rTensor: Tools for Tensor Analysis and Decomposition
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
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, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some
auxiliary functions include the Khatri-Rao product, Kronecker product, and
the Hadamard product for a list of matrices.
Documentation:
Downloads:
Reverse dependencies:
| Reverse imports: |
ccTensor, dcTensor, DelayedTensor, fase, gcTensor, iTensor, LTAR, mwTensor, nnTensor, parafac4microbiome, rMultiNet, rTensor2, RTFA, scITD, scTensor, SmoothTensor, TDbasedUFE, TDbasedUFEadv, TensorClustering, tensorMiss, TensorPreAve, tensorTS, Tlasso, TransGraph, TransTGGM, TRES, WormTensor |
| Reverse suggests: |
oddnet |
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