<p>Y is the M by N matrix describes the dependent attribute of M nodes in N samples</p>

datasets

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.

Anhui Huang

sparseSEM

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

Y function

<p>The format is:
 num [1:30, 1:200] 3.02 1.12 -2.24 3.58 2.18 ...</p>

Y: Gene expression matrix

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Examples