Software Packages For Bayesian Multilevel Modeling

Keywords

Bayesian computer software; MCMC; multilevel modeling; R packages

Abstract

Multilevel modeling is a statistical approach to analyze hierarchical data that consist of individual observations nested within clusters. Bayesian method is a well-known, sometimes better, alternative of Maximum likelihood method for fitting multilevel models. Lack of user friendly and computationally efficient software packages or programs was a main obstacle in applying Bayesian multilevel modeling. In recent years, the development of software packages for multilevel modeling with improved Bayesian algorithms and faster speed has been growing. This article aims to update the knowledge of software packages for Bayesian multilevel modeling and therefore to promote the use of these packages. Three categories of software packages capable of Bayesian multilevel modeling including brms, MCMCglmm, glmmBUGS, Bambi, R2BayesX, BayesReg, R2MLwiN and others are introduced and compared in terms of computational efficiency, modeling capability and flexibility, as well as user-friendliness. Recommendations to practical users and suggestions for future development are also discussed.

Publication Date

7-4-2018

Publication Title

Structural Equation Modeling

Volume

25

Issue

4

Number of Pages

650-658

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/10705511.2018.1431545

Socpus ID

85045639092 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85045639092

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