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: 2026-01-08 18:55:16 UTC; User
Author: Ritwick Mondal [aut, cre]
Repository: CRAN
Date/Publication: 2026-01-12 19:30:06 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-28 12:17:40 UTC; unix
Archs: TensorMCMC.so.dSYM
