Package: semTests 1.0.0

Jonas Moss

semTests: Robust Test Statistics for Structural Equation Models

Computes robust p-values for overall fit and nested comparisons of structural equation models fitted with 'lavaan'. Implements penalized eigenvalue block averaging and penalized regression (Foldnes, Moss, Grønneberg, 2025) <doi:10.1080/10705511.2024.2372028>, including their extension to nested models (Foldnes, Grønneberg, Moss, 2026) <doi:10.3758/s13428-026-02968-4>, alongside familiar corrections such as Satorra-Bentler. Supported settings include complete-data ML, GLS, and ULS, categorical DWLS and ULS, and full-information maximum likelihood with one or several groups.

Authors:Jonas Moss [aut, cre], Njål Foldnes [ctb], Steffen Grønneberg [ctb]

semTests_1.0.0.tar.gz
semTests_1.0.0.zip(r-4.7-any)semTests_1.0.0.zip(r-4.6-any)semTests_1.0.0.zip(r-4.5-any)
semTests_1.0.0.tgz(r-4.6-any)semTests_1.0.0.tgz(r-4.5-any)
semTests_1.0.0.tar.gz(r-4.7-any)semTests_1.0.0.tar.gz(r-4.6-any)
semTests_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
semTests/json (API)

# Install 'semTests' in R:
install.packages('semTests', repos = c('https://jonasmoss.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jonasmoss/semtests/issues

On CRAN:

Conda:

5.55 score 39 scripts 197 downloads 2 exports 6 dependencies

Last updated from:e54d90b27c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK162
source / vignettesOK195
linux-releaseOK155
macos-releaseOK171
macos-oldrelOK199
windows-develOK110
windows-releaseOK112
windows-oldrelOK107
wasm-releaseOK107

Exports:pvaluespvalues_nested

Dependencies:lavaanMASSmnormtnumDerivpbivnormquadprog

FIML models and information conventions
Scope | A reproducible MAR-like example | Keep the two information choices separate | Why observed information is the default | Comparing observed and expected model information | Nested FIML comparison | FIML with several groups | What to report | References
Rendered fromfiml-missing-data.Rmdusingknitr::rmarkdown

Last update: 2026-07-19
Started: 2026-07-17

Categorical and mixed-indicator models
Scope | Overall goodness of fit | Estimator families | Mixed indicators | Pairwise missingness | Nested categorical models | Practical checks | References
Rendered fromcategorical-data.Rmdusingknitr::rmarkdown

Last update: 2026-07-19
Started: 2026-07-17

Latent growth models with missing data
Scope | A reproducible missing-data mechanism | Linear growth under FIML | Is the growth linear?
Rendered fromlatent-growth.Rmdusingknitr::rmarkdown

Last update: 2026-07-19
Started: 2026-07-18

Measurement invariance with ordinal indicators
Scope | The configural model | Thresholds first | Then the loadings | References
Rendered frommeasurement-invariance.Rmdusingknitr::rmarkdown

Last update: 2026-07-19
Started: 2026-07-18

Robust test statistics with semTests
Introduction | Choose a focused guide | Basic usage: goodness of fit | The test-name grammar | Nested model comparison | More data families | Ordered and mixed indicators | References
Rendered fromsemTests.Rmdusingknitr::rmarkdown

Last update: 2026-07-19
Started: 2026-06-09

Complete continuous-data models
Scope | Overall goodness of fit | Reading the result | Nested comparison | GLS and ULS | Practical checks | References
Rendered fromcontinuous-data.Rmdusingknitr::rmarkdown

Last update: 2026-07-19
Started: 2026-07-17