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Title: Resampling-based multiple hypothesis testing
Version: 2.34.0
Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra
Taylor, Sandrine Dudoit
Description: 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.
Maintainer: Katherine S. Pollard <katherine.pollard@gladstone.ucsf.edu>
Depends: R (>= 2.10), methods, BiocGenerics, Biobase
Imports: survival, MASS, stats4
Suggests: snow
License: LGPL
LazyLoad: yes
biocViews: Microarray, DifferentialExpression, MultipleComparison
NeedsCompilation: yes
Packaged: 2017-10-30 22:37:03 UTC; biocbuild
Built: R 3.4.2; x86_64-pc-linux-gnu; 'Wed, 08 Nov 2017 10:19:32 +0100'; unix
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