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').

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. Current models supported include Dyadic Confirmatory Factor Analysis, the Actor–Partner Interdependence Model (observed and latent), the Common Fate Model (observed and latent), Mutual Influence Model (latent), and the Bifactor Dyadic Model (latent).

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) .

Building and Estimating Structural Equation Models

A powerful, easy to syntax for specifying and estimating complex Structural Equation Models. Models can be estimated using Partial Least Squares Path Modeling or Covariance-Based Structural Equation Modeling or covariance based Confirmatory Factor Analysis. Methods described in Ray, Danks, and Valdez (2021).

Case Influence in Structural Equation Models

A set of tools for evaluating several measures of case influence for structural equation models.

Identifiability of Linear Structural Equation Models

Provides routines to check identifiability or non-identifiability of linear structural equation models as described in Drton, Foygel, and Sullivant (2011) , Foygel, Draisma, and Drton (2012) , and other works. The routines are based on the graphical representation of structural equation models.

Helper Functions for Structural Equation Modeling

An assortment of helper functions for doing structural equation modeling, mainly by 'lavaan' for now. Most of them are time-saving functions for common tasks in doing structural equation modeling and reading the output. This package is not for functions that implement advanced statistical procedures. It is a light-weight package for simple functions that do simple tasks conveniently, with as few dependencies as possible.

Spatially Explicit Structural Equation Modeling

Structural equation modeling is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with inter-correlated dependent and independent variables. Here we implement a simple method for spatially explicit structural equation modeling based on the analysis of variance co-variance matrices calculated across a range of lag distances. This method provides readily interpreted plots of the change in path coefficients across scale.

Bootstrapping Helpers for Structural Equation Modelling

A collection of helper functions for forming bootstrapping confidence intervals and examining bootstrap estimates in structural equation modelling. Currently supports models fitted by the 'lavaan' package by Rosseel (2012) .

Symbolic Computation for Structural Equation Models

A collection of functions for symbolic computation using the 'caracas' package for structural equation models and other statistical analyses. Among its features is the ability to calculate the model-implied covariance (and correlation) matrix and the sampling covariance matrix of variable functions using the delta method.