Performs cluster analysis using an ensemble clustering
    framework, Chiu & Talhouk (2018) <doi:10.1186/s12859-017-1996-y>.
    Results from a diverse set of algorithms are pooled together using
    methods such as majority voting, K-Modes, LinkCluE, and CSPA. There
    are options to compare cluster assignments across algorithms using
    internal and external indices, visualizations such as heatmaps, and
    significance testing for the existence of clusters.

View 2D/3D sections, contour plots, mesh of excursion sets for computer experiments designs, surrogates or test functions.

Collapse red-green or green-blue distinctions to simulate
        the effects of different types of color-blindness.

This function provides an interface between 'Matlab' and 'R' in facilitating fast processing for reading and saving DICOM images.

Given  an initial set of points, this package minimizes the number of elements to discard from this set such that there exists at least one monotonic and convex mapping within pre-specified upper and lower bounds.

A key-value dictionary data structure based on R6 class which is designed to be similar usages with other languages dictionary (e.g. 'Python') with reference semantics and extendabilities by R6.

Efficient object-oriented R6 dictionary capable of holding objects of any class, including R6. Typed and untyped dictionaries are provided as well as the 'usual' dictionary methods that are available in other OOP languages, for example listing keys, items, values, and methods to get/set these.

An R interface to the 'Free Dictionary API' 
    <https://dictionaryapi.dev/>, 
    <https://github.com/meetDeveloper/freeDictionaryAPI>. Retrieve
    dictionary definitions for English words, as well 
    as additional information including phonetics, part of speech, origins, 
    audio pronunciation, example usage, synonyms and antonyms, returned in 
    'tidy' format for ease of use.

The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group.  Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time.  This package contains tools for computing average treatment effect parameters in Difference in Differences setups with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>.  The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time.  These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these.  There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.