EQL, FNN, gcmr, lattice, latticeExtra, mvtnorm, Matrix, MASS,
numDeriv, scatterplot3d, snowfall, sp

EQL, FNN, lattice, latticeExtra, mvtnorm, Matrix, MASS,
numDeriv, scatterplot3d, snowfall, sp

c(person("Koki", "Tsuyuzaki", role = c("aut", "cre"),
email = "k.t.the-answer@hotmail.co.jp"))
Multiple matrices/tensors can be specified and decomposed simultaneously by Probabilistic Latent Tensor Factorisation (PLTF). See the reference section of GitHub README.md <https://github.com/rikenbit/gcTensor>, for details of the method.

GPU/CPU Benchmarking in Debian-based systems
GPU/CPU Benchmarking for Debian-package based systems This
package benchmarks performance of a few standard linear algebra
operations (such as a matrix product and QR, SVD and LU
decompositions) across a number of different BLAS libraries as
well as a GPU implementation.

To do so, it takes advantage of the ability to 'plug and play'
different BLAS implementations easily on a Debian and/or Ubuntu
system.  The current version supports - reference blas
(refblas) which are unaccelerated as a baseline - Atlas which
are tuned but typically configure single-threaded - Atlas39
which are tuned and configured for multi-threaded mode - Goto
Blas which are accelerated and multithreaded - Intel MKL which
are a commercial accelerated and multithreaded version. As for
GPU computing, we use the CRAN package - gputools

For Goto Blas, the gotoblas2-helper script from the ISM in Tokyo can be
used. For Intel MKL we use the Revolution R packages from
Ubuntu 9.10.
R (>= 2.11.1), RSQLite, plyr, reshape, lattice
Debian or Ubuntu system with access to Goto Blas,
Intel MKL, Atlas development build as well as a Nvidia GPU with
CUDA support

GPU/CPU Benchmarking on Debian-package based systems This
package benchmarks performance of a few standard linear algebra
operations (such as a matrix product and QR, SVD and LU
decompositions) across a number of different BLAS libraries as
well as a GPU implementation.

To do so, it takes advantage of the ability to 'plug and play'
different BLAS implementations easily on a Debian and/or Ubuntu
system.  The current version supports - reference blas
(refblas) which are unaccelerated as a baseline - Atlas which
are tuned but typically configure single-threaded - Atlas39
which are tuned and configured for multi-threaded mode - Goto
Blas which are accelerated and multithreaded - Intel MKL which
are a commercial accelerated and multithreaded version. As for
GPU computing, we use the CRAN package - gputools

For Goto Blas, the gotoblas2-helper script from the ISM in Tokyo can be
used. For Intel MKL we use the Revolution R packages from
Ubuntu 9.10.