An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) <http://proceedings.mlr.press/v108/cisneros20a.html>.

Allows loading and displaying an Observable notebook (online JavaScript  
    notebooks powered by <https://observablehq.com>) as an HTML Widget in an R 
    session, 'shiny' application or 'rmarkdown' document.

Fast algorithms for the Theil-Sen estimator,
  Siegel's repeated median slope estimator, and Passing-Bablok regression.
  The implementation is based on algorithms by
  Dillencourt et. al (1992) <doi:10.1142/S0218195992000020> and Matousek et. al (1998)  <doi:10.1007/PL00009190>.
  The implementations are detailed in 
  Raymaekers (2023) <doi:10.32614/RJ-2023-012> and 
  Raymaekers J., Dufey F. (2022) <doi:10.48550/arXiv.2202.08060>.
  All algorithms run in quasilinear time.

Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>.  This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.

Functions to perform robust stepwise split regularized regression. The approach first 
             uses a robust stepwise algorithm to split the variables into the models of an ensemble. 
             An adaptive robust regularized estimator is then applied to each subset of predictors
             in the models of an ensemble.

Robust (outlier-resistant) estimators of finite population
    characteristics like of means, totals, ratios, regression, etc. Available
    methods are M- and GM-estimators of regression, weight reduction,
    trimming, and winsorization. The package extends the 'survey'
    <https://CRAN.R-project.org/package=survey> package.

An implementation of Bayesian model-averaged t-tests that allows 
    users to draw inferences about the presence versus absence of an effect, 
    variance heterogeneity, and potential outliers. The 'RoBTT' package estimates 
    ensembles of models created by combining competing hypotheses and applies 
    Bayesian model averaging using posterior model probabilities. Users can 
    obtain model-averaged posterior distributions and inclusion Bayes factors, 
    accounting for uncertainty in the data-generating process 
    (Maier et al., 2024, <doi:10.3758/s13423-024-02590-5>). The package also 
    provides a truncated likelihood version of the model-averaged t-test, 
    enabling users to exclude potential outliers without introducing bias 
    (Godmann et al., 2024, <doi:10.31234/osf.io/j9f3s>). Users can specify 
    a wide range of informative priors for all parameters of interest. 
    The package offers convenient functions for summary, visualization, 
    and fit diagnostics.