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

Last updated: May 21,2024

1. Install "r-cran-loo" package

Please follow the step by step instructions below to install r-cran-loo on Ubuntu 21.10 (Impish Indri)

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

2. Uninstall "r-cran-loo" package

Please follow the instructions below to uninstall r-cran-loo on Ubuntu 21.10 (Impish Indri):

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

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

Package: r-cran-loo
Architecture: all
Version: 2.4.1-1
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: 3657
Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-cran-checkmate, r-cran-matrixstats (>= 0.52)
Recommends: r-cran-testthat (>= 2.1.0)
Suggests: r-cran-bayesplot (>= 1.7.0), r-cran-brms (>= 2.10.0), r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-rstan, r-cran-rstanarm (>= 2.19.0), r-cran-rstantools, r-cran-spdep
Filename: pool/universe/r/r-cran-loo/r-cran-loo_2.4.1-1_all.deb
Size: 2787384
MD5sum: 60ee82f5596e8881b943a8d2b0c34f3e
SHA1: 19f9cd9530e2cdf5fb9ea87bf1fb05f80d799abe
SHA256: 78472c78dcb66d34afcd41435d77783ecce4e80cce65229812357bc3ed717bb3
SHA512: 3d910f6ceb40a176984d209fab6ab2f301aeefafe7c4a60d1dc427a493643091f6e3bc41f0953a8c88ecf2993f74bdd02dadab23a8f73faddf8f927f030e76ad
Homepage: https://cran.r-project.org/package=loo
Description-en: GNU R leave-one-out cross-validation and WAIC for Bayesian models
Efficient approximate leave-one-out cross-validation (LOO) for Bayesian
models fit using Markov chain Monte Carlo. The approximation uses Pareto
smoothed importance sampling (PSIS), a new procedure for regularizing
importance weights. As a byproduct of the calculations, it is possible
as well to obtain approximate standard errors for estimated predictive
errors and for the comparison of predictive errors between models. The
package also provides methods for using stacking and other model
weighting techniques to average Bayesian predictive distributions.
Description-md5: 541ebc4ccd5628907cfdc754e577ac33