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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.
| Version: | 0.1.0 |
| Imports: | Rcpp (≥ 1.0.10), glmnet, stats |
| LinkingTo: | Rcpp |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2026-01-12 |
| DOI: | 10.32614/CRAN.package.TensorMCMC |
| Author: | Ritwick Mondal [aut, cre] |
| Maintainer: | Ritwick Mondal <ritwick12 at tamu.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | TensorMCMC results |
| Reference manual: | TensorMCMC.html , TensorMCMC.pdf |
| Vignettes: |
TensorMCMC (source, R code) |
| Package source: | TensorMCMC_0.1.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: TensorMCMC_0.1.0.zip, r-oldrel: not available |
| macOS binaries: | r-release (arm64): TensorMCMC_0.1.0.tgz, r-oldrel (arm64): TensorMCMC_0.1.0.tgz, r-release (x86_64): TensorMCMC_0.1.0.tgz, r-oldrel (x86_64): TensorMCMC_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.