The different methods for defining, detecting, and categorising the extreme events 
    known as heatwaves or cold-spells, as first proposed in Hobday et al. (2016) <doi: 10.1016/j.pocean.2015.12.014> 
    and Hobday et al. (2018) <https://www.jstor.org/stable/26542662>. The functions in this package work on both air 
    and water temperature data of hourly and daily temporal resolution. These detection algorithms may be used on 
    non-temperature data as well.

Implements the estimators and algorithms described in Chapters 8 and 9 of the book "The Fundamentals of Heavy Tails: Properties, Emergence, and Estimation" by Nair et al. (2022, ISBN:9781009053730). These include the Hill estimator, Moments estimator, Pickands estimator, Peaks-over-Threshold (POT) method, Power-law fit, and the Double Bootstrap algorithm.

Provides a case conversion between common cases like CamelCase and 
    snake_case. Using the 'rust crate heck' <https://github.com/withoutboats/heck>
    as the backend for a highly performant case conversion for 'R'.

It performs maximum likelihood estimation for the Heckman selection model (Normal, Student-t or Contaminated normal) using an EM-algorithm <doi:10.1016/j.jmva.2021.104737>. It also performs influence diagnostic through global and local influence for four possible perturbation schema.

Tools for the estimation of Heckman selection models with robust 
  variance-covariance matrices. It includes functions for computing the bread 
  and meat matrices, as well as clustered standard errors for generalized Heckman 
  models, see Fernando de Souza Bastos and Wagner Barreto-Souza and Marc G. Genton 
  (2022, ISSN: <https://www.jstor.org/stable/27164235>). The package also offers 
  cluster-robust inference with sandwich estimators, and tools for handling issues 
  related to eigenvalues in covariance matrices.

Implements Heckman selection models using a Bayesian approach via 'Stan' and compares the performance of normal, Student’s t, and contaminated normal distributions in addressing complexities and selection bias (Heeju Lim, Victor E. Lachos, and Victor H. Lachos, Bayesian analysis of flexible Heckman selection models using Hamiltonian Monte Carlo, 2025, under submission).