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: | 4.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | parallel |
| Suggests: | knitr, plot.matrix |
| Published: | 2024-10-27 |
| DOI: | 10.32614/CRAN.package.sparseSEM |
| Author: | Anhui Huang [aut, ctb, cre] |
| Maintainer: | Anhui Huang <anhuihuang at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | sparseSEM results |
| Package source: | sparseSEM_4.1.tar.gz |
| Windows binaries: | r-devel: sparseSEM_4.1.zip, r-release: sparseSEM_4.1.zip, r-oldrel: sparseSEM_4.1.zip |
| macOS binaries: | r-release (arm64): sparseSEM_4.1.tgz, r-oldrel (arm64): sparseSEM_4.1.tgz, r-release (x86_64): sparseSEM_4.1.tgz, r-oldrel (x86_64): sparseSEM_4.1.tgz |
| Old sources: | sparseSEM archive |
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