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>.

Provides Partial least squares Regression and various regular, sparse 
    or kernel, techniques for fitting Cox models for big data. Provides a Partial 
    Least Squares (PLS) algorithm adapted to Cox proportional hazards models that 
    works with 'bigmemory' matrices without loading the entire dataset in memory. 
    Also implements a gradient-descent based solver for Cox proportional hazards 
    models that works directly on 'bigmemory' matrices.     
    Bertrand and Maumy (2023) <https://hal.science/hal-05352069>, and 
    <https://hal.science/hal-05352061> highlighted 
    fitting and cross-validating PLS-based Cox models to censored big data.