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>.

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.