The programs were developed for estimation of parameters and testing exponential versus Pareto distribution during our work on hydrologic extremes. See Kozubowski, T.J., A.K. Panorska, F. Qeadan, and A. Gershunov (2007) <doi:10.1080/03610910802439121>, and Panorska, A.K., A. Gershunov, and T.J. Kozubowski (2007) <doi:10.1007/978-0-387-34918-3_26>.

In the fashion of 'node.js' <https://nodejs.org/>, requires a file,
  sourcing into the current environment only the variables explicitly specified
  in the module.exports or exports list variable. If the file was already sourced,
  the result of the earlier sourcing is returned to the caller.

Implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution. Described further in Mantz (2016) <DOI:10.1093/mnras/stv3008>.

Provides S3 classes to represent low rank matrix
    decompositions.

Provides methods for linear regression in the presence of missing data,
    including missingness in covariates and responses. The package implements two
    estimators: oss_estimator(), a low-dimensional semi-supervised method, and
    dantzig_missing(), a high-dimensional approach. The tuning parameter can be
    selected automatically via cv_dantzig_missing(). See Risebrow and Berrett (2026)
    <doi:10.48550/arXiv.2602.13729>. Optional support for the 'gurobi' optimizer via
    the 'gurobi' R package (available from Gurobi, see
    <https://docs.gurobi.com/projects/optimizer/en/current/reference/r.html>).

A Low Rank Correction Variational Bayesian algorithm for high-dimensional multi-source 
    heterogeneous quantile linear models. More details have been written up in a paper 
    submitted to the journal Statistics in Medicine, and the details of variational
    Bayesian methods can be found in Ray and Szabo (2021) <doi:10.1080/01621459.2020.1847121>.
    It simultaneously performs parameter estimation and variable selection. The 
    algorithm supports two model settings: (1) local models, where variable selection
    is only applied to homogeneous coefficients, and (2) global models, where variable
    selection is also performed on heterogeneous coefficients. Two forms of parameter 
    estimation are output: one is the standard variational Bayesian estimation, 
    and the other is the variational Bayesian estimation corrected with low-rank adjustment.