Useful tools for working with HTTP organised by HTTP verbs
    (GET(), POST(), etc). Configuration functions make it easy to control
    additional request components (authenticate(), add_headers() and so
    on).

Tools for creating and modifying HTTP requests, then
    performing them and processing the results. 'httr2' is a modern
    re-imagining of 'httr' that uses a pipe-based interface and solves
    more of the problems that API wrapping packages face.

Collection of functions to help retrieving data from
    'Hub'Eau' the free and public French National APIs on water
    <https://hubeau.eaufrance.fr/>.

Functions for combining model outputs (e.g. predictions or
    estimates) from multiple models into an aggregated ensemble model
    output.

Core set of low-level utilities common across the 
  'hubverse'. Used to interact with 'hubverse' schema, Hub configuration files 
  and model outputs and designed to be primarily used internally by other 
  'hubverse' packages. See Reich et al. (2022) <doi:10.2105/AJPH.2022.306831> 
  for an overview of Collaborative Hubs.

Provides functions to access data from the US Department of Housing and Urban Development <https://www.huduser.gov/portal/dataset/fmr-api.html>.

Creating effective colour palettes for figures is 
    challenging. This package generates and plot palettes of optimally 
    distinct colours in perceptually uniform colour space, based on 
    'iwanthue' <http://tools.medialab.sciences-po.fr/iwanthue/>. 
    This is done through k-means clustering of CIE Lab colour space, 
    according to user-selected constraints on hue, chroma, and 
    lightness.

An open-source R package to deploys reproducible and flexible labels using layers. 
  The 'huito' package is part of the 'inkaverse' project for developing different procedures and
  tools used in plant science and experimental designs. 
  Learn more about the 'inkaverse' project at <https://inkaverse.com/>.

Tools for computing HUM (Hypervolume Under the Manifold) value to estimate features ability
             to discriminate the class labels, visualizing the ROC curve for two or three class labels
             (Natalia Novoselova, Cristina Della Beffa, Junxi Wang, Jialiang Li, Frank Pessler, Frank Klawonn
             (2014) <doi:10.1093/bioinformatics/btu086>).