Missing

<p>M by N matrix corresponding to elements of Y. 0 denotes no missing, while 1 denotes missing</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

Missing function

<p>The format is:
 num [1:30, 1:200] 0 0 0 0 0 0 0 0 0 0 ...</p>

Missing: Missing Network Node dependent variable data

Description

Usage

Arguments

Format

References

Examples