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.

Provides a general framework for
        high-dimensional undirected graph estimation. It integrates
        data preprocessing, neighborhood screening, graph estimation,
        and model selection techniques into a pipeline. In
        preprocessing stage, the nonparanormal(npn) transformation is
        applied to help relax the normality assumption. In the graph
        estimation stage, the graph structure is estimated by
        Meinshausen-Buhlmann graph estimation or the graphical lasso,
        and both methods can be further accelerated by the lossy
        screening rule preselecting the neighborhood of each variable
        by correlation thresholding. We target on high-dimensional data
        analysis usually d >> n, and the computation is
        memory-optimized using the sparse matrix output. We also
        provide a computationally efficient approach, correlation
        thresholding graph estimation. Three
        regularization/thresholding parameter selection methods are
        included in this package: (1)stability approach for
        regularization selection (2) rotation information criterion (3)
        extended Bayesian information criterion which is only available
        for the graphical lasso.