How to Install and Uninstall r-bioc-multtest Package on Ubuntu 21.10 (Impish Indri)

Last updated: May 18,2024

1. Install "r-bioc-multtest" package

Please follow the instructions below to install r-bioc-multtest on Ubuntu 21.10 (Impish Indri)

$ sudo apt update $ sudo apt install r-bioc-multtest

2. Uninstall "r-bioc-multtest" package

This tutorial shows how to uninstall r-bioc-multtest on Ubuntu 21.10 (Impish Indri):

$ sudo apt remove r-bioc-multtest $ sudo apt autoclean && sudo apt autoremove

3. Information about the r-bioc-multtest package on Ubuntu 21.10 (Impish Indri)

Package: r-bioc-multtest
Architecture: amd64
Version: 2.46.0-1
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: 1061
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, r-api-bioc-3.12, r-bioc-biocgenerics, r-bioc-biobase, r-cran-survival, r-cran-mass, libc6 (>= 2.29)
Suggests: r-cran-snow
Filename: pool/universe/r/r-bioc-multtest/r-bioc-multtest_2.46.0-1_amd64.deb
Size: 841848
MD5sum: f02ad7b98f04bd2d81b1ee764cec95f2
SHA1: 00a65172403674a5f7b0bc54a6a49e74bef36f9e
SHA256: 171ba994859f47e7979ca26ee147a859b38a78ee8f71dfa6f06ee42ae8685fa3
SHA512: 0efa2ca7c50ce909d1597bf6c06d09a7b6b005a27f6f24d805eba2234124697e1b74bb13242ff9faed9efed850139964a96c875fadc745526988932c6d9fc509
Homepage: https://bioconductor.org/packages/multtest/
Description-en: Bioconductor resampling-based multiple hypothesis testing
Non-parametric bootstrap and permutation resampling-based multiple
testing procedures (including empirical Bayes methods) for controlling
the family-wise error rate (FWER), generalized family-wise error rate
(gFWER), tail probability of the proportion of false positives (TPPFP),
and false discovery rate (FDR). Several choices of bootstrap-based null
distribution are implemented (centered, centered and scaled,
quantile-transformed). Single-step and step-wise methods are available.
Tests based on a variety of t- and F-statistics (including t-statistics
based on regression parameters from linear and survival models as well
as those based on correlation parameters) are included. When probing
hypotheses with t-statistics, users may also select a potentially faster
null distribution which is multivariate normal with mean zero and
variance covariance matrix derived from the vector influence function.
Results are reported in terms of adjusted p-values, confidence regions
and test statistic cutoffs. The procedures are directly applicable to
identifying differentially expressed genes in DNA microarray
experiments.
Description-md5: c4112391aa6882e8925f94048452c84f