- show just the names of the installed packages (ordered)
sort(row.names(installed.packages()))
[1] "abind" "acepack" "AER" "akima"
[5] "anchors" "ape" "base" "bdsmatrix"
[9] "biglm" "Biobase" "BiocInstaller" "bitops"
[13] "boot" "car" "CarbonEL" "caTools"
[17] "chron" "class" "cluster" "coda"
[21] "coda" "codetools" "coin" "colorspace"
[25] "compiler" "CompQuadForm" "cubature" "DAAG"
[29] "datasets" "DBI" "Deducer" "DeducerExtras"
[33] "degreenet" "Design" "digest" "diptest"
[37] "doMC" "doSNOW" "dynlm" "e1071"
[41] "Ecdat" "effects" "ellipse" "ergm"
[45] "fBasics" "fCalendar" "fEcofin" "flexmix"
...
- the command installed.packages() provides much more information:
colnames(installed.packages())
[1] "Package" "LibPath" "Version" "Priority" "Depends" "Imports"
[7] "LinkingTo" "Suggests" "Enhances" "OS_type" "License" "Built"
- so if you want to know the package and its version
installed.packages()[,c("Package","Version")] # you can also use the col numbers c(1,3)
Package Version
Biobase "Biobase" "2.14.0"
BiocInstaller "BiocInstaller" "1.2.1"
GenABEL "GenABEL" "1.6-9"
multtest "multtest" "2.10.0"
abind "abind" "1.3-0"
acepack "acepack" "1.3-3.0"
AER "AER" "1.1-7"
akima "akima" "0.5-4"
anchors "anchors" "3.0-7"
ape "ape" "2.7-1"
bdsmatrix "bdsmatrix" "1.0"
...
- show information about a package
packageDescription("multtest")
Package: multtest
Title: Resampling-based multiple hypothesis testing
Version: 2.10.0
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 (>= 2.9.0), methods, Biobase
Imports: survival, MASS
Suggests: snow
License: LGPL
biocViews: Microarray, DifferentialExpression, MultipleComparisons
LazyLoad: yes
Packaged: 2011-11-01 04:28:05 UTC; biocbuild
Built: R 2.14.0; i686-pc-linux-gnu; 2011-11-11 10:32:24 UTC; unix
-- File: /home/mandy/R/i686-pc-linux-gnu-library/2.14/multtest/Meta/package.rds
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