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) <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-test that allows 
    users to draw inference about the presence vs absence of the effect, 
    heterogeneity of variances, and outliers. The 'RoBTT' packages estimates model 
    ensembles of models created as a combination of the competing hypotheses and uses 
    Bayesian model-averaging to combine the models using posterior model probabilities. 
    Users can obtain the model-averaged posterior distributions and inclusion Bayes 
    factors which account for the uncertainty in the data generating process 
    (Maier et al., 2022, <doi:10.31234/osf.io/d5zwc>).
    Users can define a wide range of informative priors for all parameters 
    of interest. The package provides convenient functions for summary, visualizations, 
    and fit diagnostics.