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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 gmail.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL] |
NeedsCompilation: | yes |
Materials: | README |
CRAN checks: | sparseSEM results |
Package source: | sparseSEM_4.0.tar.gz |
Windows binaries: | r-devel: sparseSEM_4.0.zip, r-release: sparseSEM_4.0.zip, r-oldrel: sparseSEM_4.0.zip |
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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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