Dynamic Structural Equation Models

Applies dynamic structural equation models to time-series data with generic and simplified specification for simultaneous and lagged effects. Methods are described in Thorson et al. (2024) "Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms."

Regularized Structural Equation Modeling

Uses both ridge and lasso penalties (and extensions) to penalize specific parameters in structural equation models. The package offers additional cost functions, cross validation, and other extensions beyond traditional structural equation models. Also contains a function to perform exploratory mediation (XMed).

Dyadic Structural Equation Modeling

Scripting of structural equation models via 'lavaan' for Dyadic Data Analysis, and helper functions for supplemental calculations, tabling, and model visualization.

Network Structural Equation Modeling

Several methods have been developed to integrate structural equation modeling techniques with network data analysis to examine the relationship between network and non-network data. Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the 'lavaan' package, ensuring ease of use for researchers. Technical details and examples can be found at < https://bigsem.psychstat.org>.

SIMulated Structural Equation Modeling

Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.

Phylogenetic Structural Equation Model

Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation using structural equation models (SEM), extending methods from Thorson et al. (2023) . This implementation includes a minimal set of features, to allow users to easily read all of the documentation and source code. PCM using SEM includes phylogenetic linear models and structural equation models as nested submodels, but also allows imputation of missing values. Features and comparison with other packages are described in Thorson and van der Bijl (2023) .

Structural Equation Modeling Tables

For confirmatory factor analysis ('CFA') and structural equation models ('SEM') estimated with the 'lavaan' package, this package provides functions to create model summary tables and model comparison tables for hypothesis testing. Tables can be produced in 'LaTeX', 'HTML', or comma separated variables ('CSV').

Tidy Structural Equation Modeling

A tidy workflow for generating, estimating, reporting, and plotting structural equation models using 'lavaan', 'OpenMx', or 'Mplus'. Throughout this workflow, elements of syntax, results, and graphs are represented as 'tidy' data, making them easy to customize. Includes functionality to estimate latent class analyses, and to plot 'dagitty' and 'igraph' objects.

Extended Structural Equation Modelling

Create structural equation models that can be manipulated programmatically. Models may be specified with matrices or paths (LISREL or RAM) Example models include confirmatory factor, multiple group, mixture distribution, categorical threshold, modern test theory, differential Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares. equations, state space, and many others. Support and advanced package binaries available at < https://openmx.ssri.psu.edu>. The software is described in Neale, Hunter, Pritikin, Zahery, Brick, Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) .

Network Structural Equation Modeling

The network structural equation modeling conducts a network statistical analysis on a data frame of coincident observations of multiple continuous variables [1]. It builds a pathway model by exploring a pool of domain knowledge guided candidate statistical relationships between each of the variable pairs, selecting the 'best fit' on the basis of a specific criteria such as adjusted r-squared value. This material is based upon work supported by the U.S. National Science Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, under the NSF Solicitation: NSF 20-570 Industry-University Cooperative Research Centers Program [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) .