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

Downloads:

Reference manual: glmpca.pdf
Vignettes: Applying GLM-PCA to Data
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: glmpca_0.2.0.tgz, r-oldrel: glmpca_0.2.0.tgz
Old sources: glmpca archive

Reverse dependencies:

Reverse imports: scry
Reverse suggests: systemPipeShiny

Linking:

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