Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework

Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Jin Liu (2023) .

Structural Equation Modeling Using the Reticular Action Model (RAM) Notation

We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features. First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models.

Influential Case Detection Methods for Factor Analysis and Structural Equation Models

Tools for detecting and summarize influential cases that can affect exploratory and confirmatory factor analysis models as well as structural equation models more generally (Chalmers, 2015, ; Flora, D. B., LaBrish, C. & Chalmers, R. P., 2012, ).

Fused Sparse Structural Equation Models to Jointly Infer Gene Regulatory Network

An optimizer of Fused-Sparse Structural Equation Models, which is the state of the art jointly fused sparse maximum likelihood function for structural equation models proposed by Xin Zhou and Xiaodong Cai (2018 ).

Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood or Least Squares

Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) or penalized least squares (PLS). For details, please see Huang (2020) .

Elastic Net Penalized Maximum Likelihood for Structural Equation Models with Network GPT Framework

Provides elastic net penalized maximum likelihood estimator for structural equation models (SEM). The package implements `lasso` and `elastic net` (l1/l2) penalized SEM and estimates the model parameters with an efficient block coordinate ascent algorithm that maximizes the penalized likelihood of the SEM. Hyperparameters are inferred from cross-validation (CV). A Stability Selection (STS) function is also available to provide accurate causal effect selection. The software achieves high accuracy performance through a `Network Generative Pre-trained Transformer` (Network GPT) Framework with two steps: 1) pre-trains the model to generate a complete (fully connected) graph; and 2) uses the complete graph as the initial state to fit the `elastic net` penalized SEM.

Latent Variable Models

A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) ). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) ). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.

Fitting Structural Equation Mixture Models

Estimation of structural equation models with nonlinear effects and underlying nonnormal distributions.

Bayesian Latent Variable Analysis

Fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) ; Merkle et al. (2021) .

Supplementary Item Response Theory Models

Supplementary functions for item response models aiming to complement existing R packages. The functionality includes among others multidimensional compensatory and noncompensatory IRT models (Reckase, 2009, ), MCMC for hierarchical IRT models and testlet models (Fox, 2010, ), NOHARM (McDonald, 1982, ), Rasch copula model (Braeken, 2011, ; Schroeders, Robitzsch & Schipolowski, 2014, ), faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011, ), ordinal IRT model (ISOP; Scheiblechner, 1995, ), DETECT statistic (Stout, Habing, Douglas & Kim, 1996, ), local structural equation modeling (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016, ).