X7          X8          X9         X10         X11         X12
-X1   0.00645183  0.00134749  0.00102085  4.1922e-03  5.6695e-04   0.9880788
-X2   0.00235903  0.01069573 -0.00088293 -1.1052e-04  2.9419e-04  -0.0082295
-X3  -0.00034285  0.00379506 -0.00028776 -2.4367e-04 -1.5733e-04   0.2061199
-X4  -0.00012447 -0.00050283 -0.00012836 -7.0647e-05 -6.9493e-05   0.0336530
-X5   0.01296102  0.11513582  0.01691681  1.2418e-03 -6.4678e-03  -7.5778385
-X6  -0.00052697 -0.00669701 -0.00247827 -5.6708e-04 -1.8709e-03   0.1449407
-X7   0.01814906  0.00907264  0.00310316  4.0988e-03  2.4118e-03  -0.9209858
-X8   0.00907264  0.31131638  0.02161427  8.4322e-04 -4.1483e-03  -1.6502294
-X9   0.00310316  0.02161427  0.01625583  8.3585e-03  4.7558e-03  -0.7464878
-X10  0.00409877  0.00084322  0.00835851  1.1715e-02  5.8556e-03  -0.0691263
-X11  0.00241178 -0.00414831  0.00475585  5.8556e-03  9.6256e-03  -0.0041453
-X12 -0.92098578 -1.65022943 -0.74648785 -6.9126e-02 -4.1453e-03 570.5615105
-X13  0.00819946 -0.01310972 -0.00010858  6.4591e-04 -2.8970e-05  -2.3265554
-X14  0.00675925 -0.02415322  0.00359022  2.0706e-03 -3.1777e-03  -0.0669957
-X15  0.02340544  0.08230609  0.05234856  1.2394e-02  2.2244e-02  -3.6248446
-X16 -0.01023067  0.04623564  0.00693304  2.1221e-03  8.3413e-03   1.9642246
-X17  0.00406769  0.11928564  0.00248024 -5.4204e-03 -6.5957e-03   0.1671808
-X18  0.03486301 -0.03274811  0.03781091  3.1579e-02  2.1337e-02  19.8029175
-X19  0.09190512  0.28521726  0.04452677  4.7613e-02  6.8189e-03 -17.2705717
-X20 -0.11218479 -0.11319286 -0.00921619 -4.8350e-02  1.4504e-02   9.5705631
-X21 -0.00581759 -0.15457742 -0.06484867 -2.4268e-02 -3.2304e-02  -9.5229519
-X22 -0.07558756 -1.83978316 -0.22725626 -1.0883e-01 -1.9892e-02  11.7579738
-X23 -0.00234028 -0.01278442 -0.00399936 -9.5896e-04 -2.3341e-03  -2.0958393
-X24  0.02420392 -0.11698629  0.01408465  4.4690e-02  1.0745e-02  19.4775814
-X25  0.10175617  0.09937063  0.06430041  6.3542e-02 -7.3078e-02  23.7635443
-            X13         X14         X15         X16        X17        X18
-X1  -0.00751626  0.04802392  0.03109355 -0.03932736  0.0337689  0.3388774
-X2  -0.00065848 -0.00322869 -0.02457409  0.00297128 -0.0065312  0.0042358
-X3  -0.00038564  0.00104036 -0.00144177 -0.00031606  0.0070526  0.0125884
-X4  -0.00017521  0.00024042 -0.00058214 -0.00018113  0.0004860  0.0017861
-X5   0.02322166  0.00788301  0.10594069  0.00380470  0.0555813 -0.7257213
-X6   0.00202313  0.00354865 -0.03282645 -0.00652306 -0.0061967 -0.0593089
-X7   0.00819946  0.00675925  0.02340544 -0.01023067  0.0040677  0.0348630
-X8  -0.01310972 -0.02415322  0.08230609  0.04623564  0.1192856 -0.0327481
-X9  -0.00010858  0.00359022  0.05234856  0.00693304  0.0024802  0.0378109
-X10  0.00064591  0.00207058  0.01239412  0.00212213 -0.0054204  0.0315791
-X11 -0.00002897 -0.00317768  0.02224398  0.00834127 -0.0065957  0.0213373
-X12 -2.32655540 -0.06699571 -3.62484459  1.96422459  0.1671808 19.8029175
