VsusP1.0.0 package

Variable Selection using Shrinkage Priors

numNoiseCoeff

Variable selection using shrinkage priors :: numNoiseCoeff

OptimalHbi

Variable selection using shrinkage priors :: OptimalHbi

S2MVarSelection

Variable selection using shrinkage priors :: S2MVarSelection

S2MVarSelectionV1

Variable selection using shrinkage priors :: S2MVarSelectionV1

Sequential2Means

Variable selection using shrinkage priors :: Sequential2Means

Sequential2MeansBeta

Variable selection using shrinkage prior :: Sequential2MeansBeta

Download source packageRead PDF manual

Bayesian variable selection using shrinkage priors to identify significant variables in high-dimensional datasets. The package includes methods for determining the number of significant variables through innovative clustering techniques of posterior distributions, specifically utilizing the 2-Means and Sequential 2-Means (S2M) approaches. The package aims to simplify the variable selection process with minimal tuning required in statistical analysis.

  • Maintainer: Nilson Chapagain
  • License: GPL (>= 3)
  • Last published: 2024-06-25

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