Probability distributions that are sometimes useful in hydrology.

An implementation of a clustering algorithm for functional data based on adaptive density peak detection technique, in which the density is estimated by functional k-nearest neighbor density estimation based on a proposed semi-metric between functions. The proposed functional data clustering algorithm is computationally fast since it does not need iterative process. (Alex Rodriguez and Alessandro Laio (2014) <doi:10.1126/science.1242072>; Xiao-Feng Wang and Yifan Xu (2016) <doi:10.1177/0962280215609948>).

An easy framework to read FDA Adverse Event Reporting System XML/ASCII files <https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-latest-quarterly-data-files>.

Data and functions for the book "Multivariate Statistical 
              Modelling Based on Generalized Linear Models", first edition, by 
              Ludwig Fahrmeir and Gerhard Tutz.  Useful when using the book.

More comfortable interface to work with R data or source files
    in a key-value fashion.

An implementation of the fair data adaptation with quantile
    preservation described in Plecko & Meinshausen (2019) <arXiv:1911.06685>.
    The adaptation procedure uses the specified causal graph to pre-process the
    given training and testing data in such a way to remove the bias caused by
    the protected attribute. The procedure uses tree ensembles for quantile
    regression.

Translates several CSV files with ontological terms and corresponding
    data into RDF triples. These RDF triples are stored in OWL and JSON-LD files,
    facilitating data accessibility, interoperability, and knowledge unification.
    The triples are also visualized in a graph saved as an SVG. The input CSVs
    must be formatted with a template from a public Google Sheet; see README or
    vignette for more information. This is a tool is used by the SDLE Research
    Center at Case Western Reserve University to create and visualize material
    science ontologies, and it includes example ontologies to demonstrate its
    capabilities. This work was supported by the U.S. Department of Energy’s
    Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy
    Technologies Office (SETO) Agreement Numbers E-EE0009353 and DE-EE0009347,
    Department of Energy (National Nuclear Security Administration) under Award
    Number DE-NA0004104 and Contract number B647887, and U.S. National Science
    Foundation Award under Award Number 2133576.