glmpca: Dimension Reduction of Non-Normally Distributed Data

Implements a generalized version of principal components analysis (GLM-PCA) for dimension reduction of non-normally distributed data such as counts or binary matrices. Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1186/s13059-019-1861-6>. Townes FW (2019) <arXiv:1907.02647>.

Version:	0.2.0
Depends:	R (≥ 3.5)
Imports:	MASS, methods, stats, utils
Suggests:	covr, ggplot2, knitr, logisticPCA, markdown, Matrix, testthat
Published:	2020-07-18
Author:	F. William Townes [aut, cre, cph], Kelly Street [aut], Jake Yeung [ctb]
Maintainer:	F. William Townes <will.townes at gmail.com>
BugReports:	https://github.com/willtownes/glmpca/issues
License:	LGPL (≥ 3) | file LICENSE
URL:	https://github.com/willtownes/glmpca
NeedsCompilation:	no
Language:	en-US
Materials:	README NEWS
CRAN checks:	glmpca results

Documentation:

Reference manual:	glmpca.pdf
Vignettes:	Applying GLM-PCA to Data

Downloads:

Package source:	glmpca_0.2.0.tar.gz
Windows binaries:	r-devel: glmpca_0.2.0.zip, r-release: glmpca_0.2.0.zip, r-oldrel: glmpca_0.2.0.zip
macOS binaries:	r-release (arm64): glmpca_0.2.0.tgz, r-oldrel (arm64): glmpca_0.2.0.tgz, r-release (x86_64): glmpca_0.2.0.tgz, r-oldrel (x86_64): glmpca_0.2.0.tgz
Old sources:	glmpca archive

Reverse dependencies:

Reverse imports:	scpoisson, scry, systemPipeTools
Reverse suggests:	systemPipeShiny

Linking:

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