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:10.1101/574574>. Townes FW (2019) <arXiv:1907.02647>.

Version: 0.1.0
Depends: R (≥ 3.6), stats
Suggests: knitr, MASS, testthat, covr, ggplot2
Published: 2019-09-27
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: Artistic-2.0
URL: https://github.com/willtownes/glmpca
NeedsCompilation: no
Materials: README NEWS
CRAN checks: glmpca results

Downloads:

Reference manual: glmpca.pdf
Vignettes: Title of your vignette
Package source: glmpca_0.1.0.tar.gz
Windows binaries: r-devel: glmpca_0.1.0.zip, r-devel-gcc8: glmpca_0.1.0.zip, r-release: glmpca_0.1.0.zip, r-oldrel: not available
OS X binaries: r-release: glmpca_0.1.0.tgz, r-oldrel: not available

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