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

Akio Onogi1, Aisaku Arakawa2

  • 1Department of Plant Life Science, Faculty of Agriculture, Ryukoku University, Otsu, Shiga 520-2194, Japan.

Bioinformatics (Oxford, England)
|May 16, 2022
PubMed
Summary
This summary is machine-generated.

A new R package, VIGoR, integrates multiple linear regression models, including penalized regression and spike and slab priors, for complex datasets. It uses variational Bayesian inference for efficient solutions with high-dimensional data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • High-dimensional data and multimodal explanatory variables present challenges for traditional regression models.
  • Integrating diverse linear learners into a single framework is computationally intensive.

Purpose of the Study:

  • To develop an R package for implementing multiple linear learners within a unified model.
  • To facilitate the incorporation of multimodal and high-dimensional variables in regression analysis.

Main Methods:

  • Utilizes variational Bayesian inference for model fitting.
  • Employs fast minorize-maximization algorithms for efficient computation.
  • Implements penalized regression and regression with spike and slab priors.

Main Results:

  • The VIGoR package provides a unified approach to complex regression modeling.
  • Efficient solutions are obtained even with high-dimensional and multimodal data.
  • The package integrates various linear regression techniques.

Conclusions:

  • VIGoR offers a powerful tool for advanced regression analysis in R.
  • The package simplifies the analysis of complex datasets with numerous predictors.
  • It enables more robust and flexible modeling through integrated Bayesian methods.