File: control

package info (click to toggle)
r-bioc-multtest 2.62.0-2
  • links: PTS, VCS
  • area: main
  • in suites: forky, sid, trixie
  • size: 3,368 kB
  • sloc: ansic: 2,181; makefile: 5
file content (45 lines) | stat: -rw-r--r-- 2,078 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
Source: r-bioc-multtest
Section: gnu-r
Priority: optional
Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Uploaders: Andreas Tille <tille@debian.org>
Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-bioc-multtest
Vcs-Git: https://salsa.debian.org/r-pkg-team/r-bioc-multtest.git
Homepage: https://bioconductor.org/packages/multtest/
Standards-Version: 4.7.0
Rules-Requires-Root: no
Build-Depends: debhelper-compat (= 13),
               dh-r,
               r-base-dev,
               r-bioc-biocgenerics,
               r-bioc-biobase,
               r-cran-survival,
               r-cran-mass,
               architecture-is-64-bit
Testsuite: autopkgtest-pkg-r

Package: r-bioc-multtest
Architecture: any
Depends: ${R:Depends},
         ${shlibs:Depends},
         ${misc:Depends}
Recommends: ${R:Recommends}
Suggests: ${R:Suggests}
Description: 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.