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. These detection algorithms may be used on non-temperature data as well.

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.

This tool identifies hydropeaking events from raw time-series flow record, a rapid flow variation induced by the hourly-adjusted electricity market. The novelty of 'HEDA' is to use vector angle instead of the first-order derivative to detect change points which not only largely improves the computing efficiency but also accounts for the rate of change of the flow variation. More details <doi:10.1016/j.jhydrol.2021.126392>.

Helper functions designed to make
    dynamically generating R Markdown documents easier by providing a
    simple and tidy way to create report pieces, shape them to your data,
    and combine them for exporting into a single R Markdown document.