Functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level.

Access and manage the application programming interface (API) of the Armed Conflict Location & Event Data Project (ACLED) at <https://acleddata.com/>. The package makes it easy to retrieve a user-defined sample (or all of the available data) of ACLED, enabling a seamless integration of regular data updates into the research work flow. It requires a minimal number of dependencies. See the package's README file for a note on replicability when drawing on ACLED data. When using this package, you acknowledge that you have read ACLED's terms and conditions of use, and that you agree with their attribution requirements.

Tools working with data from ACLED (Armed Conflict Location and Event Data). Functions include simplified access to ACLED's API (<https://apidocs.acleddata.com/>), methods for keeping local versions of ACLED data up-to-date, and functions for common ACLED data transformations.

Provides a function to calibrate variant effect scores against
    evidence strength categories defined by the American College of Medical
    Genetics and Genomics (ACMG) and the Association for Molecular Pathology
    (AMP) guidelines. The method computes likelihood ratios of pathogenicity
    via kernel density estimation of pathogenic and benign score distributions,
    and derives score intervals corresponding to ACMG/AMP evidence levels. This
    enables researchers and clinical geneticists to interpret functional and
    computational variant scores in a reproducible and standardised manner. For
    details, see Badonyi and Marsh (2025) <doi:10.1093/bioinformatics/btaf503>.