fastglmpca: Fast Algorithms for Generalized Principal Component Analysis
Implements fast, scalable optimization algorithms for
fitting generalized principal components analysis (GLM-PCA) models,
as described in "A Generalization of Principal Components
Analysis to the Exponential Family" Collins M, Dasgupta S, Schapire RE
(2002, ISBN:9780262271738), and subsequently "Feature Selection
and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial
Model" Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019)
<doi:10.1186/s13059-019-1861-6>.
| Version: |
0.1-103 |
| Depends: |
R (≥ 3.6) |
| Imports: |
utils, Matrix, MatrixExtra, stats, distr, daarem, Rcpp (≥
1.0.8), RcppParallel (≥ 5.1.5) |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppParallel |
| Suggests: |
testthat, knitr, rmarkdown, ggplot2, cowplot |
| Published: |
2024-01-31 |
| Author: |
Eric Weine [aut, cre],
Peter Carbonetto [aut],
Matthew Stephens [aut] |
| Maintainer: |
Eric Weine <ericweine15 at gmail.com> |
| BugReports: |
https://github.com/stephenslab/fastglmpca/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/stephenslab/fastglmpca |
| NeedsCompilation: |
yes |
| SystemRequirements: |
GNU make |
| Materials: |
NEWS |
| CRAN checks: |
fastglmpca results |
Documentation:
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