Methods for detecting signals related to (adverse event, medical
    product e.g. drugs, vaccines) pairs, a data generation function for 
    simulating pharmacovigilance datasets, and various utility functions.
    For more details please see Liu A., Mukhopadhyay R., and Markatou M. 
    <doi:10.48550/arXiv.2410.01168>.

Lightweight maps of mammals of the world. These maps are a 
    comprehensive collection of maps aligned with the Mammal Diversity Database
    taxonomy of the American Society of Mammalogists. They are generated at low
    resolution for easy access, consultation and manipulation in shapefile
    format. The package connects to a binary backup hosted in the Digital Ocean
    cloud service and allows individual or batch download of any mammal species
    in the mdd taxonomy by providing the scientific species name.

Correct identification and handling of missing data is one of the most important steps in any analysis. To aid this process, 'mde' provides a very easy to use yet robust framework to quickly get an idea of where the missing data
             lies and therefore find the most appropriate action to take.
             Graham WJ (2009) <doi:10.1146/annurev.psych.58.110405.085530>.

Provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.

A comprehensive collection of linkage methods for agglomerative
  hierarchical clustering on a matrix of proximity data (distances or
  similarities), returning a multifurcated dendrogram or multidendrogram.
  Multidendrograms can group more than two clusters when ties in proximity data
  occur, and therefore they do not depend on the order of the input data.
  Descriptive measures to analyze the resulting dendrogram are additionally
  provided. <doi:10.18637/jss.v114.i02>.

Functions for MultiDimensional Feature Selection (MDFS):
 calculating multidimensional information gains, scoring variables,
 finding important variables, plotting selection results.
 This package includes an optional CUDA implementation that speeds up
 information gain calculation using NVIDIA GPGPUs.
 R. Piliszek et al. (2019) <doi:10.32614/RJ-2019-019>.