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.

Conducts and visualizes propensity score analysis for
    multilevel, or clustered data. Bryer & Pruzek (2011)
    <doi:10.1080/00273171.2011.636693>.

Effect sizes, diagnostics and performance metrics for
  multilevel and mixed effects models.
  Includes marginal and conditional 'R2' estimates for linear mixed effects models
  based on Johnson (2014) <doi:10.1111/2041-210X.12225>.

Efficiently estimates single- and multilevel latent class models with covariates, allowing for output visualization in all specifications. For more technical details, see Lyrvall et al. (2025) <doi:10.1080/00273171.2025.2473935>.