Maximum likelihood estimation of an extended class of row-column (RC) association models for two-dimensional contingency tables, which are formulated by a condition of reduced rank on a matrix of extended association parameters; see Forcina (2019) <arXiv:1910.13848>. These parameters are defined by choosing the logit type for the row and column variables among four different options and a transformation derived from suitable divergence measures.

A set of procedures for parametric and non-parametric modelling of the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. It adapts the methodologies of Beranger and Padoan (2015) <doi:10.48550/arXiv.1508.05561>, Marcon et al. (2016) <doi:10.1214/16-EJS1162>, Marcon et al. (2017) <doi:10.1002/sta4.145>, Marcon et al. (2017) <doi:10.1016/j.jspi.2016.10.004> and Beranger et al. (2021) <doi:10.1007/s10687-019-00364-0>. This package also allows for the modelling of spatial extremes using flexible max-stable processes. It provides simulation algorithms and fitting procedures relying on the Stephenson-Tawn likelihood as per Beranger at al. (2021) <doi:10.1007/s10687-020-00376-1>.

An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis.

This framework provides versatile algorithms to efficiently infer confidence intervals for
  extreme value statistics, such as extreme quantiles and return levels, that are representative
  of the asymmetric uncertainty spread, using extreme value theory extrapolation and the profile likelihood
  (see e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>).
  Unlike existing algorithms, the CI endpoints are found without the need for a strict prespecified range,
  can be covariate-dependent, and can be based on weighted samples.
  This package is motivated by Zeder et al. (2023) <doi:10.1029/2023GL104090> and by
  Pasche et al. (2026) <doi:10.1007/s10687-026-00536-9>.