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tensorBF
View on CRAN: Click
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Download and install tensorBF package within the R console
Install from CRAN:
install.packages("tensorBF")
Install from Github:
library("remotes")
install_github("cran/tensorBF") Install by package version:
library("remotes")
install_version("tensorBF", "1.0.2") Attach the package and use:
library("tensorBF")
Maintained by
Suleiman A Khan
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First Published: 2016-12-29
Latest Update: 2018-10-02
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.
How to cite:
Suleiman A Khan (2016). tensorBF: Bayesian Tensor Factorization. R package version 1.0.2, https://cran.r-project.org/web/packages/tensorBF. Accessed 04 Jun. 2026.
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Complete documentation for tensorBF
Functions, R codes and Examples using
the tensorBF R package
Some associated functions: getDefaultOpts . normFiberCentering . normSlabScaling . plotTensorBF . predictTensorBF . reconstructTensorBF . tensorBF . undoFiberCentering . undoSlabScaling .
Some associated R codes: noiseProportion.R . normalize.R . plot.R . reconstruct.R . tensorBF.R . Full tensorBF package functions and examples
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