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

Last updated: May 18,2024

1. Install "r-cran-brms" package

This is a short guide on how to install r-cran-brms on Ubuntu 21.10 (Impish Indri)

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

2. Uninstall "r-cran-brms" package

In this section, we are going to explain the necessary steps to uninstall r-cran-brms on Ubuntu 21.10 (Impish Indri):

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

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

Package: r-cran-brms
Architecture: all
Version: 2.14.4-1
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: 6911
Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-cran-rcpp (>= 0.12.0), r-cran-rstan (>= 2.19.2), r-cran-ggplot2 (>= 2.0.0), r-cran-loo (>= 2.3.1), r-cran-matrix (>= 1.1.1), r-cran-mgcv (>= 1.8-13), r-cran-rstantools (>= 2.1.1), r-cran-bayesplot (>= 1.5.0), r-cran-shinystan (>= 2.4.0), r-cran-projpred (>= 2.0.0), r-cran-bridgesampling (>= 0.3-0), r-cran-glue (>= 1.3.0), r-cran-matrixstats, r-cran-nleqslv, r-cran-nlme, r-cran-coda, r-cran-abind, r-cran-future, r-cran-backports
Recommends: r-cran-testthat (>= 0.9.1), r-cran-emmeans (>= 1.4.2), r-cran-mnormt, r-cran-spdep, r-cran-rwiener, r-cran-splines2, r-cran-rtdists
Suggests: r-cran-mice, r-cran-lme4, r-cran-ape, r-cran-arm, r-cran-statmod, r-cran-digest, r-cran-knitr, r-cran-rmarkdown
Filename: pool/universe/r/r-cran-brms/r-cran-brms_2.14.4-1_all.deb
Size: 5647092
MD5sum: a90fc3fe8fa1ca3a75bb6b9e79e4910b
SHA1: f080d2b7683d4ee23ad2e5444ebc1d5a731e4fcc
SHA256: 7059628302405851fb1bd46a2273ea004504cedb48dd4479ae31a9dd27e0c377
SHA512: ef838caee398e8f2b30c1feb0ac6eaceb23ff250b2590884dbd9375529b5d0e223c99fb06b23db0770c56081e97980eb326a0b19bc5837493dc29a2f3418f847
Homepage: https://cran.r-project.org/package=brms
Description-en: GNU R Bayesian regression models using 'Stan'
Fit Bayesian generalized (non-)linear multivariate multilevel models
using 'Stan' for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit -- among others
-- linear, robust linear, count data, survival, response times, ordinal,
zero-inflated, hurdle, and even self-defined mixture models all in a
multilevel context. Further modeling options include non-linear and
smooth terms, auto-correlation structures, censored data, meta-analytic
standard errors, and quite a few more. In addition, all parameters of
the response distribution can be predicted in order to perform
distributional regression. Prior specifications are flexible and
explicitly encourage users to apply prior distributions that actually
reflect their beliefs. Model fit can easily be assessed and compared
with posterior predictive checks and leave-one-out cross-validation.
Description-md5: f9d33571831e39eaf395113e94f37f38