The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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) <doi:10.48550/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: pmartR, scpoisson, scry, systemPipeTools
Reverse suggests: systemPipeShiny

Linking:

Please use the canonical form https://CRAN.R-project.org/package=glmpca to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.