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tensorBF  

Bayesian Tensor Factorization
View on CRAN: Click here


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
[Scholar Profile | Author Map]
All associated links for this package
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.
Previous versions and publish date:
1.0.0 (2016-12-29 14:10), 1.0.1 (2017-01-09 21:46)
Other packages that cited tensorBF R package
View tensorBF citation profile
Other R packages that tensorBF depends, imports, suggests or enhances
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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