r-cran-amelia 1.7.3-1 source package in Ubuntu

Changelog

r-cran-amelia (1.7.3-1) unstable; urgency=medium


  * New upstream release.

 -- Chris Lawrence <email address hidden>  Sun, 07 Dec 2014 22:49:52 -0500

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Uploaded by:
Chris Lawrence
Uploaded to:
Sid
Original maintainer:
Chris Lawrence
Architectures:
any
Section:
gnu-r
Urgency:
Medium Urgency

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Series Pocket Published Component Section

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File Size SHA-256 Checksum
r-cran-amelia_1.7.3-1.dsc 1.8 KiB 6f6b4273b3c06064fac14d9579ae4e0d962384828425da11b7302b4a86cc21f2
r-cran-amelia_1.7.3.orig.tar.gz 1.2 MiB 614512741c88a5468f02c2dd7a0915eec2e8db99cc1ee4fa24533649de9b4f1d
r-cran-amelia_1.7.3-1.debian.tar.xz 4.7 KiB caf3db3bb842f665ec6e363ca83d1dcda57fbc423afa202f0a59eedce6e39007

Available diffs

No changes file available.

Binary packages built by this source

r-cran-amelia: GNU R package supporting multiple imputation of missing data

 Amelia II "multiply imputes" missing data in a single cross-section
 (such as a survey), from a time series (like variables collected for
 each year in a country), or from a time-series-cross-sectional data
 set (such as collected by years for each of several
 countries). Amelia II implements our bootstrapping-based algorithm
 that gives essentially the same answers as the standard IP or EMis
 approaches, is usually considerably faster than existing approaches
 and can handle many more variables.
 .
 The program also generalizes existing approaches by allowing for
 trends in time series across observations within a cross-sectional
 unit, as well as priors that allow experts to incorporate beliefs
 they have about the values of missing cells in their data. Amelia II
 also includes useful diagnostics of the fit of multiple imputation
 models. The program works from the R command line or via a graphical
 user interface that does not require users to know R.

r-cran-amelia-dbgsym: debug symbols for package r-cran-amelia

 Amelia II "multiply imputes" missing data in a single cross-section
 (such as a survey), from a time series (like variables collected for
 each year in a country), or from a time-series-cross-sectional data
 set (such as collected by years for each of several
 countries). Amelia II implements our bootstrapping-based algorithm
 that gives essentially the same answers as the standard IP or EMis
 approaches, is usually considerably faster than existing approaches
 and can handle many more variables.
 .
 The program also generalizes existing approaches by allowing for
 trends in time series across observations within a cross-sectional
 unit, as well as priors that allow experts to incorporate beliefs
 they have about the values of missing cells in their data. Amelia II
 also includes useful diagnostics of the fit of multiple imputation
 models. The program works from the R command line or via a graphical
 user interface that does not require users to know R.