sparseSEM: 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.

Version: 4.0
Depends: R (≥ 3.5.0)
Imports: parallel
Suggests: knitr, plot.matrix
Published: 2023-08-09
DOI: 10.32614/CRAN.package.sparseSEM
Author: Anhui Huang [aut, ctb, cre]
Maintainer: Anhui Huang <anhuihuang at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: yes
Materials: README
CRAN checks: sparseSEM results


Reference manual: sparseSEM.pdf
Vignettes: Elastic Net Penalized Structural Equation Models
Elastic Net Enabled Sparse-Aware Maximum Likelihood for Structural Equation Models in Inferring Gene Regulatory Networks


Package source: sparseSEM_4.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sparseSEM_4.0.tgz, r-oldrel (arm64): sparseSEM_4.0.tgz, r-release (x86_64): sparseSEM_4.0.tgz, r-oldrel (x86_64): sparseSEM_4.0.tgz
Old sources: sparseSEM archive


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