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 | 
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