Tools for constructing and auditing longitudinal decision paths
    from panel data. Implements a decision infrastructure framework for
    representing institutional AI systems as generators of time-ordered binary
    decision sequences. Provides functions to build path objects from panel
    data, summarise per-unit descriptors (dosage, switching rate, onset,
    duration, longest run), compute the Decision Reliability Index (DRI)
    following Cronbach (1951) <doi:10.1007/BF02310555>, estimate Shannon
    decision-path entropy following Shannon (1948)
    <doi:10.1002/j.1538-7305.1948.tb01338.x>, classify systems by
    infrastructure type (static, periodic, continuous, human-in-the-loop),
    and evaluate subgroup disparities in decision exposure and stability.
    Applications include education, policy, health, and organisational research.

Supporting the quantitative analysis of binary welfare based
    decision making processes using Monte Carlo simulations. Decision support
    is given on two levels: (i) The actual decision level is to choose between
    two alternatives under probabilistic uncertainty. This package calculates
    the optimal decision based on maximizing expected welfare. (ii) The meta
    decision level is to allocate resources to reduce the uncertainty in the
    underlying decision problem, i.e to increase the current information to
    improve the actual decision making process. This problem is dealt with
    using the Value of Information Analysis. The Expected Value of
    Information for arbitrary prospective estimates can be calculated as
    well as Individual Expected Value of Perfect Information.
    The probabilistic calculations are done via Monte Carlo
    simulations. This Monte Carlo functionality can be used on its own.

Makes 'deck.gl' <https://deck.gl/>, a WebGL-powered open-source JavaScript framework
  for visual exploratory data analysis of large datasets, available within R via the 'htmlwidgets' package.
  Furthermore, it supports basemaps from 'mapbox' <https://www.mapbox.com/> via
  'mapbox-gl-js' <https://github.com/mapbox/mapbox-gl-js>.