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ERIC ED609283: Review of Software Packages for Bayesian Multilevel ... |
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by ERIC |
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Multilevel modeling is a statistical approach to analyze |
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hierarchical data, which consist of individual |
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observations nested within clusters. Bayesian methods is |
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a well-known, sometimes better, alternative of Maximum |
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likelihood methods for fitting multilevel models. Lack of |
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user-friendly and computationally efficient software |
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packages or programs was a main obstacle in applying |
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Bayesian multilevel modeling. In recent years, the |
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development of software packages for multilevel modeling |
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with improved Bayesian algorithms and faster speed has |
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been growing. This article aims to update the knowledge |
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of available software packages for Bayesian multilevel |
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modeling and therefore to promote the use of these |
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packages. Three categories of software packages capable |
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of Bayesian multilevel modeling including brms, MCMCglmm, |
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glmmBUGS, Bambi, R2BayesX, BayesReg, R2MLwiN and others |
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are introduced and compared in terms of computational |
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efficiency, modeling capability and flexibility, as well |
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as user-friendliness. Recommendations to practical users |
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and suggestions for future development are also |
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discussed. [This paper was published in "Structural |
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Equation Modeling" v25 n4 p650-658 2018.] |
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Date Published: 2022-07-15 01:33:22 |
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Identifier: ERIC_ED609283 |
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Item Size: 12733198 |
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Language: english |
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Media Type: texts |
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# Topics |
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ERIC Archive; ERIC; Mai, Yujiao\nZhan... |
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# Uploaded by |
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@chris85 |
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