Homepage of "tensorA",
an R package for tensor algebra
Download tensorA
Disclaimer
The software on this site is distributed under the GNU
Public license as is and comes with absolutely no warranty.
Current version
tensorA_0.2.tar.gz (8 July 2006)
Future versions

The discussion on further necessary functionality is hereby opened.
 Whoever might find himself able to provide a more detailed and
mathematically found explanation of tensors and their operations  maybe including informative
English references  for the help files, is warmly invited for honorable
contribution.

A the thing with tensorial derivatives in missing in the package. However it is
not clear how to implement such a thing in a numerical package, since
derivatives are symbolic concept. I am looking for suggestions.

The package currently only provides basic statistical procedures for
tensors (mean and variance). General noncentered, centered and factorial
moments are planned. Can anybody provide tensorial datasets? What models are
most used?
Previous versions
0.2 is the first release version.
Known Bugs
Currently the dimnames
support is experimentally and not
stable. However in the rest of the package there are no currently known
bugs. So please complain any bug you find to boogaart@unigreifswald.de.
What is "tensorA"?
"tensorA", is a new Rpackage for computations with tensors and datasets of
vectors or matrices, written by K. Gerald van den
Boogaart, Greifswald. It is now available as a
first complete version and can be downloaded from this page.
Simply speaking a tensor is multidimensional array. And seen from this point
of view the package just provides some additional operations of arrays. All
those who want to do computations on arrays that are most naturally expressed
in operations of tensors, vectors and matrices of vectors or matrices will
find this package useful for several reasons:
 The package organizes the dimensions by names, such that there is no
need reordering dimensions or keeping track of the sequence of
dimensions.
 The package supports parallelized computations on datasets of vectors,
matrices and tensors just as R provides for numbers, e.g. inversion, solving
of equation system, decompositions. The resulting computation is typically
substantially faster, transparent, and faster to code than any
sapply
constructs computing the same results.
 The package provides a direct implementation of the calculus of tensor
algebra including co and contravariate basis, Einsteins and Riemanns summing
convention, dragging rule and named indices.
If you have ever used the dim(x)<value
construct to reshape your
matrix for the next %*%
you will be a friend of tensorA.