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.

Tools for pedigree quality control and genomic breed/line
    composition estimation in diploid and polyploid breeding populations.
    'BIGpopA' provides functions to check and correct common pedigree
    errors, assign parentage from SNP genotype data using Mendelian error
    rates, validate parent-offspring trios, and estimate genome-wide
    breed or line composition using quadratic programming. Supports both
    diploid and polyploid species. For more details about the included 'breedTools' 
    functions, see Funkhouser et al. (2017) <doi:10.2527/tas2016.0003>.