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.

A computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics <doi:10.1002/gepi.22136>. Also provides stochastic singular value decomposition for dense or sparse matrices.

Use Newton's method, coordinate descent, and METIS clustering
    to solve the L1 regularized Gaussian MLE inverse covariance
    matrix estimation problem.

Read large text files by splitting them in smaller files.
    Package 'bigreadr' also provides some convenient wrappers around fread()
    and fwrite() from package 'data.table'.

Allows the user to carry out GLM on very large
    data sets. Data can be created using the data_frame() function and appended
    to the object with object$append(data); data_frame and data_matrix objects
    are available that allow the user to store large data on disk. The data is
    stored as doubles in binary format and any character columns are transformed
    to factors and then stored as numeric (binary) data while a look-up table is
    stored in a separate .meta_data file in the same folder. The data is stored in
    blocks and GLM regression algorithm is modified and carries out a MapReduce-
    like algorithm to fit the model. The functions bglm(), and summary()
    and bglm_predict() are available for creating and post-processing of models.
    The library requires Armadillo installed on your system. It may not 
    function on windows since multi-core processing is done using mclapply() 
    which forks R on Unix/Linux type operating systems.