Package: nbfar 0.1

Aditya Mishra

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.

Authors:Aditya Mishra [aut, cre], Christian Mueller [aut]

nbfar_0.1.tar.gz
nbfar_0.1.tar.gz(r-4.5-noble)nbfar_0.1.tar.gz(r-4.4-noble)
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nbfar.pdf |nbfar.html✨
nbfar/json (API)

# Install 'nbfar' in R:
install.packages('nbfar', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/amishra-stats/nbfar/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

2.00 score 6 scripts 140 downloads 4 exports 47 dependencies

Last updated 3 years agofrom:92e078ea69. Checks:OK: 1 WARNING: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 24 2024
R-4.5-linux-x86_64WARNINGOct 24 2024

Exports:nbfarnbfar_controlnbfar_simnbrrr

Dependencies:bstclicodetoolscolorspacedoParallelfansifarverforeachgbmggplot2glmnetgluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmpathmunsellnlmenumDerivpillarpkgconfigpsclR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrlangrpartrrpackscalesshapesurvivaltibbleutf8vctrsviridisLiteWeightSVMwithr

Negative Binomial factor regression with application to microbiome data analysis

Aditya Mishra, Christian L. Müller

Rendered fromnbfar_vignette.Rmdusingknitr::rmarkdownon Oct 24 2024.

Last update: 2022-02-22
Started: 2022-02-22