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, the graphical lasso,
        or the TIGER (tuning-insensitive graph estimation and
        regression) method, and the first two 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.

Access models and datasets hosted on the 'Hugging Face' Hub
    through its Inference Application Programming Interface (API). Run text
    classification, embeddings, chat, translation, image, audio, and other
    tasks from tidy 'R' workflows without installing 'Python' by default.
    Results are returned as data frames or simple 'R' objects so they can be
    composed with 'dplyr', 'tidyr', and related tooling. Helpers also support
    Hub search, file download, provider discovery, and guarded uploads for
    authenticated workflows.