r-cran-mice 3.8.0-2 source package in Ubuntu

Changelog

r-cran-mice (3.8.0-2) unstable; urgency=medium

  * Team Upload.
  * Update test depends
  * routine-update: Add salsa-ci.yml
  * Add "Rules-Requires-Root:no"
  * Add upstream/metadata

 -- Nilesh Patra <email address hidden>  Fri, 13 Mar 2020 19:00:59 +0530

Upload details

Uploaded by:
Debian R Packages Maintainers
Uploaded to:
Sid
Original maintainer:
Debian R Packages Maintainers
Architectures:
any
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section

Downloads

File Size SHA-256 Checksum
r-cran-mice_3.8.0-2.dsc 2.4 KiB afcb6420c2387694045a11246fb9f1e5f9f555bb44d57810474bb52c67bf8ac7
r-cran-mice_3.8.0.orig.tar.gz 541.7 KiB 04bc18d6cf225d626d4a5d52dd98a30a19662ae14263c83b51744efce25e7ec5
r-cran-mice_3.8.0-2.debian.tar.xz 3.3 KiB af56a2d9b03031e18ddc116fe72a3ec3d022ec2c91cd66a70d9433013c5f7154

Available diffs

No changes file available.

Binary packages built by this source

r-cran-mice: GNU R multivariate imputation by chained equations

 Multiple imputation using Fully Conditional Specification (FCS)
 implemented by the MICE algorithm as described in Van Buuren and
 Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has
 its own imputation model. Built-in imputation models are provided for
 continuous data (predictive mean matching, normal), binary data (logistic
 regression), unordered categorical data (polytomous logistic regression)
 and ordered categorical data (proportional odds). MICE can also impute
 continuous two-level data (normal model, pan, second-level variables).
 Passive imputation can be used to maintain consistency between variables.
 Various diagnostic plots are available to inspect the quality of the
 imputations.

r-cran-mice-dbgsym: debug symbols for r-cran-mice