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

Last updated: May 14,2024

1. Install "r-cran-mcmcpack" package

This guide let you learn how to install r-cran-mcmcpack on Ubuntu 21.10 (Impish Indri)

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

2. Uninstall "r-cran-mcmcpack" package

This guide let you learn how to uninstall r-cran-mcmcpack on Ubuntu 21.10 (Impish Indri):

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

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

Package: r-cran-mcmcpack
Architecture: amd64
Version: 1.5-0-1
Priority: optional
Section: universe/math
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 3583
Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-cran-coda (>= 0.11-3), r-cran-mass, r-cran-lattice, r-cran-mcmc, r-cran-quantreg, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 9)
Filename: pool/universe/r/r-cran-mcmcpack/r-cran-mcmcpack_1.5-0-1_amd64.deb
Size: 1718708
MD5sum: 9b61e638ff73d21b5cc1696e21fdb10c
SHA1: 302f66b221f01af619f45f2465dc5538bee8e2c8
SHA256: 346d605ee3e9acbc30d6750247406ba909e851768790518f56ce0c1eb85ff28d
SHA512: 15ee94570c66ed35f1f13e359b3c101a59b2281600bcbb9452a9d1c8be87fc1c77dc08a3af70d262c000a8c5b07dc2378c9770dce5edbd2b13289a1c033b7fdb
Homepage: https://cran.r-project.org/package=MCMCpack
Description-en: R routines for Markov chain Monte Carlo model estimation
This is a set of routines for GNU R that implement various
statistical and econometric models using Markov chain Monte Carlo
(MCMC) estimation, which allows "solving" models that would otherwise
be intractable with traditional techniques, particularly problems in
Bayesian statistics (where one or more "priors" are used as part of
the estimation procedure, instead of an assumption of ignorance about
the "true" point estimates), although MCMC can also be used to solve
frequentist statistical problems with uninformative priors. MCMC
techniques are also preferable over direct estimation in the presence
of missing data.
.
Currently implemented are a number of ecological inference (EI)
routines (for estimating individual-level attributes or behavior from
aggregate data, such as electoral returns or census results), as well
as models for traditional linear panel and cross-sectional data, some
visualization routines for EI diagnostics, two item-response theory
(or ideal-point estimation) models, metric, ordinal, and
mixed-response factor analysis, and models for Gaussian (linear) and
Poisson regression, logistic regression (or logit), and binary and
ordinal-response probit models.
.
The suggested packages (r-cran-bayesm, -eco, and -mnp) contain
additional models that may also be useful for those interested in
this package.
Description-md5: e61e7c97144ccf110c561d9a0afdc130