Reads/write binary genotype file compatible with 'PLINK' <https://www.cog-genomics.org/plink/1.9/input#bed> into/from a R matrix; traverse genotype data one windows of variants at a time, like apply() or a for loop; reads/writes genotype relatedness/kinship matrices created by 'PLINK' <https://www.cog-genomics.org/plink/1.9/distance#make_rel> or 'GCTA' <https://cnsgenomics.com/software/gcta/#MakingaGRM> into/from a R square matrix. It is best used for bringing data produced by 'PLINK' and 'GCTA' into R workflow.

Genotyping arrays enable the direct measurement of an individuals
    genotype at thousands of markers. 'plinkQC' facilitates genotype quality
    control for genetic association studies as described by Anderson and
    colleagues (2010) <doi:10.1038/nprot.2010.116>. It makes 'PLINK' basic
    statistics (e.g. missing genotyping rates per individual, allele frequencies
    per genetic marker) and relationship functions accessible from 'R' and
    generates a per-individual and per-marker quality control report.
    Individuals and markers that fail the quality control can subsequently be
    removed to generate a new, clean dataset. Removal of individuals based on
    relationship status is optimised to retain as many individuals as possible
    in the study. Additionally, there is a trained classifier to predict 
    genomic ancestry of human samples.

A multiple testing procedure for testing several groups of 
        hypotheses is implemented. Linear dependency among the hypotheses 
        within the same group is modeled by using hidden Markov Models. 
        It is noted that a smaller p value does not necessarily imply 
        more significance due to the dependency. A typical application is 
        to analyze genome wide association studies datasets, where SNPs 
        from the same chromosome are treated as a group and exhibit 
        strong linear genomic dependency. See Wei Z, Sun W, Wang K, 
        Hakonarson H (2009) <doi:10.1093/bioinformatics/btp476> for more details.