How to Install and Uninstall r-cran-mice Package on Ubuntu 21.10 (Impish Indri)

Last updated: May 20,2024

1. Install "r-cran-mice" package

This guide covers the steps necessary to install r-cran-mice on Ubuntu 21.10 (Impish Indri)

$ sudo apt update $ sudo apt install r-cran-mice

2. Uninstall "r-cran-mice" package

This guide let you learn how to uninstall r-cran-mice on Ubuntu 21.10 (Impish Indri):

$ sudo apt remove r-cran-mice $ sudo apt autoclean && sudo apt autoremove

3. Information about the r-cran-mice package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-mice
Architecture: amd64
Version: 3.13.0-2
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1572
Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-generics, r-cran-lattice, r-cran-rcpp, r-cran-rlang, r-cran-tidyr, r-cran-cpp11, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2)
Recommends: r-cran-boot, r-cran-testthat, r-cran-hsaur3, r-cran-mass, r-cran-nloptr, r-cran-matrix, r-cran-rcppeigen, r-cran-minqa, r-cran-mitml, r-cran-haven, r-cran-jomo, r-cran-pan, r-cran-readr, r-cran-hms, r-cran-forcats, r-cran-ordinal, r-cran-nnet, r-cran-ucminf, r-cran-numderiv, r-cran-clipr, r-cran-lme4, r-cran-survival, r-cran-lmtest, r-cran-broom.mixed, r-cran-metafor
Suggests: r-cran-knitr, r-cran-randomforest, r-cran-rmarkdown, r-cran-rpart
Filename: pool/universe/r/r-cran-mice/r-cran-mice_3.13.0-2_amd64.deb
Size: 1326508
MD5sum: 70ea0309a96e80fec02b25fd0a2ea5b8
SHA1: ca39452ae878ab69ba0a6eda92354ca8cd277fdc
SHA256: 6e752f37ffd8cd95ac0f6375391382360561cd61e74baa732d319e7b58e886dc
SHA512: 574325d61183839b23c49b30433fc54a04c6052a5eb1a14f3635c67aa79f485735136109604529fba830b4a8288f8272b7492a04879f588085f4333ef652d624
Homepage: https://cran.r-project.org/package=mice
Description-en: 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) . 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.
Description-md5: e96f2e2829bbbdab64a562ce6d23139a