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

Last updated: May 13,2024

1. Install "r-cran-mets" package

Please follow the guidance below to install r-cran-mets on Ubuntu 20.10 (Groovy Gorilla)

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

2. Uninstall "r-cran-mets" package

In this section, we are going to explain the necessary steps to uninstall r-cran-mets on Ubuntu 20.10 (Groovy Gorilla):

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

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

Package: r-cran-mets
Architecture: amd64
Version: 1.2.7.1-1build1
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: 5973
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-timereg (>= 1.9.4), r-cran-lava (>= 1.6.6), r-cran-mvtnorm, r-cran-numderiv, r-cran-rcpp, r-cran-survival (>= 2.43-1), r-cran-rcpparmadillo, libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 9)
Recommends: r-cran-testthat (>= 0.11), r-cran-prodlim
Suggests: r-cran-ucminf
Filename: pool/universe/r/r-cran-mets/r-cran-mets_1.2.7.1-1build1_amd64.deb
Size: 4291272
MD5sum: 4cfefc74bb06ffb49cceecea286c75f6
SHA1: c9c2dbe782afa88ef603503ac2fe81297a57bcf7
SHA256: 5a7346674b6d61de864345645b59adf8d865db63167a19f419f66cb3bd1482ac
SHA512: 6e1615b53ff315879d68c926a9a32c861307f300eee7a82b8fbb05641d9efbeb50c16b94baf66022d79a154f8b09453890fe34cc2393417ade6b73ca45d63b19
Homepage: https://cran.r-project.org/package=mets
Description-en: GNU R analysis of multivariate event times
Implementation of various statistical models for multivariate
event history data . Including multivariate
cumulative incidence models , and bivariate random
effects probit models (Liability models) .
Also contains two-stage binomial modelling that can do pairwise odds-ratio
dependence modelling based marginal logistic regression models. This is an
alternative to the alternating logistic regression approach (ALR).
Description-md5: 3897a54c9237ed9b5a0148788f0cfb9d