Includes several statistical methods for the estimation of parameters and high quantiles of river flow distributions. The focus is on regional estimation based on homogeneity assumptions and computed from multivariate observations (multiple measurement stations).
	For details see Kinsvater et al. (2017) <arXiv:1701.06455>.

Plotting flood quantiles and their corresponding probabilities (return periods) on the probability papers.
    The details of relevant methods are available in Chow et al (1988, ISBN: 007070242X, 9780070702424), and
    Bobee and Ashkar (1991, ISBN: 0918334683, 9780918334688).

Provides an efficient 'C++' code for computing an
 optimal segmentation model
 with Poisson loss,
 up-down constraints,
 and label constraints,
 as described by Kaufman et al. (2024) <doi:10.1080/10618600.2023.2293216>.

Tools to quickly compile taxonomic and distribution data from
    the Brazilian Flora 2020.

A collection of functions designed to retrieve, filter and spatialize data from the Flora e Funga do Brasil dataset. For more information about the dataset, please visit <https://floradobrasil.jbrj.gov.br/consulta/>.

Log-ratio Lasso regression for continuous, binary, and survival outcomes with (longitudinal) compositional features. See Fei and others (2024) <doi:10.1016/j.crmeth.2024.100899>.

Interactive data visualization for data practitioners. 'flourishcharts' allows users to visualize their data 
  using 'Flourish' graphs that are grounded in data storytelling principles. Users can create racing bar & line
  charts, as well as other interactive elements commonly found in 'D3' graphics, easily in 'R' and 'Python'. 
  The package relies on an enterprise API provided by 'Flourish', a data visualization platform <https://developers.flourish.studio/api/introduction/>.

Visualize as flow diagrams the logic of functions, expressions or 
  scripts in a static way or when running a call, visualize the dependencies between
  functions or between modules in a shiny app, and more.

Creates participant flow diagrams directly from a dataframe. Representing the flow of participants through each stage of a study, especially in clinical trials, is essential to assess the generalisability and validity of the results. This package provides a set of functions that can be combined with a pipe operator to create all kinds of flowcharts from a data frame in an easy way.