Evaluating probabilistic forecasts via proper scoring rules.  scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families.  Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score.  For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.

Implements multiple consistent scoring functions
    (Gneiting T (2011) <doi:10.1198/jasa.2011.r10138>) for assessing point
    forecasts and point predictions. Detailed documentation of scoring
    functions' properties is included for facilitating interpretation of
    results.

Dictionary-like reference for computing scoring rules in a wide
    range of situations. Covers both parametric forecast distributions (such as
    mixtures of Gaussians) and distributions generated via simulation. Further 
    details can be found in the package vignettes <doi:10.18637/jss.v090.i12>, 
    <doi:10.18637/jss.v110.i08>.

Facilitate the evaluation of forecasts in a convenient
    framework based on data.table. It allows user to to check their forecasts
    and diagnose issues, to visualise forecasts and missing data, to transform
    data before scoring, to handle missing forecasts, to aggregate scores, and
    to visualise the results of the evaluation. The package mostly focuses on
    the evaluation of probabilistic forecasts and allows evaluating several
    different forecast types and input formats. Find more information about the
    package in the Vignettes as well as in the accompanying paper,
    <doi:10.48550/arXiv.2205.07090>.

A consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. See van der Laan and Robins (1997) <doi:10.2307/2670119>.

Constructs cell-type–specific gene regulatory networks from single-cell RNA-sequencing data. The method implements the SCORPION algorithm, which first aggregates individual cells into super-cells and then applies PANDA (Passing Attributes between Networks for Data Assimilation) to infer transcription factor–target regulatory relationships. It also provides statistical methods for differential edge analysis.