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 |
DOI: |
10.32614/CRAN.package.glmpca |
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:
Downloads:
Reverse dependencies:
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