by default, lavaan implements the textbook/paper formulas, so there are no surprises
lavaan can mimic many results of several commercial packages (including Mplus
and Eqs using the mimic="Mplus" or mimic="EQS"
arguments)
lavaan is not a black box: you can browse the source code on GitHub
lavaan can be extended: see the Related Projects page for extensions and add-ons.
the ‘lavaan model syntax’ allows users to express their models in a compact, elegant and useR-friendly way; for example, a typical CFA analysis looks as follows:
library(lavaan)
myData <- read.csv("/path/to/mydata/myData.csv")
myModel <- '
f1 =~ item1 + item2 + item3
f2 =~ item4 + item5 + item6
f3 =~ item7 + item8 + item9
'
fit <- cfa(model = myModel, data = myData)
summary(fit, fit.measures = TRUE)
you can choose between a user-friendly interface in combination with the
fitting functions cfa() and sem() or a low-level interface using
the fitting function lavaan() where ‘defaults’ do not get in the way
convenient arguments (eg. group.equal="loadings") simplify many common tasks
(eg. measurement invariance testing)
lavaan outputs all the information you need: a large number of fit measures, modification indices, standardized solutions, and technical information that is stored in a fitted lavaan object
full support for meanstructures and multiple groups
several estimators are available: ML (and robust variants MLM, MLMV, MLR), GLS, WLS (and robust variants DWLS, WLSM, WLSMV), ULS (ULSM, ULMV), DLS, and pairwise ML (PML)
standard errors: standard, robust/huber-white/sandwich, bootstrap
test statistics: standard, Satorra-Bentler, Yuan-Bentler, Satterthwaite, scaled-shifted, Bollen-Stine bootstrap
missing data: FIML estimation
linear and nonlinear equality and inequality constraints
full support for analyzing categorical data: lavaan (from version 0.5 onwards) can handle any mixture of binary, ordinal and continuous observed variables
(from version 0.6 onwards): support for multilevel level SEM