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.

Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.

Easy-to-use, efficient, flexible and scalable statistical tools.
  Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping.
  It provides for instance matrix operations, Principal Component Analysis,
  sparse linear supervised models, utility functions and more
  <doi:10.1093/bioinformatics/bty185>.

Selecting linear and generalized linear models for large data sets
    using modified stepwise procedure and modern selection criteria (like
    modifications of Bayesian Information Criterion). Selection can be 
    performed on data which exceed RAM capacity. Bogdan et al., (2004)
    <doi:10.1534/genetics.103.021683>.