Robust estimators for generalized ratio model (Wada, Sakashita and Tsubaki, 2021)<doi:10.17713/ajs.v50i1.994> and linear regression model by the IRLS(iterative reweighted least squares) algorithm are contained.

Three-step regression and inference for cellwise and casewise contamination.

We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter  estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.

Robust methods for estimating the parameters of multivariate Gaussian linear models.

A framework for estimating ensembles of parametric survival models
    with different parametric families. The RoBSA framework uses Bayesian 
    model-averaging to combine the competing parametric survival models into 
    a model ensemble, weights the posterior parameter distributions based on 
    posterior model probabilities and uses Bayes factors to test for the 
    presence or absence of the individual predictors or preference for a 
    parametric family (Bartoš, Aust & Haaf, 2022, <doi:10.1186/s12874-022-01676-9>).
    The user can define a wide range of informative priors for all parameters 
    of interest. The package provides convenient functions for summary, visualizations, 
    fit diagnostics, and prior distribution calibration.

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.