rTensor: Tools for tensor analysis and decomposition

rTensor is a set of tools for creation, manipulation, and modeling of tensors with arbitrary number of modes. A tensor in the context of data analysis is a multidimensional array. rTensor does this by providing a S4 class 'Tensor' that wraps around the base 'array' class. rTensor also provides common tensor operations as methods, including matrix unfolding, summing/averaging across modes, calculating the Frobenius norm, and taking the inner product between two tensors. Familiar array operations are overloaded, such as index subsetting via '[' and element-wise operations. rTensor also implements various tensor decomposition, including CP, GLRAM, MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements transpose, product, and SVD, as defined in Kilmer et al. (2013). Some auxiliary functions include the Khatri-Rao product, Kronecker product, and the Hamadard product for a list of matrices. Development of rTensor has been generously supported by Cornell's Department of Statistical Science.

Version: 1.1
Depends: methods
Published: 2014-04-08
Author: James Li and Jacob Bien and Martin Wells
Maintainer: James Li <jamesyili at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://jamesyili.github.io/rTensor
NeedsCompilation: no
Materials: NEWS
CRAN checks: rTensor results

Downloads:

Reference manual: rTensor.pdf
Package source: rTensor_1.1.tar.gz
Windows binaries: r-devel: rTensor_1.1.zip, r-release: rTensor_1.1.zip, r-oldrel: rTensor_1.1.zip
OS X Snow Leopard binaries: r-release: rTensor_1.1.tgz, r-oldrel: rTensor_1.1.tgz
OS X Mavericks binaries: r-release: rTensor_1.1.tgz
Old sources: rTensor archive