Elastic Net Penalized Maximum Likelihood for Structural Equation Models with Netowrk GPT Framework ================
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:
Network GPT
that generates a complete (fully
connected) graph from l2 penalized SEM (i.e., ridge
SEM); andelastic net
(l1 and l2)
penalized SEM.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:
Network Inferrence via sparseSEM
to enable quick setup and
running of the package;yeast GRN
real dataset that was used to
generate the graph in the vignettes;Version 3.8:
Version 3 is a major release that updates BLAS/Lapack routines according to R-API change.
Huang Anhui. (2014)
Sparse Model Learning for Inferring Genotype
and Phenotype Associations.
Ph.D Dissertation, University of Miami,
Coral Gables, FL, USA.