Elastic Net Penalized Maximum Likelihood for Structural Equation Models with Netowrk GPT Framework ================

CRAN_Status_Badge

We provide extremely efficient procedures for fitting the lasso and elastic net regularized Structural Equation Models (SEM). The model output can be used for inferring network structure (topology) and estimating causal effects. Key features include sparse variable selection and effect estimation via l1 and l2 penalized maximum likelihood estimator (MLE) implemented with BLAS/Lapack routines. The implementation enables extremely efficient computation. Details can be found in Huang A. (2014).

To achieve high performance accuracy, the software implements a Network Generative Pre-traning Transformer (GPT) framework:

Note that the term Transformer does not carry the same meaning as the transformer architecture commonly used in Natural Language Processing (NLP). In Network GPT, the term refers to the creation and generation of the complete graph.

Version 4.0:

Version 3.8:

Version 3 is a major release that updates BLAS/Lapack routines according to R-API change.

References

Huang Anhui. (2014)
Sparse Model Learning for Inferring Genotype and Phenotype Associations.
Ph.D Dissertation, University of Miami, Coral Gables, FL, USA.