Provides an implementation of a rare variant association test that utilizes protein tertiary structure to increase signal and to identify likely causal variants. Performs structure-guided collapsing, which leads to local tests that borrow information from neighboring variants on a protein and that provide association information on a variant-specific level. For details of the implemented method see West, R. M.,  Lu, W., Rotroff, D. M., Kuenemann, M., Chang, S-M., Wagner M. J., Buse, J. B., Motsinger-Reif, A., Fourches, D., and Tzeng, J-Y. (2019) <doi:10.1371/journal.pcbi.1006722>.

Validate data in data frames, 'tibble' objects, 'Spark'
    'DataFrames', and database tables. Validation pipelines can be made using
    easily-readable, consecutive validation steps. Upon execution of the
    validation plan, several reporting options are available. User-defined
    thresholds for failure rates allow for the determination of appropriate
    reporting actions. Many other workflows are available including an
    information management workflow, where the aim is to record, collect, and
    generate useful information on data tables.

Imports Coral Point Count with Excel extensions (CPCe)
    point-count annotations and related exported tables, standardizes labels
    with a user-supplied crosswalk, creates ecological cover summaries, writes
    quality-control overlays, and exports machine-learning-ready point labels,
    image patches, sparse masks, and train/validation/test splits. The package
    is fully local and does not depend on third-party web platforms, user
    accounts, or other closed services. CPCe methods are described by Kohler
    and Gill (2006) "Coral Point Count with Excel extensions (CPCe): A Visual
    Basic program for the determination of coral and substrate coverage using
    random point count methodology" <doi:10.1016/j.cageo.2005.11.009>.

The function pointdensity returns a density count and the temporal average for
    every point in the original list. The dataframe returned includes four
    columns: lat, lon, count, and date_avg. The "lat" column is the original
    latitude data; the "lon" column is the original longitude data; the "count"
    is the density count of the number of points within a radius of
    radius*grid_size (the neighborhood); and the date_avg column includes the
    average date of each point in the neighborhood.