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) | fileLICENSE |
| URL: | https://github.com/willtownes/glmpca |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README,NEWS |
| CRAN checks: | glmpca results |
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