Influential Cases in Structural Equation Modeling

Sensitivity analysis in structural equation modeling using influence measures and diagnostic plots. Support leave-one-out casewise sensitivity analysis presented by Pek and MacCallum (2011) and approximate casewise influence using scores and casewise likelihood.

Recursive Partitioning for Structural Equation Models

SEM Trees and SEM Forests -- an extension of model-based decision trees and forests to Structural Equation Models (SEM). SEM trees hierarchically split empirical data into homogeneous groups each sharing similar data patterns with respect to a SEM by recursively selecting optimal predictors of these differences. SEM forests are an extension of SEM trees. They are ensembles of SEM trees each built on a random sample of the original data. By aggregating over a forest, we obtain measures of variable importance that are more robust than measures from single trees. A description of the method was published by Brandmaier, von Oertzen, McArdle, & Lindenberger (2013) and Arnold, Voelkle, & Brandmaier (2020) .

Exploratory Structural Equation Modeling ESEM

A collection of functions developed to support the tutorial on using Exploratory Structural Equiation Modeling (ESEM) (Asparouhov & Muthén, 2009) < https://www.statmodel.com/download/EFACFA810.pdf>) with Longitudinal Study of Australian Children (LSAC) dataset (Mohal et al., 2023) . The package uses 'tidyverse','psych', 'lavaan','semPlot' and provides additional functions to conduct ESEM. The package provides general functions to complete ESEM, including esem_c(), creation of target matrix (if it is used) make_target(), generation of the Confirmatory Factor Analysis (CFA) model syntax esem_cfa_syntax(). A sample data is provided - the package includes a sample data of the Strengths and Difficulties Questionnaire of the Longitudinal Study of Australian Children (SDQ LSAC) in sdq_lsac(). 'ESEM' package vignette presents the tutorial demonstrating the use of ESEM on SDQ LSAC data.

Customizing Structural Equation Modelling Plots

Most function focus on specific ways to customize a graph. They use a 'qgraph' output as the first argument, and return a modified 'qgraph' object. This allows the functions to be chained by a pipe operator.

Structural Equation Modeling for the Social Relations Model

Provides functionality for structural equation modeling for the social relations model (Kenny & La Voie, 1984; ; Warner, Kenny, & Soto, 1979, ). Maximum likelihood estimation (Gill & Swartz, 2001, ; Nestler, 2018, ) and least squares estimation is supported (Bond & Malloy, 2018, ).

Structural Equation Modeling and Confirmatory Network Analysis

Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data . Allows for confirmatory testing and fit as well as exploratory model search.

Non-Smooth Regularization for Structural Equation Models

Provides regularized structural equation modeling (regularized SEM) with non-smooth penalty functions (e.g., lasso) building on 'lavaan'. The package is heavily inspired by the ['regsem'](< https://github.com/Rjacobucci/regsem>) and ['lslx'](< https://github.com/psyphh/lslx>) packages.

Stable Specification Search in Structural Equation Models

An exploratory and heuristic approach for specification search in Structural Equation Modeling. The basic idea is to subsample the original data and then search for optimal models on each subset. Optimality is defined through two objectives: model fit and parsimony. As these objectives are conflicting, we apply a multi-objective optimization methods, specifically NSGA-II, to obtain optimal models for the whole range of model complexities. From these optimal models, we consider only the relevant model specifications (structures), i.e., those that are both stable (occur frequently) and parsimonious and use those to infer a causal model.

Complex Survey Structural Equation Modeling (SEM)

Fit structural equation models (SEM) including factor analysis, multivariate regression models with latent variables and many other latent variable models while correcting estimates, standard errors, and chi-square-derived fit measures for a complex sampling design. Incorporate clustering, stratification, sampling weights, and finite population corrections into a SEM analysis. Wrapper around packages lavaan and survey.

Interactively Visualize Structural Equation Modeling Diagrams

It is an R package and web-based application, allowing users to perform interactive and reproducible visualizations of path diagrams for structural equation modeling (SEM) and networks using the 'ggplot2' engine. Its app (built with 'shiny') provides an interface that allows extensive customization, and creates CSV outputs, which can then be used to recreate the figures either using the web app or script-based workflow.