person("Jonas", "Kurle", email = "mail@jonaskurle.com",
role = c("aut","cre"), comment = c(ORCID = "0000-0003-2197-2012"))
datasets, doFuture, doParallel, future, ggplot2, grDevices,
knitr, parallel, rmarkdown, testthat, utils
AER, doRNG, foreach, MASS, mathjaxr, pracma, stats

covr, datasets, doFuture, doParallel, future, ggplot2,
grDevices, ivgets, knitr, parallel, rmarkdown, testthat, utils
doRNG, exactci, foreach, ivreg, MASS, mathjaxr, pracma, stats

Robust Bayesian Linear Mixed-Effects Models using ABC
Bayesian robust fitting of linear mixed effects models through weighted likelihood equations and approximate Bayesian computation as proposed by Ruli et al. (2017) <arXiv:1706.01752>.
Rcpp (>= 0.12.1), numDeriv, mvtnorm, utils, parallel,
doParallel, foreach, iterators, stats

Erlis Ruli [aut, cre],
Nicola Sartori [aut],
Laura Ventura [aut]

Robust mixture discriminant analysis (RMDA, Bouveyron & Girard, 2009) allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.

Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 <doi:10.1016/j.patcog.2009.03.027>, allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.

Robust Methods using Exponential Tilt Model
Chuan Hong, Yong Chen, Yang Ning, Hao Wu
Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison.