The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for
 multilevel mediation, the option of a moderating variable for either the a, b,
 or both paths, and covariates. Currently the 1-1-1 model is supported
 and several options of random effects; the initial code for bootstrapping was
 evaluated in simulations by Falk, Vogel, Hammami, and Miočević (2024) <doi:10.3758/s13428-023-02079-4>.
 Support for Bayesian estimation using 'brms' comprises ongoing work. Currently
 only continuous mediators and outcomes are supported. Factors for any
 predictors must be numerically represented.

Bindings for hierarchical regression models for use with the
    'parsnip' package. Models include longitudinal generalized linear
    models (Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and
    mixed-effect models (Pinheiro and Bates)
    <doi:10.1007/978-1-4419-0318-1_1>.

Implements a regularized Bayesian estimator that optimizes the estimation
 of between-group coefficients for multilevel latent variable models by minimizing
 mean squared error (MSE) and balancing variance and bias. The package provides more reliable
 estimates in scenarios with limited data, offering a robust solution for accurate
 parameter estimation in two-level latent variable models. It is designed for
 researchers in psychology, education, and related fields who face challenges in
 estimating between-group effects under small sample sizes and low intraclass
 correlation coefficients. The package includes comprehensive S3 methods for result
 objects: print(), summary(), coef(), se(), vcov(), confint(), as.data.frame(),
 dim(), length(), names(), and update() for enhanced usability and integration
 with standard R workflows. Dashuk et al. (2025a) <doi:10.1017/psy.2025.10045>
 derived the optimal regularized Bayesian estimator;
 Dashuk et al. (2025b) <doi:10.1007/s41237-025-00264-7> extended it to 
 the multivariate case; and Luedtke et al. (2008) <doi:10.1037/a0012869>
 formalized the two-level latent variable framework.