-X13  0.09314139 -0.01371355 -0.13887769 -0.09685275 -0.0251558 -0.2366341
-X14 -0.01371355  0.08569274  0.01283184 -0.05366265  0.0319259  0.2587917
-X15 -0.13887769  0.01283184  1.52108997  0.28311061  0.3573105  1.0490827
-X16 -0.09685275 -0.05366265  0.28311061  0.21816257  0.1218445  0.1481618
-X17 -0.02515576  0.03192593  0.35731050  0.12184448  0.9448063  0.5353998
-X18 -0.23663407  0.25879166  1.04908269  0.14816176  0.5353998  7.8439689
-X19  0.17569895 -0.02016795  0.30797337 -0.09386781  0.6301040  2.4692987
-X20 -0.12691476 -0.21229418 -0.39268477  0.35973245 -0.4995288 -6.4558908
-X21  0.15130569 -0.00481127 -0.72769612 -0.34443129 -0.5938679 -3.2309755
-X22  0.09813539  0.18387313 -1.63040599 -0.81637456 -4.2017148  2.6304812
-X23  0.04103544 -0.01460143 -0.00168594 -0.06091978 -0.0388835  0.0045153
-X24 -0.01378664 -0.04430127  0.25915406  0.03867848 -0.2721244  0.9671924
-X25 -1.04329960  0.61856140  0.45467403  0.66782286 -0.2145114  6.7597399
-            X19         X20         X21        X22         X23         X24
-X1    0.1631687  -0.4348288 -0.04043629  0.2004848  1.0465e-02 -0.00755487
-X2   -0.0535377   0.0279702  0.01403923 -0.0072205 -2.3270e-02  0.07956393
-X3   -0.0057031   0.0098134 -0.01603514 -0.0476278  6.7694e-04  0.00847590
-X4   -0.0024236   0.0016396 -0.00084389  0.0018697 -1.4813e-05  0.00034738
-X5    0.8114971  -0.2279477  0.10159764 -1.9108830  1.9585e-02 -0.25109569
-X6    0.0584550  -0.0513707  0.04518346  0.1040701  7.3121e-03 -0.01942691
-X7    0.0919051  -0.1121848 -0.00581759 -0.0755876 -2.3403e-03  0.02420392
-X8    0.2852173  -0.1131929 -0.15457742 -1.8397832 -1.2784e-02 -0.11698629
-X9    0.0445268  -0.0092162 -0.06484867 -0.2272563 -3.9994e-03  0.01408465
-X10   0.0476128  -0.0483499 -0.02426789 -0.1088311 -9.5896e-04  0.04469024
-X11   0.0068189   0.0145040 -0.03230423 -0.0198919 -2.3341e-03  0.01074503
-X12 -17.2705717   9.5705631 -9.52295189 11.7579738 -2.0958e+00 19.47758137
-X13   0.1756990  -0.1269148  0.15130569  0.0981354  4.1035e-02 -0.01378664
-X14  -0.0201679  -0.2122942 -0.00481127  0.1838731 -1.4601e-02 -0.04430127
-X15   0.3079734  -0.3926848 -0.72769612 -1.6304060 -1.6859e-03  0.25915406
-X16  -0.0938678   0.3597325 -0.34443129 -0.8163746 -6.0920e-02  0.03867848
-X17   0.6301040  -0.4995288 -0.59386792 -4.2017148 -3.8884e-02 -0.27212444
-X18   2.4692987  -6.4558908 -3.23097548  2.6304812  4.5153e-03  0.96719243
-X19  11.3335349 -10.6900683 -2.62969087 -7.1987020  5.8698e-01 -0.71619389
-X20 -10.6900683  17.1971342 -0.32452420 -3.1859349 -7.3744e-01  0.51118300
-X21  -2.6296909  -0.3245242  5.79856806  7.5319093  1.5327e-01 -0.62724807
-X22  -7.1987020  -3.1859349  7.53190932 70.4210395  6.9185e-01 -6.76366912
-X23   0.5869839  -0.7374410  0.15327429  0.6918465  1.8584e-01 -0.15067380
-X24  -0.7161939   0.5111830 -0.62724807 -6.7636691 -1.5067e-01  8.72169417
-X25  -9.1898756   3.5870282 -1.22416518  1.6412777 -1.4256e+00  2.62754282
-           X25
