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.
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