README.Rmd

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output: github_document
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# tensorr: sparse tensors in R
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`tensorr` provides methods to manipulate and store sparse tensors. Tensors are multi-dimensional generalizations of matrices (two dimensional) and vectors (one dimensional).

It has three main goals:

- Provide an efficient format to store sparse tensors in R.
- Provide standard tensor operations such as multiplication and unfolding.
- Provide standard tensor decomposition techniques such as CP and Tucker.

## Installation
The development version of **tensorr** is available on github.
```{r eval = FALSE}
devtools::install_github("zamorarr/tensorr")
```

## Usage
See the [introduction vignette](https://zamorarr.github.io/tensorr/articles/introduction.html)
for a comprehensive overview. To create a sparse tensor you have to provide the 
non-zero values, subscripts to the non-zero values, and the overall dimensions 
of the tensor.

```{r into-sparse, message = FALSE}
library(tensorr)

subs <- list(c(1,1,1), c(1,1,2))
vals <- c(10, 20)
dims <- c(2,2,2)
x <- sptensor(subs, vals, dims)
x
```

## Tensor References
Many of the dense and sparse implementation ideas were adpated from:

 - B. W. Bader and T. G. Kolda. Algorithm 862: MATLAB tensor classes for fast algorithm prototyping, ACM Transactions on Mathematical Software 32(4):635-653, December 2006.
- B. W. Bader and T. G. Kolda. Efficient MATLAB computations with sparse and factored tensors, SIAM Journal on Scientific Computing 30(1):205-231, December 2007.
- [scikit-tensor](https://github.com/mnick/scikit-tensor)

For a review on tensors, see:

- T. G. Kolda and B. W. Bader, Tensor Decompositions and Applications, SIAM Review 51(3):455-500, September 2009