How to Install and Uninstall r-cran-rms Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 17,2024

1. Install "r-cran-rms" package

Here is a brief guide to show you how to install r-cran-rms on Ubuntu 20.10 (Groovy Gorilla)

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

2. Uninstall "r-cran-rms" package

This guide let you learn how to uninstall r-cran-rms on Ubuntu 20.10 (Groovy Gorilla):

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

3. Information about the r-cran-rms package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-cran-rms
Architecture: amd64
Version: 6.0-1-1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Dirk Eddelbuettel
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 2385
Depends: libc6 (>= 2.29), r-base-core (>= 4.0.2-1), r-api-4.0, r-cran-hmisc (>= 4.3-0), r-cran-survival (>= 3.1-12), r-cran-lattice, r-cran-ggplot2 (>= 2.2), r-cran-sparsem, r-cran-quantreg, r-cran-rpart, r-cran-nlme (>= 3.1-123), r-cran-polspline, r-cran-multcomp, r-cran-htmltable (>= 1.11.0), r-cran-htmltools, r-cran-mass, r-cran-cluster, r-cran-digest, r-cran-foreign, r-cran-nnet
Conflicts: r-noncran-design
Replaces: r-noncran-design
Filename: pool/universe/r/r-cran-rms/r-cran-rms_6.0-1-1_amd64.deb
Size: 2079996
MD5sum: b327f8a397a23c202127b2e26681b8a2
SHA1: 49d1e82db7311b050e9ab3c2263a1a9b225b9458
SHA256: 94496776da3a3880ff22670943bfdbb1600f9408dd7315a65a81b0542002dbc4
SHA512: bcdf559512915463f162544d48d78dab06573798cc00901e35c4ebe8d13f135e202699e7a4c69686cb2902c9b9e9bbe24cfea0917d7d172ff9dcbb7a5ff235fa
Homepage: https://cran.r-project.org/package=rms
Description-en: GNU R regression modeling strategies by Frank Harrell
Regression modeling, testing, estimation, validation, graphics,
prediction, and typesetting by storing enhanced model design
attributes in the fit. rms is a collection of 229 functions that
assist with and streamline modeling. It also contains functions for
binary and ordinal logistic regression models and the Buckley-James
multiple regression model for right-censored responses, and implements
penalized maximum likelihood estimation for logistic and ordinary
linear models. rms works with almost any regression model, but it
was especially written to work with binary or ordinal logistic
regression, Cox regression, accelerated failure time models,
ordinary linear models, the Buckley-James model, generalized least
squares for serially or spatially correlated observations, generalized
linear models, and quantile regression.
.
See Frank Harrell (2001), Regression Modeling Strategies, Springer
Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/Rrms.
Description-md5: 9fe79ccc22f1a3025abc6da6b5e51bde