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>.
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