Format dates and times flexibly and to whichever locales
    make sense. Parses dates, times, and date-times in various formats
    (including string-based ISO 8601 constructions). The formatting syntax gives
    the user many options for formatting the date and time output in a precise
    manner. Time zones in the input can be expressed in multiple ways and there
    are many options for formatting time zones in the output as well. Several of
    the provided helper functions allow for automatic generation of locale-aware
    formatting patterns based on date/time skeleton formats and standardized
    date/time formats with varying specificity.

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 using the 'httr2' package.

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, QR decomposition, and canonical correlation analysis) written 
    in C++ and R. 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.