Elastic Net Penalized Maximum Likelihood for Structural Equation Models with Network GPT Framework
The Elastic Net penalized SEM with Network GPT Framework
The Elastic Net penalty for SEM with user supplied (alphas, lambdas) f...
The Elastic Net penalty for SEM
Parallel Stability Selection for the Elastic Net penalized SEM
Stability Selection for the Elastic Net penalized SEM
The Lasso penalty for SEM
Internal sparseSEM function
sparseSEM: Elastic Net Penalized Maximum Likelihood for Structural Equ...
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.