Tensor Regression with Stochastic Low-Rank Updates R package TensorMCMC version 0.1.0 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. Software Imports: Rcpp (>= 1.0.10), glmnet, stats LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Ritwick Mondal <ritwick12@tamu.edu> Comprehensive R Archive Network (CRAN) Ritwick Mondal [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=TensorMCMC/LICENSE) 2026-01-12 application/tgz https://CRAN.R-project.org/package=TensorMCMC doi:10.32614/CRAN.package.TensorMCMC