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.

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.

Functions developed within Breeding Insight to analyze
    diploid and polyploid breeding and genetic data. 'BIGr' provides the
    ability to filter variant call format (VCF) files, extract single nucleotide polymorphisms (SNPs) 
    from diversity arrays technology missing allele discovery count (DArT MADC) files, 
    and manipulate genotype data for both diploid and polyploid species. It
    also serves as the core dependency for the 'BIGapp' 'Shiny' app, which
    provides a user-friendly interface for performing routine genotype
    analysis tasks such as dosage calling, filtering, principal component analysis (PCA), 
    genome-wide association studies (GWAS), and
    genomic prediction. For more details about the included 'breedTools' 
    functions, see Funkhouser et al. (2017) <doi:10.2527/tas2016.0003>, and
    the 'updog' output format, see Gerard et al. (2018) <doi:10.1534/genetics.118.301468>.