semTests computes robust p-values for
structural equation models fitted with lavaan: tests of
overall fit, nested model comparisons, and score and
criterion-difference tests of parameter restrictions. It implements
penalized eigenvalue block averaging and penalized regression (Foldnes, Moss, and
Grønneberg, 2025) and their extension to nested models (Foldnes, Grønneberg,
and Moss, 2026). It supports ML, GLS, ULS, categorical DWLS and ULS,
and FIML, with one or several groups.
install.packages("semTests")
# Development version:
# remotes::install_github("JonasMoss/semTests")library("semTests")
model <- "visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9"
fit <- lavaan::cfa(model, lavaan::HolzingerSwineford1939)
pvalues(fit)
#> peba4_rls
#> 5.165712e-07
#> estimator: ML | data: continuous | information: expected | df: 24To test a restriction, fit the restricted model and pass it first:
restricted <- "visual =~ x1 + a*x2 + a*x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9"
fit0 <- lavaan::cfa(restricted, lavaan::HolzingerSwineford1939)
pvalues_nested(fit0, fit)
#> pall_ug_ml
#> 0.06061142
#> estimator: ML | data: continuous | information: expected | df: 1 | nested (method 2000, A.method delta)
semtest(fit0, fit)
#> score test: Common criterion pseudo-target satisfies the restricted model
#> type statistic df rank reference p_value
#> score 2.770785 1 1 weighted_chisq 0.06131629
#> hypothesis
#> Common criterion pseudo-target satisfies the restricted modelvignette("semTests", package = "semTests"): getting
startedvignette("categorical-data", package = "semTests"):
ordinal indicators and measurement invariancevignette("fiml-missing-data", package = "semTests"):
missing data with FIMLvignette("score-criterion", package = "semTests"):
score and criterion tests?semTests-support lists every supported combination, and
citation("semTests") shows how to cite the package. Bugs
and ideas go to GitHub
issues.