nmfgpu4R: Non-Negative Matrix Factorization (NMF) using CUDA

Wrapper package for the nmfgpu library, which implements several Non-negative Matrix Factorization (NMF) algorithms for CUDA platforms. By using the acceleration of GPGPU computing, the NMF can be used for real-world problems inside the R environment. All CUDA devices starting with Kepler architecture are supported by the library.

Depends: R (≥ 3.1.0)
Imports: Rcpp (≥ 0.11.4), Matrix, SparseM, stats, stringr, tools, utils
LinkingTo: Rcpp
Suggests: gdata
Published: 2016-10-17
Author: Sven Koitka [aut, cre, cph], Christoph M. Friedrich [ctb]
Maintainer: Sven Koitka <sven.koitka at fh-dortmund.de>
BugReports: https://github.com/razorx89/nmfgpu4R/issues
License: GPL-3 | file LICENSE
URL: https://github.com/razorx89/nmfgpu4R
NeedsCompilation: yes
SystemRequirements: CUDA >= v7.0, Nvidia GPU (e.g. GeForce or Tesla) with compute capability >= 3.0 (Kepler)
CRAN checks: nmfgpu4R results


Reference manual: nmfgpu4R.pdf
Package source: nmfgpu4R_0.2.5.2.tar.gz
Windows binaries: r-devel: nmfgpu4R_0.2.5.2.zip, r-release: nmfgpu4R_0.2.5.2.zip, r-oldrel: nmfgpu4R_0.2.5.2.zip
OS X El Capitan binaries: r-release: nmfgpu4R_0.2.5.2.tgz
OS X Mavericks binaries: r-oldrel: nmfgpu4R_0.2.5.2.tgz
Old sources: nmfgpu4R archive


Please use the canonical form https://CRAN.R-project.org/package=nmfgpu4R to link to this page.