Methods for robust statistics, a state of the art in the early
  2000s, notably for robust regression and robust multivariate analysis.

An implementation of easy tools for outlier robust inference in
    two-stage least squares (2SLS) models. The user specifies a reference 
    distribution against which observations are classified as outliers or not. 
    After removing the outliers, adjusted standard errors are automatically 
    provided. Furthermore, several statistical tests for the false outlier 
    detection rate can be calculated. The outlier removing algorithm can be 
    iterated a fixed number of times or until the procedure converges. The 
    algorithms and robust inference are described in more detail in Jiao (2019) 
    <https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.

Provides a method to decompose a univariate time series into meaningful subcomponents for analysis and denoising.

R functions for the computation of the truncated maximum
	     likelihood and the robust accelerated failure time regression 
	     for gaussian and log-Weibull case.

Robust tests (RW, RPB and RGF) are provided for testing the equality of several long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW, RPB and RGF tests are robust versions of Welch's F test proposed by Welch (1951) <doi:10.2307/2332579>, parametric bootstrap test proposed by Krishnamoorthy et. al (2007) <doi:10.1016/j.csda.2006.09.039>; and generalized F test proposed by Weerahandi (1995) <doi:10.2307/2532947>;, respectively. These tests are based on the modified maximum likelihood (MML) estimators proposed by Tiku(1967, 1968) <doi:10.2307/2333859>, <doi:10.1080/01621459.1968.11009228>.

Functions for fitting a linear regression model with ARIMA
  errors using a filtered tau-estimate.
  The methodology is described in Maronna et al (2017, ISBN:9781119214687).

"Essential" Robust Statistics.
 Tools allowing to analyze data with robust methods.  This includes
 regression methodology including model selections and multivariate
 statistics where we strive to cover the book "Robust Statistics,
 Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.