slfm: Tools for Fitting Sparse Latent Factor Model

Set of tools to find coherent patterns in microarray data using a Bayesian Sparse Latent Factor Model - SLFM; see Duarte and Mayrink (2015) <doi:10.1007/978-3-319-12454-4_15>. Considerable effort has been put into making slfm fast and memory efficient, turning it an interesting alternative to simpler methods in terms of execution time. It implements versions of the SLFM based on two type of mixtures priors for the loadings: one relying on a degenerate component at zero and the other using a small variance normal distribution for the spike part of the mixture. It also implements additional functions to allow pre-processing procedures for the data and to fit the model for a large number of probesets or genes.

Version: 0.2.3
Depends: R (≥ 3.1.0)
Imports: Rcpp (≥ 0.11.0), coda, lattice
LinkingTo: Rcpp, RcppArmadillo
Published: 2019-05-27
Author: Joao Duarte and Vinicius Mayrink
Maintainer: Joao Duarte <jdanielnd at gmail.com>
BugReports: https://github.com/jdanielnd/slfm/issues
License: GPL-2
URL: https://github.com/jdanielnd/slfm
NeedsCompilation: yes
Materials: NEWS
CRAN checks: slfm results

Downloads:

Reference manual: slfm.pdf
Package source: slfm_0.2.3.tar.gz
Windows binaries: r-devel: slfm_0.2.3.zip, r-release: slfm_0.2.3.zip, r-oldrel: slfm_0.2.3.zip
OS X binaries: r-release: slfm_0.2.3.tgz, r-oldrel: slfm_0.2.3.tgz
Old sources: slfm archive

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