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.

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.

Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)

Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) The constrained- unconstrained, residual- and double centering- approaches are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via 'modsem' it self. Alternatively model can be estimated via 'Mplus' (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). . "A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3." Klein, A., & Moosbrugger, H. (2000). . "Maximum likelihood estimation of latent interaction effects with the LMS method." Klein, A. G., & Muthén, B. O. (2007). . "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). . "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). . "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." Marsh, H. W., Wen, Z., & Hau, K. T. (2004). . "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." Muthén, L.K. and Muthén, B.O. (1998-2017). "'Mplus' User’s Guide. Eighth Edition." < https://www.statmodel.com/>. Rosseel Y (2012). . "'lavaan': An R Package for Structural Equation Modeling."

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 .

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.