bed_fastCovEVs          Computation of the k leading eigenvectors of
                        the covariance matrix directly from a
                        bed+bim+fam file.
bed_fastGrmEVs          Computation of the k leading eigenvectors of
                        the genomic relationship matrix, defined in
                        Yang et al. (2011), directly from a bed+bim+fam
                        file.
bed_fastJaccardEVs      Computation of the k leading eigenvectors of
                        the Jaccard similarity matrix directly from a
                        bed+bim+fam file.. Note that this computation
                        is only approximate and does not necessarily
                        coincide with the result obtained by extracting
                        the k leading eigenvectors of the Jaccard
                        matrix computed with the function
                        'jaccardMatrix'.
bed_fastSMatrixEVs      Computation of the k leading eigenvectors of
                        the s-matrix (the weighted Jaccard similarity
                        matrix) directly from a bed+bim+fam file. Note
                        that in contrast to the parameters of the
                        function 'sMatrix', the choice 'phased=FALSE'
                        cannot be modified for the fast eigenvector
                        computation. Moreover, inverting the minor
                        allele is not possible when reading directly
                        from external files.
covMatrix               C++ implementation to compute the covariance
                        matrix for a (sparse) input matrix. The
                        function is equivalent to the R command 'cov'
                        applied to matrices.
fastCovEVs              Computation of the k leading eigenvectors of
                        the covariance matrix for a (sparse) input
                        matrix.
fastGrmEVs              Computation of the k leading eigenvectors of
                        the genomic relationship matrix, defined in
                        Yang et al. (2011), for a (sparse) input
                        matrix.
fastJaccardEVs          Computation of the k leading eigenvectors of
                        the Jaccard similarity matrix for a (sparse)
                        input matrix. Note that this computation is
                        only approximate and does not necessarily
                        coincide with the result obtained by extracting
                        the k leading eigenvectors of the Jaccard
                        matrix computed with the function
                        'jaccardMatrix'.
fastSMatrixEVs          Computation of the k leading eigenvectors of
                        the s-matrix (the weighted Jaccard similarity
                        matrix) for a (sparse) input matrix. Note that
                        in contrast to the parameters of the function
                        'sMatrix', the choice 'phased=FALSE' cannot be
                        modified for the fast eigenvector computation.
fullscan                A full scan of the input data 'm' using a
                        collection of windows given by the two-column
                        matrix 'windows'. For each window, the data is
                        processed using the function 'matrixFunction'
                        (this could be, e.g., the 'covMatrix'
                        function), then the processed data is
                        summarized using the function 'summaryFunction'
                        (e.g., the largest eigenvector computed with
                        the function 'powerMethod'), and finally the
                        global and local summaries are compared using
                        the function 'comparisonFunction' (e.g., the
                        vector correlation with R's function 'cor').
                        The function returns a two-column matrix which
                        contains per row the global summary statistics
                        (e.g., the correlation between the global and
                        local eigenvectors) and the local summary
                        statistics (e.g., the correlation between the
                        local eigenvectors of the previous and current
                        windows) for each window.
grMatrix                C++ implementation to compute the genomic
                        relationship matrix (grm) for a (sparse) input
                        matrix as defined in Yang et al. (2011).
jaccardMatrix           C++ implementation to compute the Jaccard
                        similarity matrix for a (sparse) input matrix.
makeWindows             Auxiliary function to generate a two-column
                        matrix of windows to be used in the function
                        'fullscan'.
powerMethod             C++ implementation of the power method (von
                        Mises iteration) to compute the largest
                        eigenvector of a dense input matrix.
sMatrix                 C++ implementation to compute the s-matrix (the
                        weighted Jaccard similarity matrix) for a
                        (sparse) input matrix as in the 'Stego'
                        package: https://github.com/dschlauch/stego
selectVariants          Auxiliary function to invert minor alleles and
                        to select those variants/loci exceeding a
                        minimal cutoff value.
testdata                Simulated test data.
