- Aug 3, 2020: blavaan 0.3-10 is released on CRAN.

- Jul 18, 2019: blavaan was included in a symposium on the lavaan ecosystem at the 2019 International Meeting of the Psychometric Society.

- Jun 10, 2018: The blavaan paper is published in Journal of Statistical Software!

- Nov 17, 2017: Rens van de Schoot has developed and posted some useful introductory blavaan materials. See here.

The blavaan package is intended to provide researchers with an open, flexible, accessible set of tools for estimating Bayesian structural equation models. It uses model specification and helper functionality from R package lavaan, MCMC samplers from JAGS, and MCMC tools (including parallelization) from R package runjags.

The official reference to the blavaan package, which includes specific details on the package and the underlying modeling procedure, is:

- Merkle, E. C. & Rosseel, Y. (2018). blavaan: Bayesian structural equation models via parameter expansion. Journal of Statistical Software, 85(4), 1-30.

As shown below, model estimation in blavaan is nearly identical to model estimation in lavaan. Prior distributions are set by default, and these priors can be modified as desired (see the examples for more detail).

```
model <- '
# latent variable definitions
ind60 =~ x1 + x2 + x3
dem60 =~ y1 + a*y2 + b*y3 + c*y4
dem65 =~ y5 + a*y6 + b*y7 + c*y8
# regressions
dem60 ~ ind60
dem65 ~ ind60 + dem60
# residual correlations
y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8
'
fit <- bsem(model, data=PoliticalDemocracy)
summary(fit)
```

blavaan is partially supported by National Science Foundation (NSF) grants SES-1061334 and 1460719. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Website created using R Markdown. Content was strongly influenced by the lavaan website.