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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: NEWS
CRAN checks: fastglmpca results


Reference manual: fastglmpca.pdf
Vignettes: Analysis of single-cell RNA-seq data using fastglmpca


Package source: fastglmpca_0.1-103.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): fastglmpca_0.1-103.tgz, r-release (arm64): fastglmpca_0.1-103.tgz, r-oldrel (arm64): fastglmpca_0.1-103.tgz, r-prerel (x86_64): fastglmpca_0.1-103.tgz, r-release (x86_64): fastglmpca_0.1-103.tgz


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