Bivariate Copula Functions Based on Regular Grid
Estimates grid type bivariate copula functions, calculates some association measures and provides several copula graphics.
c(person("Rogelio Salinas Gutiérrez", "Developer" , role = c("aut", "cre","cph"),
email = "rsalinas@correo.uaa.mx"),
person("Angélica Hernández Quintero", "Dev", role = "aut"),
person("Pedro Abraham Montoya Calzada", "Dev", role = "cph"))
Rsolnp, fields, limSolve, ggplot2, pracma, reshape2
Rogelio Salinas Gutiérrez Developer [aut, cre, cph],
Angélica Hernández Quintero Dev [aut],
Pedro Abraham Montoya Calzada Dev [cph]
Rogelio Salinas Gutiérrez Developer <rsalinas@correo.uaa.mx>

General Regression Neural Networks Package
person(given = "Shufeng",
family = "LI",
role = c("aut", "cre"),
email = "lisf@xtbg.org.cn",
comment = c(ORCID = "0000-0002-5144-4432"))
This General Regression Neural Networks Package uses various distance functions.
It was motivated by Specht (1991, ISBN:1045-9227), and updated from previous published paper
Li et al. (2016) <doi:10.1016/j.palaeo.2015.11.005>. This package includes various functions,
although "euclidean" distance is used traditionally.

Workbench for testing genomic regression accuracy on (optionally noisy) phenotypes.
BGLR, e1071, ggplot2, knitr, randomForest

BGLR, e1071, ggplot2, knitr, randomForest, rmarkdown

Workbench for testing genomic regression accuracy on
(optionally noisy) phenotypes.

Goodness-of-Fit Tests in High-Dimensional GLMs
Jana Jankova [aut, cre], Rajen Shah [aut], Peter Buehlmann [aut], Richard Samworth [aut]
Methodology for testing nonlinearity in the conditional mean function
in low- or high-dimensional generalized linear models, and the significance of
(potentially large) groups of predictors. Details on the algorithms can be
found in the paper by Jankova, Shah, Buehlmann and Samworth (2019)
<arXiv:1908.03606>.
glmnet, randomForest, MASS, stats, RPtests

glmnet, randomForest, MASS, stats, RPtests, ranger

Walter Resch with information from 'Measurement System Assessment Via Generalized Inference'
by Michael Hamada and Sam Weerandi
Generates confidence intervals for the variance components in Gauge R and R data using ANOVA
with the Satterthwaite approximation as well as the method of Generalized Inference.