nbfar: Negative Binomial Factor Regression Models ('nbfar')

We developed a negative binomial factor regression model to estimate structured (sparse) associations between a feature matrix X and overdispersed count data Y. With 'nbfar', microbiome count data Y can be used, for example, to associate host or environmental covariates with microbial abundances. Currently, two models are available: a) Negative Binomial reduced rank regression (NB-RRR), b) Negative Binomial co-sparse factor regression (NB-FAR). Please refer the manuscript 'Mishra, A. K., & Müller, C. L. (2021). Negative Binomial factor regression with application to microbiome data analysis. bioRxiv.' for more details.

Version:	0.1
Depends:	R (≥ 3.5.0), stats, utils
Imports:	Rcpp (≥ 0.12.9), MASS, magrittr, rrpack, glmnet, RcppParallel, mpath
LinkingTo:	Rcpp, RcppArmadillo, RcppParallel
Suggests:	rmarkdown, knitr, spelling
Published:	2022-02-22
Author:	Aditya Mishra [aut, cre], Christian Mueller [aut]
Maintainer:	Aditya Mishra <amishra at flatironinstitute.org>
License:	GPL (≥ 3.0)
URL:	https://github.com/amishra-stats/nbfar, https://www.biorxiv.org/content/10.1101/2021.11.29.470304v1
NeedsCompilation:	yes
Language:	en-US
CRAN checks:	nbfar results

Documentation:

Reference manual:	nbfar.pdf
Vignettes:	Negative Binomial factor regression with application to microbiome data analysis

Downloads:

Package source:	nbfar_0.1.tar.gz
Windows binaries:	r-devel: nbfar_0.1.zip, r-release: nbfar_0.1.zip, r-oldrel: nbfar_0.1.zip
macOS binaries:	r-release (arm64): nbfar_0.1.tgz, r-oldrel (arm64): nbfar_0.1.tgz, r-release (x86_64): nbfar_0.1.tgz, r-oldrel (x86_64): nbfar_0.1.tgz

Linking:

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