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

Last updated: May 29,2024

1. Install "r-cran-rstan" package

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

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

2. Uninstall "r-cran-rstan" package

This tutorial shows how to uninstall r-cran-rstan on Ubuntu 20.10 (Groovy Gorilla):

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

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

Package: r-cran-rstan
Architecture: amd64
Version: 2.19.3-1build1
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: 6566
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-stanheaders (>= 2.18.1), r-cran-ggplot2 (>= 2.0.0), r-cran-inline, r-cran-gridextra (>= 2.0.0), r-cran-rcpp (>= 0.12.0), r-cran-loo (>= 2.0.0), r-cran-pkgbuild, r-cran-rcppeigen (>= 0.3.3.3.0), r-cran-bh (>= 1.66.0), libc6 (>= 2.14), libgcc-s1 (>= 4.0), libstdc++6 (>= 9)
Recommends: r-cran-runit
Suggests: r-cran-kernsmooth, r-cran-shinystan (>= 2.3.0), r-cran-bayesplot (>= 1.5.0), r-cran-rmarkdown, r-cran-rstantools, r-cran-rstudioapi, r-cran-matrix, r-cran-knitr (>= 1.15.1)
Filename: pool/universe/r/r-cran-rstan/r-cran-rstan_2.19.3-1build1_amd64.deb
Size: 2082796
MD5sum: 57842718237af79e90904ebddf620d8d
SHA1: 35601811433142a086b87d7b1475a606047ea73f
SHA256: 7b3a2b83ac15f9cf92535e92138e2abf4c28d65104582cb17d207e7d9d95784c
SHA512: c0fc589f06cd49c6af1d0283fd7ec3ce865b569d3358a86292b9a9601963b86ae72632370a0ec11a966123cf64acc328f2d6cabfc77b8ab250fc37448e075ac1
Homepage: https://cran.r-project.org/package=rstan
Description-en: GNU R interface to Stan
User-facing R functions are provided to parse, compile, test, estimate,
and analyze Stan models by accessing the header-only Stan library
provided by the 'StanHeaders' package. The Stan project develops a
probabilistic programming language that implements full Bayesian
statistical inference via Markov Chain Monte Carlo, rough Bayesian
inference via 'variational' approximation, and (optionally penalized)
maximum likelihood estimation via optimization. In all three cases,
automatic differentiation is used to quickly and accurately evaluate
gradients without burdening the user with the need to derive the partial
derivatives.
Description-md5: a2598b9c408db224f70af4acf31c66e2