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tensorBF (version 1.0.2)

Bayesian Tensor Factorization

Description

Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.

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Version

Install

install.packages('tensorBF')

Monthly Downloads

148

Version

1.0.2

License

MIT + file LICENSE

Maintainer

Suleiman A Khan

Last Published

October 2nd, 2018

Functions in tensorBF (1.0.2)

reconstructTensorBF

Reconstruct the data based on posterior samples
normFiberCentering

Preprocessing: fiber Centering
normSlabScaling

Preprocessing: Slab Scaling
undoFiberCentering

Postprocessing: Undo fiber Centering
getDefaultOpts

A function for generating a default set of parameters for Bayesian Tensor Factorization methods
undoSlabScaling

Postprocessing: Undo Slab Scaling
tensorBF

Bayesian Factorization of a Tensor
plotTensorBF

Plot Tensor Components
predictTensorBF

Predict Missing Values using the Bayesian tensor factorization model