Elastic Net Penalized Maximum Likelihood for Structural Equation Models with Network GPT Framework R package sparseSEM version 4.1 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. Software Depends: R (>= 3.5.0) Imports: parallel Suggests: knitr,plot.matrix Anhui Huang <anhuihuang@gmail.com> Comprehensive R Archive Network (CRAN) Anhui Huang [aut, ctb, cre] GPL 2024-10-27 application/tgz https://CRAN.R-project.org/package=sparseSEM doi:10.32614/CRAN.package.sparseSEM