graphics, stats, spatstat.geom, numDeriv, MomTrunc, quantreg,
MASS

A Package for Processing Lexical Response Data
c(person(given = "Nicholas",
family = "Maxwell",
role = c("aut", "cre"),
email = "nicholas.maxwell@usm.edu",
comment = c(ORCID = "0000-0003-4517-8323")),
person("Erin M.", "Buchanan", role = c("aut"),
email = "buchananlab@gmail.com",
comment = c(ORCID = "0000-0002-9689-4189")))
Lexical response data is a package that
can be used for processing cued-recall, free-recall,
and sentence responses from memory experiments.
Nicholas Maxwell [aut, cre] (<https://orcid.org/0000-0003-4517-8323>),
Erin M. Buchanan [aut] (<https://orcid.org/0000-0002-9689-4189>)

Sources an R "Module" with Caching & Encapsulation, Returning
Exported Vars
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.

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>.

Different types of estimators to deal with multicollinearity.
c(person("Ajith", "Dissanayake", role = c("aut", "cre"),
email = "sudeera333@gmail.com"), person("P.", "Wijekoon", role
= ("aut"),email="pushpaw@pdn.ac.lk"), person("R-core",role =
"cph", email = "R-core@R-project.org"))
When multicollinearity exists among predictor variables of the linear model, least square estimators does not provide a better solution for estimating parameters. To deal with multicollinearity several estimators are proposed in the literature. Some of these estimators are Ordinary Least Square Estimator (OLSE), Ordinary Ridge Regression Estimator (ORRE), Restricted Least Square Estimator (RLSE), Ordinary Mixed Regression Estimator (OMRE), Liu Estimator (LE), Restricted Liu Estimator (RLE), Stochastic Restricted Liu Estimator (SRLE), Type (1) Liu Estimator (Type-1 LTE), Type (2) Liu Estimator (Type-2 LTE), Type (3) Liu Estimator (Type-3 LTE), Type (1) Adjusted Liu Estimator (Type-1 ALTE), Type (2) Adjusted Liu Estimator (Type-2 ALTE), Type (3) Adjusted Liu Estimator (Type-3 ALTE), Almost Unbiased Ridge Estimator (AURE), Almost Unbiased Liu Estimator (AULE), Stochastic Restricted Ridge Estimator (SRRE) and Restricted Ridge Regression Estimator (RRRE). To select the best estimator in a practical situation the Mean Square Error (MSE) is used. Using this package scalar MSE value of all the above estimators and Prediction Sum of Square (PRESS) values of some of the estimators can be obtained, and the variation of the MSE and PRESS values for the relevant estimators can be shown graphically.
Ajith Dissanayake [aut, cre],
P. Wijekoon [aut],
R-core [cph]