(18 Dec 2017): a tutorial on 'The Pairwise Likelihood Method for Structural Equation Modelling with ordinal variables and data with missing values using the R package lavaan' prepared by Myrsini Katsikatsou has been added to the (new) tutorial page of the resources section.

(16 July 2017): a recording of my keynote presentation `Structural Equation Modeling: models, software and stories' given at the useR!2017 Conference is available here.

(25 February 2017): version 0.5-23.1097 has been released on CRAN. See Version History for more information.

Jena, 24 July 2018: half-day pre-conference (EAM) workshop on ''understanding SEM: where do all the numbers come from?'' (more info later)

Jena, 24 July 2018: half-day pre-conference (EAM) workshop on ''Multilevel SEM'' (more info later)

Gent, 9-10 April 2018: Introduction to Structural Equation Modeling (Yves Rosseel)

Gent, 11-12 April 2018: Multilevel Structural Equation Modeling (Yves Rosseel)

Giessen, 20-21 March 2018: R-Lavaan: Confirmatory Factor Analysis (CFA) and Extensions (Monika Heinzel-Gutenbrunner and Peter Schmidt)

The lavaan package is developed to provide useRs, researchers and teachers a free open-source, but commercial-quality package for latent variable modeling. You can use lavaan to estimate a large variety of multivariate statistical models, including path analysis, confirmatory factor analysis, structural equation modeling and growth curve models.

The official reference to the lavaan package is the following paper:

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling.

Journal of Statistical Software, 48(2), 1-36. URL http://www.jstatsoft.org/v48/i02/

To get a first impression of how lavaan works in practice, consider the following example of a SEM model. The figure below contains a graphical representation of the model that we want to fit.

```
model <- '
# latent variables
ind60 =~ x1 + x2 + x3
dem60 =~ y1 + y2 + y3 + y4
dem65 =~ y5 + y6 + y7 + y8
# regressions
dem60 ~ ind60
dem65 ~ ind60 + dem60
# residual covariances
y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8
'
fit <- sem(model,
data=PoliticalDemocracy)
summary(fit)
```