-X1   0.2335345
-X2   0.0551491
-X3  -0.0026966
-X4   0.0035776
-X5  -0.8649135
-X6  -0.0771842
-X7   0.1017562
-X8   0.0993706
-X9   0.0643004
-X10  0.0635419
-X11 -0.0730776
-X12 23.7635443
-X13 -1.0432996
-X14  0.6185614
-X15  0.4546740
-X16  0.6678229
-X17 -0.2145114
-X18  6.7597399
-X19 -9.1898756
-X20  3.5870282
-X21 -1.2241652
-X22  1.6412777
-X23 -1.4256286
-X24  2.6275428
-X25 50.8855054
-
-Linear Coeficients:
-      X1     X2      X3     X4      X5      X6      X7     X8      X9     X10
-1 1498.0 4402.6 -6826.0 127426 -258.07 -3473.5 -417.32 112.62 -327.36 -902.14
-2 1497.4 4399.3 -6801.1 126789 -260.43 -3466.3 -431.65 110.50 -337.72 -897.08
-3 1495.3 4393.9 -6783.0 127335 -252.66 -3443.6 -431.44 114.83 -341.34 -896.91
-4 1510.0 4393.0 -6876.3 127712 -260.25 -3459.7 -447.69 119.28 -333.59 -904.22
-      X11    X12    X13    X14    X15    X16     X17     X18    X19     X20
-1 -1141.5 12.148 5241.2 3739.2 18.044 4540.9 -623.42 -277.71 11.665 -78.251
-2 -1153.3 12.243 5244.3 3740.1 22.054 4536.6 -623.40 -277.80 12.194 -77.450
-3 -1129.8 12.071 5227.7 3728.4 17.679 4530.3 -621.44 -275.33 12.569 -77.085
-4 -1112.8 12.185 5252.8 3751.9 19.393 4544.4 -623.75 -279.31 12.583 -77.918
-      X21     X22    X23     X24    X25
-1 -36.679 -25.206 1414.3 -60.244 52.642
-2 -35.698 -25.592 1414.6 -60.949 52.684
-3 -35.707 -25.128 1408.8 -59.731 52.510
-4 -36.436 -25.103 1413.4 -59.126 52.986
-
-Constants:
-      1       2       3       4 
--359903 -359646 -358448 -359870 
                        
 Apparent error rate 0.1
 
@@ -2970,4 +1965,4 @@
 > 
 > proc.time()
    user  system elapsed 
-   4.39    0.78    5.38 
+   2.31    0.31    2.60 
diff -Nru r-cran-rrcov-1.5-2/tests/togk4.R r-cran-rrcov-1.5-5/tests/togk4.R
--- r-cran-rrcov-1.5-2/tests/togk4.R	2020-01-14 16:07:52.000000000 +0000
+++ r-cran-rrcov-1.5-5/tests/togk4.R	2020-07-28 14:07:37.000000000 +0000
@@ -61,15 +61,17 @@
     cat("Data Set               n   p  Half LOG(obj)        Time\n")
     cat("========================================================\n")
     domcd(heart[, 1:2], data(heart), nrep)
+## This will not work within the function, of course
+##  - comment it out
 ## IGNORE_RDIFF_BEGIN
-    domcd(starsCYG,data(starsCYG), nrep)
+##    domcd(starsCYG,data(starsCYG), nrep)
 ## IGNORE_RDIFF_END
     domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep)
     domcd(stack.x,data(stackloss), nrep)
     domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep)
     domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep)
 ## IGNORE_RDIFF_BEGIN
-    domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep)
+##    domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep)
 ## IGNORE_RDIFF_END
     domcd(data.matrix(subset(hbk,  select = -Y)), data(hbk), nrep)
 
diff -Nru r-cran-rrcov-1.5-2/tests/togk4.Rout.save r-cran-rrcov-1.5-5/tests/togk4.Rout.save
--- r-cran-rrcov-1.5-2/tests/togk4.Rout.save	2020-01-14 16:11:17.000000000 +0000
