MDS is a statistic tool for reduction of dimensionality, using as input a distance
    matrix of dimensions n × n. When n is large, classical algorithms suffer from
    computational problems and MDS configuration can not be obtained.
    With this package, we address these problems by means of six algorithms, being two of them 
    original proposals:
        - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004).
        - Interpolation MDS proposed by Delicado P. and C. Pachón-García (2021)
        <arXiv:2007.11919> (original proposal).
        - Reduced MDS proposed by Paradis E (2018).
        - Pivot MDS proposed by Brandes U. and C. Pich (2007)
        - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021)
        <arXiv:2007.11919> (original proposal).
        - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006).

Create, store, access, and manipulate massive matrices.
    Matrices are allocated to shared memory and may use memory-mapped
    files.  Packages 'biganalytics', 'bigtabulate', 'synchronicity', and
    'bigalgebra' provide advanced functionality.

A shared resource interface for the bigmemory and synchronicity packages.

A computational toolbox designed for handling missing values in large datasets with the Multiple Imputation by Chained Equations (MICE) by using 'Apache Spark'. The methodology is described in Morvan et al. (2026) <doi:10.48550/arXiv.2601.21613>.

Classes for storing and manipulating arbitrary-precision
    integer vectors and high-precision floating-point vectors. These
    extend the range and precision of the 'integer' and 'double' data
    types found in R. This package utilizes the 'Boost.Multiprecision' C++
    library. It is specifically designed to work well with the 'tidyverse'
    collection of R packages.

Utility functions for easy parallelism in R. Include some reexports
    from other packages, utility functions for splitting and parallelizing over
    blocks, and choosing and setting the number of cores used.