An implementation of ranked sparsity methods, including
    penalized regression methods such as the sparsity-ranked lasso, its
    non-convex alternatives, and elastic net, as well as the sparsity-ranked
    Bayesian Information Criterion. As described in Peterson and
    Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked
    sparsity is a philosophy with methods primarily useful for
    variable selection in the presence of prior informational
    asymmetry, which occurs in the context of trying to perform variable
    selection in the presence of interactions and/or polynomials. Ultimately,
    this package attempts to facilitate dealing with cumbersome interactions
    and polynomials while not avoiding them entirely. Typically, models
    selected under ranked sparsity principles will also be more transparent,
    having fewer falsely selected interactions and polynomials than other
    methods.

Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects.  This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots.  The method can implement sparse linear regression and sparse probit regression.  Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to  models with truncated outcomes, propensity score, and instrumental variable analysis.

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.