+++ r-cran-rrcov-1.5-5/tests/togk4.Rout.save	2020-07-28 14:21:36.000000000 +0000
@@ -1,5 +1,5 @@
 
-R Under development (unstable) (2020-01-08 r77640) -- "Unsuffered Consequences"
+R version 4.0.2 (2020-06-22) -- "Taking Off Again"
 Copyright (C) 2020 The R Foundation for Statistical Computing
 Platform: x86_64-w64-mingw32/x64 (64-bit)
 
@@ -78,15 +78,17 @@
 +     cat("Data Set               n   p  Half LOG(obj)        Time\n")
 +     cat("========================================================\n")
 +     domcd(heart[, 1:2], data(heart), nrep)
++ ## This will not work within the function, of course
++ ##  - comment it out
 + ## IGNORE_RDIFF_BEGIN
-+     domcd(starsCYG,data(starsCYG), nrep)
++ ##    domcd(starsCYG,data(starsCYG), nrep)
 + ## IGNORE_RDIFF_END
 +     domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep)
 +     domcd(stack.x,data(stackloss), nrep)
 +     domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep)
 +     domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep)
 + ## IGNORE_RDIFF_BEGIN
-+     domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep)
++ ##    domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep)
 + ## IGNORE_RDIFF_END
 +     domcd(data.matrix(subset(hbk,  select = -Y)), data(hbk), nrep)
 + 
@@ -126,24 +128,6 @@
 height  15.88   32.07 
 weight  32.07   78.28 
 --------------------------------------------------------
-starsCYG              47   2
-Outliers:  7 
-[1]  7  9 11 14 20 30 34
--------------
-
-Call:
-CovOgk(x = x)
--> Method:  Orthogonalized Gnanadesikan-Kettenring Estimator 
-
-Robust Estimate of Location: 
-   log.Te  log.light  
-    4.413      4.934  
-
-Robust Estimate of Covariance: 
-           log.Te   log.light
-log.Te     0.01122  0.03755  
-log.light  0.03755  0.23499  
---------------------------------------------------------
 phosphor              18   2
 Outliers:  2 
 [1] 1 6
@@ -221,26 +205,6 @@
 X2  -0.6365   3.0976  -1.3520
 X3  -0.4720  -1.3520   2.3648
 --------------------------------------------------------
-wood                  20   5
-Outliers:  0 
--------------
-
-Call:
-CovOgk(x = x)
--> Method:  Orthogonalized Gnanadesikan-Kettenring Estimator 
-
-Robust Estimate of Location: 
-    x1      x2      x3      x4      x5  
-0.5508  0.1330  0.5087  0.5112  0.9069  
-
-Robust Estimate of Covariance: 
-    x1          x2          x3          x4          x5        
-x1   0.0078774  -0.0002767   0.0034220   0.0025563  -0.0027250
-x2  -0.0002767   0.0004644  -0.0003344  -0.0007968   0.0005818
-x3   0.0034220  -0.0003344   0.0039759   0.0014998  -0.0016071
-x4   0.0025563  -0.0007968   0.0014998   0.0037489  -0.0007524
-x5  -0.0027250   0.0005818  -0.0016071  -0.0007524   0.0026191
---------------------------------------------------------
 hbk                   75   3
 Outliers:  14 
  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14
@@ -336,4 +300,4 @@
 > 
 > proc.time()
    user  system elapsed 
-   0.82    0.24    1.06 
+   0.40    0.20    0.59