Package: sparseSEM
Type: Package
Title: Elastic Net Penalized Maximum Likelihood for Structural Equation
        Models with Network GPT Framework
Version: 4.1
Date: 2024-10-25
Authors@R: c(person("Anhui", "Huang", role=c("aut","ctb", "cre" ), email="anhuihuang@gmail.com"))
Maintainer: Anhui Huang <anhuihuang@gmail.com>
Description: 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.
Depends: R (>= 3.5.0)
Imports: parallel
License: GPL
Packaged: 2024-10-27 15:12:26 UTC; anhui
NeedsCompilation: yes
Repository: CRAN
Suggests: knitr,plot.matrix
VignetteBuilder: knitr
Date/Publication: 2024-10-27 15:30:02 UTC
Author: Anhui Huang [aut, ctb, cre]
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-25 22:30:39 UTC; unix
Archs: sparseSEM.so.dSYM
