Provides R bindings to decode DAG-CBOR (Directed Acyclic Graph
    Concise Binary Object Representation) encoded data, CIDs (Content Identifiers),
    and CAR (Content Addressable aRchive) files using the 'Rust' 'IPLD'
    (InterPlanetary Linked Data) library <https://github.com/ipld/libipld>.
    This is especially useful for working with data from 'IPFS'
    (InterPlanetary File System) and 'AtProto' (Bluesky) applications.

A wrapper around the LIBLINEAR C/C++ library for machine
        learning (available at
        <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is
        a simple library for solving large-scale regularized linear
        classification and regression. It currently supports
        L2-regularized classification (such as logistic regression,
        L2-loss linear SVM and L1-loss linear SVM) as well as
        L1-regularized classification (such as L2-loss linear SVM and
        logistic regression) and L2-regularized support vector
        regression (with L1- or L2-loss). The main features of
        LiblineaR include multi-class classification (one-vs-the rest,
        and Crammer & Singer method), cross validation for model
        selection, probability estimates (logistic regression only) or
        weights for unbalanced data. The estimation of the models is
        particularly fast as compared to other libraries.

Provides the 'OpenEXR' static library and 'C++' headers 
    for high-dynamic-range image I/O  (see <https://openexr.com/>) 
    needed to link R packages against the 'OpenEXR' library, along 
    with a basic R interface to load 'EXR' images.

Contains a set of functions to create data libraries,
    generate data dictionaries, and simulate a data step.
    The libname() function will load a directory of data into 
    a library in one line of code.  The dictionary() function
    will generate data dictionaries for individual
    data frames or an entire library.  And the datestep() function
    will perform row-by-row data processing.