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An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference.

ABSTRACT:

Summary

An R package that can implement multiple linear learners, including penalized regression and regression with spike and slab priors, in a single model has been developed. Solutions are obtained with fast minorize-maximization algorithms in the framework of variational Bayesian inference. This package helps to incorporate multimodal and high-dimensional explanatory variables in a single regression model.

Availability and implementation

The R package VIGoR (Variational Bayesian Inference for Genome-wide Regression) is available at the Comprehensive R Archive Network (CRAN) (https://cran.r-project.org/) and at GitHub (https://github.com/Onogi/VIGoR).

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Onogi A

PROVIDER: S-EPMC9191213 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference.

Onogi Akio A Arakawa Aisaku A

Bioinformatics (Oxford, England) 20220601 12

<h4>Summary</h4>An R package that can implement multiple linear learners, including penalized regression and regression with spike and slab priors, in a single model has been developed. Solutions are obtained with fast minorize-maximization algorithms in the framework of variational Bayesian inference. This package helps to incorporate multimodal and high-dimensional explanatory variables in a single regression model.<h4>Availability and implementation</h4>The R package VIGoR (Variational Bayesi ...[more]

PMID: 35575313

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