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

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. Thi...

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Detalles Bibliográficos
Autores principales: Onogi, Akio, Arakawa, Aisaku
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9191213/
https://www.ncbi.nlm.nih.gov/pubmed/35575313
http://dx.doi.org/10.1093/bioinformatics/btac328
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author Onogi, Akio
Arakawa, Aisaku
author_facet Onogi, Akio
Arakawa, Aisaku
author_sort Onogi, Akio
collection PubMed
description 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.
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spelling pubmed-91912132022-06-14 An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference Onogi, Akio Arakawa, Aisaku Bioinformatics Applications Notes 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. Oxford University Press 2022-05-16 /pmc/articles/PMC9191213/ /pubmed/35575313 http://dx.doi.org/10.1093/bioinformatics/btac328 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Onogi, Akio
Arakawa, Aisaku
An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference
title An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference
title_full An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference
title_fullStr An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference
title_full_unstemmed An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference
title_short An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference
title_sort r package vigor for joint estimation of multiple linear learners with variational bayesian inference
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9191213/
https://www.ncbi.nlm.nih.gov/pubmed/35575313
http://dx.doi.org/10.1093/bioinformatics/btac328
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