# bioconductor-multtest¶

## Summary¶

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.

• 2.26.0

LGPL

## Meta¶

package:
name: bioconductor-multtest
version: 2.26.0
source:
fn: multtest_2.26.0.tar.gz
url: http://bioconductor.org/packages/release/bioc/src/contrib/multtest_2.26.0.tar.gz
md5: 77592f4a4f61628d00bb356da06b6179
build:
number: 0
rpaths:
- lib/R/lib/
- lib/
requirements:
build:
- bioconductor-biobase
- bioconductor-biocgenerics
- 'r >=2.10'
run:
- bioconductor-biobase
- bioconductor-biocgenerics
- 'r >=2.10'
test:
commands:
- '\$R -e "library(''multtest'')"'
home: http://bioconductor.org/packages/release/bioc/html/multtest.html
summary: '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.'