一个用于使用变分贝叶斯推断联合估计多个线性学习者的 R 包 VIGoR。
An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference.
机构信息
Department of Plant Life Science, Faculty of Agriculture, Ryukoku University, Otsu, Shiga 520-2194, Japan.
Division of Animal Breeding and Reproduction Research, Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-0901, Japan.
出版信息
Bioinformatics. 2022 Jun 13;38(12):3306-3309. doi: 10.1093/bioinformatics/btac328.
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.
摘要
开发了一个 R 包，可以在单个模型中实现多种线性学习者，包括惩罚回归和带有尖峰和板条先验的回归。在变分贝叶斯推断的框架中，通过快速的最小最大化算法来获得解。该包有助于在单个回归模型中纳入多峰和高维解释变量。
可用性和实现
R 包 VIGoR（全基因组回归的变分贝叶斯推断）可在 Comprehensive R Archive Network（CRAN）（https://cran.r-project.org/）和 GitHub（https://github.com/Onogi/VIGoR）上获得。
补充信息
补充数据可在生物信息学在线获得。
Zotero 插件