blavaan: Bayesian structural equation models via parameter expansion

By Edgar C. Merkle, Yves Rosseel. 2015

317 citations recorded by the source corpus.

This article describes blavaan, an R package for estimating Bayesian structural equation models (SEMs) via JAGS and for summarizing the results. It also describes a novel parameter expansion approach for estimating specific types of models with residual covariances, which facilitates estimation of these models in JAGS. The methodology and software are intended to provide users with a general means of estimating Bayesian SEMs, both classical and novel, in a straightforward fashion. Users can estimate Bayesian versions of classical SEMs with lavaan syntax, they can obtain state-of-the-art Bayesian fit measures associated with the models, and they can export JAGS code to modify the SEMs as desired. These features and more are illustrated by example, and the parameter expansion approach is explained in detail.

Subfields: Experimental and Cognitive Psychology, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1ef9d441b0df554d730bd6a92047eeb9a2caf92c

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In Experimental and Cognitive Psychology

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Small Sample Size Solutions (2020, methodology): “The column BayesDefault shows the blavaan default priors (Merkle & Rosseel, 2018).”
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery (2021, background): “Given a particular network structure, there are several approaches to learning DAG edge weights, even in the presence of latent variables [33].”
Bayesian Item Response Modeling in R with brms and Stan (2019, methodology): “Further, IRT models may also be specified in general purpose multilevel or structural equation modeling packages such as lme4 (Bates, Mächler, Bolker, and Walker 2015b), lavaan (Rosseel 2012), blavaan (Merkle and Rosseel 2018), or MCMCglmm (Hadfield 2010).”
bridgesampling: An R Package for Estimating Normalizing Constants (2017, unknown): “…such as the MCMCglmm package (Hadﬁeld 2010), the JAGS interface of the mgcv package (Wood 2016), the glmmBUGS package (Brown and Zhou 2018), or the blavaan 21 package (Merkle and Rosseel 2018) so that users could conduct Bayesian model comparisons in a black box way similar to the Stan interface.”