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.

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.

High performance principal component analysis routines
       that operate directly on 'bigmemory::big.matrix' objects. The
       package avoids materialising large matrices in memory by
       streaming data through 'BLAS' and 'LAPACK' kernels and provides
       helpers to derive scores, loadings, correlations, and
       contribution diagnostics, including utilities that stream
       results into 'bigmemory'-backed matrices for file-based
       workflows. Additional interfaces expose 'scalable' singular value
       decomposition, robust PCA, and robust SVD algorithms so that
       users can explore large matrices while tempering the influence
       of outliers. 'Scalable' principal component analysis is also implemented,
       Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015) 
       <doi:10.1145/2723372.2751520>.