Unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. Dimensionality reduction is performed using a parallelized implementation of the t-Stochastic Neighboring Embedding algorithm (Garriga J. and Bartumeus F. (2018), <arXiv:1812.09869>).

Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. The main functions in the 'bigmatch' package are optcal() to find the optimal caliper, optconstant() to find the optimal number of nearest neighbors, and nfmatch() to find a near-fine balance match with a caliper and a restriction on the number of nearest neighbors. 
    Yu, R., Silber, J. H., and Rosenbaum, P. R. (2020). <DOI:10.1214/19-sts699>.

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.