Package: multtest
Title: Resampling-based multiple hypothesis testing
Version: 1.8.3
Author: Katherine S. Pollard, Yongchao Ge, Sandrine Dudoit
Description: Non-parametric bootstrap and permutation resampling-based multiple testing procedures 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). Single-step and step-wise methods are implemented. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models) are included. Results are reported in terms of adjusted p-values, confindence regions and test statistic cutoffs.
Maintainer: Katherine S. Pollard <kpollard@wald.ucdavis.edu>
Depends: R (>= 2.1.0), survival, methods
License: LGPL
Packaged: Sat Jan  6 11:59:58 2007; kpollard

Package: multtest
Title: Resampling-based multiple hypothesis testing
Version: 2.0.0
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 <kpollard@gladstone.ucsf.edu>
Depends: R (>= 2.1.0), methods, Biobase
Imports: survival, MASS
Enhances: snow, Rmpi, rpvm
License: LGPL
biocViews: Microarray, DifferentialExpression, MultipleComparisons
LazyLoad: yes
Packaged: 2009-04-21 08:58:33 UTC; biocbuild
Repository: CRAN
Date/Publication: 2009-05-10 18:54:50