Building patient level networks for prediction of 
        medical outcomes and draw the cluster of network.        
        This package is based on paper Personalized disease 
        networks for understanding and predicting cardiovascular 
        diseases and other complex processes 
        (See Cabrera et al. <http://circ.ahajournals.org/content/134/Suppl_1/A14957>).

Tools to discover, download, and spatially filter open
    geographic data from 'PDOK' (Publieke Dienstverlening Op de Kaart), the
    national geodata platform of the Netherlands. Datasets and their layers
    are searched and loaded as vector simple feature ('sf') objects through
    'OGC' API Features endpoints, with automatic pagination and explicit
    coordinate reference system handling. Loaded layers can be filtered by
    any polygon area, and addresses or place names can be geocoded through
    the 'PDOK' location server. The focus is on vector feature data; raster,
    tile, and coverage services are out of scope. See
    <https://www.pdok.nl/> for more information about the platform and its
    services.

A general framework for constructing partial dependence (i.e.,
        marginal effect) plots from various types machine learning models
        in R.

Create, transform, and summarize custom random
    variables with distribution functions (analogues of 'p*()', 'd*()',
    'q*()', and 'r*()' functions from base R). Two types of distributions
    are supported: "discrete" (random variable has finite number of output
    values) and "continuous" (infinite number of values in the form of
    continuous random variable). Functions for distribution
    transformations and summaries are available. Implemented approaches
    often emphasize approximate and numerical solutions: all distributions
    assume finite support and finite values of density function; some
    methods implemented with simulation techniques.

A collection of tools for approximating the 'PDQ' functions
    (respectively, the cumulative distribution, density, and quantile) of
    probability distributions via classical expansions involving moments and
    cumulants.