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TensorMCMC  

Tensor Regression with Stochastic Low-Rank Updates
View on CRAN: Click here


Download and install TensorMCMC package within the R console
Install from CRAN:
install.packages("TensorMCMC")

Install from Github:
library("remotes")
install_github("cran/TensorMCMC")

Install by package version:
library("remotes")
install_version("TensorMCMC", "0.1.0")



Attach the package and use:
library("TensorMCMC")
Maintained by
Ritwick Mondal
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-01-12
Latest Update: 2026-01-12
Description:
Provides methods for low-rank tensor regression with tensor-valued predictors and scalar covariates. Model estimation is performed using stochastic optimization with random-walk updates for low-rank factor matrices. Computationally intensive components for coefficient estimation and prediction are implemented in C++ via 'Rcpp'. The package also includes tools for cross-validation and prediction error assessment.
How to cite:
Ritwick Mondal (2026). TensorMCMC: Tensor Regression with Stochastic Low-Rank Updates. R package version 0.1.0, https://cran.r-project.org/web/packages/TensorMCMC. Accessed 18 Sep. 2026.
Previous versions and publish date:
No previous versions
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Complete documentation for TensorMCMC
Functions, R codes and Examples using the TensorMCMC R package
Full TensorMCMC package functions and examples
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