PMA: Penalized Multivariate Analysis
Version 1.0.9

Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in the following papers: (1) Witten, Tibshirani and Hastie (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3):515-534. (2) Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data. Statistical Applications in Genetics and Molecular Biology 8(1): Article 28.

AuthorDaniela Witten and Rob Tibshirani and Sam Gross and Balasubramanian Narasimhan
Date of publication2013-03-25 08:26:26
MaintainerDaniela Witten <dwitten@u.washington.edu>
LicenseGPL (>= 2)
Version1.0.9
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("PMA")

Getting started

Package overview

Popular man pages

breastdata: Breast cancer gene expression + DNA copy number data set from...
MultiCCA: Perform sparse multiple canonical correlation analysis.
notusing/CCA: Perform sparse canonical correlation analysis using the...
PlotCGH: Plot CGH data
PMA-package: Penalized Multivariate Analysis
SPC: Perform sparse principal component analysis
SPC.cv: Perform cross-validation on sparse principal component...
See all...

All man pages Function index File listing

Man pages

breastdata: Breast cancer gene expression + DNA copy number data set from...
CCA: Perform sparse canonical correlation analysis using the...
CCA.permute: Select tuning parameters for sparse canonical correlation...
MultiCCA: Perform sparse multiple canonical correlation analysis.
MultiCCA.permute: Select tuning parameters for sparse multiple canonical...
notusing/CCA: Perform sparse canonical correlation analysis using the...
notusing/CCA.permute: Select tuning parameters for sparse canonical correlation...
PlotCGH: Plot CGH data
PMA-internal: Internal PMA functions
PMA-package: Penalized Multivariate Analysis
PMD: Get a penalized matrix decomposition for a data matrix.
PMD.cv: Do tuning parameter selection for PMD via cross-validation
SPC: Perform sparse principal component analysis
SPC.cv: Perform cross-validation on sparse principal component...

Functions

Files

MD5
R
R/CCA.xlfuns.R
R/zzz.R
R/SamrFunctions.R
R/PMD.R
R/PlotFusedLasso.R
R/CCA.R
R/FLSAGeneralInclude.R
R/MultiCCA.R
R/PMD.CGH.R
src
src/Groups.h
src/init.cc
src/FLSAGeneralMain.cc
src/Scheduler.cc
src/FLSA.h
src/GeneralFunctions.cc
src/PenaltyGraph.h
src/Groups.cc
src/PenaltyGraph.cc
src/FLSAGeneral.cc
src/MaxFlowGraph.cc
src/GraphDefinitions.h
src/FLSA.cc
src/FLSAGeneral.h
src/Scheduler.h
src/GeneralFunctions.h
src/MaxFlowGraph.h
data
data/breastdata.rda
data/datalist
NAMESPACE
DESCRIPTION
man
man/SPC.Rd
man/notusing
man/notusing/CCA.Rd
man/notusing/CCA.permute.Rd
man/MultiCCA.Rd
man/SPC.cv.Rd
man/PMA-package.Rd
man/CCA.Rd
man/PMD.Rd
man/breastdata.Rd
man/PlotCGH.Rd
man/PMA-internal.Rd
man/MultiCCA.permute.Rd
man/CCA.permute.Rd
man/PMD.cv.Rd
PMA documentation built on May 19, 2017, 9:06 p.m.

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