Title: An Alternative to the Kruskal-Wallis Based on the Kendall Tau

Description: The Concordance Test is a non-parametric method for testing whether two o more samples originate from the same distribution. It extends the Kendall Tau correlation coefficient when there are only two groups. For details, see Monge (2020) <arXiv:1912.12880v2>.

Title: Estimation and Inference for Conditional Copula Models
person("Alexis", "Derumigny", , email = "a.f.f.derumigny@tudelft.nl", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6163-8097")),
person("Jean-David", "Fermanian", , email = "Jean-David.Fermanian@ensae.fr", role = c("ctb","ths"), comment = c(ORCID = "0000-0001-5960-5555")),
person("Rutger", "van der Spek", , role = "ctb")
Provides functions for the estimation of conditional copulas models,
various estimators of conditional Kendall's tau
(proposed in Derumigny and Fermanian (2019a, 2019b, 2020) <doi:10.1515/demo-2019-0016>,
and test procedures for the simplifying assumption
(proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011>
and Derumigny, Fermanian and Min (2020) <arXiv:2008.09498>).
Suggests: MASS, knitr, rmarkdown, ggplot2, mvtnorm
Imports: VineCopula, pbapply, glmnet, ordinalNet, tree, nnet,

Title: Nonlinear Conditional Independence Tests
Author: Christina Heinze-Deml <heinzedeml@stat.math.ethz.ch>, Jonas Peters <jonas.peters@math.ku.dk>
Description: Code for a variety of nonlinear conditional independence tests:
Kernel conditional independence test (Zhang et al., UAI 2011, <arXiv:1202.3775>),
Residual Prediction test (based on Shah and Buehlmann, <arXiv:1511.03334>),
Invariant conditional quantile prediction (all from Heinze-Deml et al., <arXiv:1706.08576>).
Imports: methods, randomForest, quantregForest, lawstat, RPtests,

Asbjoern Marco Sinius Munk <fgp998@alumni.ku.dk>
caTools, mgcv, MASS, kernlab, pracma, mize

Title: Conditional Multivariate t Distribution, Expectation
Maximization Algorithm, and Its Stochastic Variants
Authors@R: c(person("Paul", "Kinyanjui", role=c("aut", "cre"),
Description: Computes conditional multivariate t probabilities, random deviates, and densities. It can also be used to create missing values at random in a dataset, resulting in a missing at random (MAR) mechanism. Inbuilt in the package are the Expectation-Maximization (EM), Monte Carlo EM, and Stochastic EM algorithms for imputation of missing values in datasets assuming the multivariate t distribution. See Kinyanjui, Tamba, Orawo, and Okenye (2020)<doi:10.3233/mas-200493>, and Kinyanjui, Tamba, and Okenye(2021)<http://www.ceser.in/ceserp/index.php/ijamas/article/view/6726/0> for more details.