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.

Implementation of corrected two-sample tests. A corrected version of the Pearson and Kendall correlation tests, 
 the Mann-Whitney (Wilcoxon) rank sum test, the Wilcoxon signed rank test and a variance test are implemented.
 The package also proposes a test for the median and an independence test between two continuous variables of Kolmogorov-Smirnov's type. 
 All these corrected tests are asymptotically calibrated in the sense that the probability of rejection under the null hypothesis is 
 asymptotically equal to the level of the test. See <doi:10.48550/arXiv.2211.08784> for more details on the statistical tests.

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.

Outliers virtually exist in any datasets of any application field. 
  To avoid the impact of outliers, we need to use robust estimators. 
  Classical estimators of multivariate mean and covariance matrix are 
  the sample mean and the sample covariance matrix. Outliers will 
  affect the sample mean and the sample covariance matrix, and thus 
  they will affect the classical factor analysis which depends on 
  the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser, 
  P. and Croux, C. (2003) <doi:10.1016/S0047-259X(02)00007-6>). 
  So it is necessary to use the robust estimators of the sample 
  mean and the sample covariance matrix. There are several robust 
  estimators in the literature: Minimum Covariance Determinant estimator, 
  Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid, 
  M, S, and Stahel-Donoho. 
  The most direct way to make multivariate analysis more robust is to replace
  the sample mean and the sample covariance matrix of the classical estimators 
  to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006) 
  <doi:10.1002/0470010940>) (Todorov, V. and Filzmoser, P. (2009) 
  <doi:10.18637/jss.v032.i03>), which is our choice of robust factor
  analysis. We created an object oriented solution for robust factor 
  analysis based on new S4 classes.