Functions to compute distances between probability measures or any other data object than can be posed in this way, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. For further details about the metrics please refer to Martos et al (2014) <doi:10.3233/IDA-140706>; Martos et al (2018) <doi:10.3390/e20010033>; Hernandez et al (2018, submitted); Martos et al (2018, submitted).

Designed to simplify the process of retrieving datasets from the 'Big Data PE' platform using secure token-based authentication. It provides functions for securely storing, retrieving, and managing tokens associated with specific datasets, as well as fetching and processing data. The data-retrieval engine is provided by the generic 'apifetch' package, which 'BigDataPE' configures for the Big Data PE service.

A framework for 'scalable' statistical computing on large on-disk 
    matrices stored in 'HDF5' files. It provides efficient block-wise 
    implementations of core linear-algebra operations (matrix multiplication, 
    SVD, PCA, and QR decomposition) written in C++ and R, along with building 
    blocks from which higher-level multivariate methods such as canonical 
    correlation analysis can be constructed. These building blocks are designed 
    not only for direct use, but also as foundational components for developing 
    new statistical methods that must operate on datasets too large to fit in 
    memory. The package supports data provided either as 'HDF5' files or 
    standard R objects, and is intended for high-dimensional applications such 
    as 'omics' and precision-medicine research.

Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) <doi:10.1016/j.humimm.2015.12.006>. Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) <doi:10.1007/978-1-61779-842-9_14>.