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r-bioc-multtest package

r-bioc-multtest 2.50.0-1 source package in Ubuntu

Changelog

r-bioc-multtest (2.50.0-1) unstable; urgency=medium

  * Team upload.
  * New upstream version 2.50.0

 -- Nilesh Patra <email address hidden>  Wed, 24 Nov 2021 21:08:17 +0530

Upload details

Uploaded by:
Debian R Packages Maintainers
Uploaded to:
Sid
Original maintainer:
Debian R Packages Maintainers
Architectures:
any
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Jammy release universe misc

Downloads

File Size SHA-256 Checksum
r-bioc-multtest_2.50.0-1.dsc 2.1 KiB 7e24a7abc59dc79ab459eabacad5e93ebcc2f6fbc7a19f6e3afd0f6260c640b0
r-bioc-multtest_2.50.0.orig.tar.gz 1.2 MiB 907a7312c2313fe33afe0d27508224fc0368d6c4b84fca09078282ab2e16b82b
r-bioc-multtest_2.50.0-1.debian.tar.xz 3.2 KiB dc6695d3d8d825c03d1321de23f51f2bb3812a03e70b0f980692c40b987a1d4f

Available diffs

No changes file available.

Binary packages built by this source

r-bioc-multtest: 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.

r-bioc-multtest-dbgsym: debug symbols for r-bioc-multtest