VariableSelection: Select Variables for Linear Models

2026 Dataset Open access

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Authors: Shuangshuang Xu
DOI: 10.32614/cran.package.variableselection
Cited by: 0
References: 1

Abstract

Provides variable selection for linear models and generalized linear models using Bayesian information criterion (BIC) and model posterior probability (MPP). Given a set of candidate predictors, it evaluates candidate models and returns model-level summaries (BIC and MPP) and predictor-level posterior inclusion probabilities (PIP). For more details see Xu, S., Ferreira, M. A., & Tegge, A. N. (2025) .

Fields of Study & Subjects

Statistical Methods and Inference Statistical Methods and Bayesian Inference Statistical Methods and Applications

Publication details

Publication date
17 February 2026
Language
EN
Document type
Dataset