Bayes Factors for Informative Hypotheses

Computes approximated adjusted fractional Bayes factors for equality, inequality, and about equality constrained hypotheses. For a tutorial on this method, see Hoijtink, Mulder, van Lissa, & Gu, (2019) . For applications in structural equation modeling, see: Van Lissa, Gu, Mulder, Rosseel, Van Zundert, & Hoijtink, (2021) . For the statistical underpinnings, see Gu, Mulder, and Hoijtink (2018) ; Hoijtink, Gu, & Mulder, J. (2019) ; Hoijtink, Gu, Mulder, & Rosseel, (2019) .

Graphing Nonlinear Relations Among Latent Variables from Structural Equation Mixture Models

Contains a graphical user interface to generate the diagnostic plots proposed by Bauer (2005; ), Pek & Chalmers (2015; ), and Pek, Chalmers, R. Kok, & Losardo (2015; ) to investigate nonlinear bivariate relationships in latent regression models using structural equation mixture models (SEMMs).

Latent Structure Learning

Fits structural equation modeling via penalized likelihood.

Power Analyses for SEM

Provides a-priori, post-hoc, and compromise power-analyses for structural equation models (SEM).

Easy Model-Builder Functions for 'OpenMx'

Utilities for building certain kinds of common matrices and models in the extended structural equation modeling package, 'OpenMx'.

'SEM Shiny'

Interactive 'shiny' application for working with Structural Equation Modelling technique. Runtime examples are provided in the package function as well as at < https://kartikeyab.shinyapps.io/semwebappk/> .

Average and Conditional Effects

Use structural equation modeling to estimate average and conditional effects of a treatment variable on an outcome variable, taking into account multiple continuous and categorical covariates.

SEM Sensitivity Analysis

Performs sensitivity analysis for Structural Equation Modeling (SEM). It determines which sample points need to be removed for the sign of a specific path in the SEM model to change, thus assessing the robustness of the model. Methodological manuscript in preparation.

Fit Measure Cutoffs in SEM

Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. Hu & Bentler, 1999 ) with the same parameters (population model, number of observations, etc.) as the model under consideration.

Ancestor Regression

Causal discovery in linear structural equation models (Schultheiss, and Bühlmann (2023) ) and vector autoregressive models (Schultheiss, Ulmer, and Bühlmann (2025) ) with explicit error control for false discovery, at least asymptotically.