gputools: GPU Drop-Ins for Statistics on Wide Data

GPU versions of base R statistics for data with many variables, using the NVIDIA 'CUDA' toolkit: correlation matrices (Pearson, Spearman and Kendall) and their tests, the strongest correlated pairs among tens of thousands of variables without forming the matrix, association scans with covariates as in expression quantitative trait loci studies (Shabalin 2012 <doi:10.1093/bioinformatics/bts163>), permutation tests with Westfall-Young maxT adjustment, distance matrices, hierarchical and k-means clustering, Granger causality tests, B-spline mutual information, principal components, and least-squares fits. Results agree with the base R functions they replace. Without 'CUDA' the package installs, and its functions explain how to get a 'CUDA' build. Described in Buckner et al. (2010) <doi:10.1093/bioinformatics/btp608>.

install.packages("gputools")

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