Provides a lightweight framework for creating high quality, complex 
    heatmaps using base graphics. Supports hierarchical clustering with 
    dendrograms, column and row scaling, cluster sub-divisions, customizable 
    cell colours, shapes and sizes, legends, and flexible layouts for arranging 
    multiple heatmaps. Designed to return plot objects that can be easily 
    arranged with other plots without sacrificing resolution. Methods for 
    hierarchical clustering and distance computations are described in 
    Murtagh and Contreras (2012) <doi:10.1002/wics.53>. Dendrogram 
    visualisation methods are based on the 'ggdendro' package by 
    de Vries and Ripley (2020) <https://CRAN.R-project.org/package=ggdendro>.

Creates self-contained widgets for interactive phylogenetic tree
    visualization. This package wraps the 'JavaScript' 'heat-tree' package using
    the 'htmlwidgets' R package.

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.