The Gaussian location-scale regression model is a multi-predictor
    model with explanatory variables for the mean (= location) and the standard
    deviation (= scale) of a response variable. This package implements maximum
    likelihood and Markov chain Monte Carlo (MCMC) inference (using algorithms
    from Girolami and Calderhead (2011) <doi:10.1111/j.1467-9868.2010.00765.x>
    and Nesterov (2009) <doi:10.1007/s10107-007-0149-x>), a parametric
    bootstrap algorithm, and diagnostic plots for the model class.

Implements Expectation/Conditional Maximization Either (ECME),
    rapidly converging algorithms, and Bayesian inference for linear mixed
    models following Schafer (1998), "Some Improved Procedures for Linear
    Mixed Models", Department of Statistics, The Pennsylvania State
    University.

In addition to modeling the expectation (location) of an outcome, mixed effects location scale models (MELSMs) include submodels on the variance components (scales) directly. This allows models on the within-group variance with mixed effects, and between-group variances with fixed effects. The MELSM can be used to model volatility, intraindividual variance, uncertainty, measurement error variance, and more. Multivariate MELSMs (MMELSMs) extend the model to include multiple correlated outcomes, and therefore multiple locations and scales. The latent multivariate MELSM (LMMELSM) further includes multiple correlated latent variables as outcomes. This package implements two-level mixed effects location scale models on multiple observed or latent outcomes, and between-group variance modeling. Williams, Martin, Liu, and Rast (2020) <doi:10.1027/1015-5759/a000624>. Hedeker, Mermelstein, and Demirtas (2008) <doi:10.1111/j.1541-0420.2007.00924.x>.

Fit right censored Multiple Ordinal Tobit (MOT) model.

Embarrassingly Parallel Linear Mixed Model calculations spread across local cores which repeat until convergence.

Implements a partitioned Empirical Bayes Expectation Conditional
    Maximization (ECM) algorithm for sparse high-dimensional linear mixed
    modeling as described in Zgodic, Bai, Zhang, and McLain (2025)
    <doi:10.1007/s11222-025-10649-z>. The package provides efficient estimation
    and inference for mixed models with high-dimensional fixed effects.