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.

Easily talk to Google's 'BigQuery' database from R.

Easily talk to Google's 'BigQuery Storage' API from R
    (<https://cloud.google.com/bigquery/docs/reference/storage/rpc>).

Simulate multivariate data with arbitrary marginal distributions.
  'bigsimr' is a package for simulating high-dimensional multivariate data with 
  a target correlation and arbitrary marginal distributions via Gaussian copula. 
  It utilizes the Julia package 'Bigsimr.jl' for its core routines.

Easy-to-use, efficient, flexible and scalable tools for analyzing 
    massive SNP arrays. Privé et al. (2018) <doi:10.1093/bioinformatics/bty185>.

Provide a sparse matrix format with data stored on disk, to be
    used in both R and C++. This is intended for more efficient use of sparse 
    data in C++ and also when parallelizing, since data on disk does not need
    copying. Only a limited number of features will be implemented. For now,
    conversion can be performed from a 'dgCMatrix' or a 'dsCMatrix' from R 
    package 'Matrix'. A new compact format is also now available.