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

Last updated: May 13,2024

1. Install "r-cran-brms" package

Please follow the step by step instructions below to install r-cran-brms on Ubuntu 20.10 (Groovy Gorilla)

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

2. Uninstall "r-cran-brms" package

Please follow the step by step instructions below to uninstall r-cran-brms on Ubuntu 20.10 (Groovy Gorilla):

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

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

Package: r-cran-brms
Architecture: all
Version: 2.13.0-1ubuntu1
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: 6754
Depends: r-base-core (>= 4.0.1-1), 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 (>= 2.2.0), r-cran-matrix (>= 1.1.1), r-cran-mgcv (>= 1.8-13), r-cran-rstantools (>= 2.0.0), r-cran-bayesplot (>= 1.5.0), r-cran-shinystan (>= 2.4.0), 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
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_2.13.0-1ubuntu1_all.deb
Size: 5414700
MD5sum: 57272ecc6a42cfcf0501defb0528c5c7
SHA1: 86c8542270a3cc5c209bb8354c247572471d3a71
SHA256: dff6d6a41cb3b452f2765879ad311e6dbea61de11ad145217952e77f80313357
SHA512: 7ea1e0dce007aa098021331aac6581a4e45beec17cb89d4d2d13ae37552866fb18e4431f65e53c51f17f74d19235b82a7d71d1aa95976eb5a9b503eaaae72014
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