slfm: Fitting a Bayesian Sparse Latent Factor Model in Gene Expression Analysis

Set of tools to find coherent patterns in gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM) <doi:10.1007/978-3-319-12454-4_15>. Considerable effort has been put to build a fast and memory efficient package, which makes this proposal an interesting and computationally convenient alternative to study patterns of gene expressions exhibited in matrices. The package contains the implementation of two versions of the model based on different mixture priors for the loadings: one relies on a degenerate component at zero and the other uses a small variance normal distribution for the spike part of the mixture.

Version: 1.0.1
Depends: R (≥ 3.1.0)
Imports: Rcpp (≥ 0.11.0), coda, lattice
LinkingTo: Rcpp, RcppArmadillo
Published: 2020-03-15
Author: Vinicius Mayrink [aut, cre], Joao Duarte [aut]
Maintainer: Vinicius Mayrink <vdinizm at gmail.com>
License: GPL-2
NeedsCompilation: yes
Citation: slfm citation info
Materials: NEWS
CRAN checks: slfm results

Downloads:

Reference manual: slfm.pdf
Package source: slfm_1.0.1.tar.gz
Windows binaries: r-devel: slfm_1.0.1.zip, r-devel-gcc8: slfm_1.0.1.zip, r-release: slfm_1.0.1.zip, r-oldrel: slfm_1.0.1.zip
OS X binaries: r-release: slfm_1.0.1.tgz, r-oldrel: slfm_1.0.1.tgz
Old sources: slfm archive

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