Structural Equation Models

Functions for fitting general linear structural equation models (with observed and latent variables) using the RAM approach, and for fitting structural equations in observed-variable models by two-stage least squares.

Useful Tools for Structural Equation Modeling

Provides miscellaneous tools for structural equation modeling, many of which extend the 'lavaan' package. For example, latent interactions can be estimated using product indicators (Lin et al., 2010, ) and simple effects probed; analytical power analyses can be conducted (Jak et al., 2021, ); and scale reliability can be estimated based on estimated factor-model parameters.

Meta-Analysis using Structural Equation Modeling

A collection of functions for conducting meta-analysis using a structural equation modeling (SEM) approach via the 'OpenMx' and 'lavaan' packages. It also implements various procedures to perform meta-analytic structural equation modeling on the correlation and covariance matrices, see Cheung (2015) .

Piecewise Structural Equation Modeling

Implements piecewise structural equation modeling from a single list of structural equations, with new methods for non-linear, latent, and composite variables, standardized coefficients, query-based prediction and indirect effects. See < http://jslefche.github.io/piecewiseSEM/> for more.

Fit Structural Equation Models to Multiply Imputed Data

The primary purpose of 'lavaan.mi' is to extend the functionality of the R package 'lavaan', which implements structural equation modeling (SEM). When incomplete data have been multiply imputed, the imputed data sets can be analyzed by 'lavaan' using complete-data estimation methods, but results must be pooled across imputations (Rubin, 1987, ). The 'lavaan.mi' package automates the pooling of point and standard-error estimates, as well as a variety of test statistics, using a familiar interface that allows users to fit an SEM to multiple imputations as they would to a single data set using the 'lavaan' package.

Composite-Based Structural Equation Modeling

Estimate, assess, test, and study linear, nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures, including estimation techniques such as partial least squares path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), generalized structured component analysis (GSCA), generalized structured component analysis with uniqueness terms (GSCAm), generalized canonical correlation analysis (GCCA), principal component analysis (PCA), factor score regression (FSR) using sum score, regression or Bartlett scores (including bias correction using Croon’s approach), as well as several tests and typical postestimation procedures (e.g., verify admissibility of the estimates, assess the model fit, test the model fit etc.).

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.

Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

A robust procedure is implemented to estimate means and covariance matrix of multiple variables with missing data using Huber weight and then to estimate a structural equation model.

Model Implied Instrumental Variable (MIIV) Estimation of Structural Equation Models

Functions for estimating structural equation models using instrumental variables.

Hierarchical Structural Equation Model

We present this package for fitting structural equation models using the hierarchical likelihood method. This package allows extended structural equation model, including dynamic structural equation model. We illustrate the use of our packages with well-known data sets. Therefore, this package are able to handle two serious problems inadmissible solution and factor indeterminacy .