Package: TensorMCMC
Type: Package
Title: Tensor Regression with Stochastic Low-Rank Updates
Version: 0.1.0
Date: 2026-01-08
Authors@R: person("Ritwick", "Mondal", email = "ritwick12@tamu.edu", role = c("aut", "cre"))
Maintainer: Ritwick Mondal <ritwick12@tamu.edu>
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.
Imports: Rcpp (>= 1.0.10), glmnet, stats
LinkingTo: Rcpp
Encoding: UTF-8
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
Config/testthat/edition: 3
RoxygenNote: 7.3.3
License: MIT + file LICENSE
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-07-11 06:30:58 UTC; root
Author: Ritwick Mondal [aut, cre]
Repository: https://cran.r-universe.dev
Date/Publication: 2026-01-12 19:30:06 UTC
RemoteUrl: https://github.com/cran/TensorMCMC
RemoteRef: HEAD
RemoteSha: 03dae74c2a43f05a503817baa30108b387e2ff34