Author: Christian E. Galarza <chedgala@espol.edu.ec>, Luis Benites <lbenitess@pucp.edu.pe>, Marcelo Bourguignon <m.p.bourguignon@gmail.com>, Victor H. Lachos <hlachos@uconn.edu>
Description: It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. Missing values and censored observations can be handled as well. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Galarza et.al.(2020) <doi:10.1007/s13571-020-00231-0>. It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.

Imports: graphics, stats, spatstat.geom, numDeriv, MomTrunc, quantreg,

Title: A Package for Processing Lexical Response Data
person("Erin M.", "Buchanan", role = c("aut"),
Description: Lexical response data is a package that
can be used for processing cued-recall, free-recall,
and sentence responses from memory experiments.
Author: Nicholas Maxwell [aut, cre] (<https://orcid.org/0000-0003-4517-8323>),

Title: Sources an R "Module" with Caching & Encapsulation, Returning
Description: 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.

Title: Linear Regression by Gibbs Sampling
Description: 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.

Description: 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>.