TensorMCMC
Tensor Regression with Stochastic Low-Rank Updates
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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | TensorMCMC_0.1.0.tar.gz |
96.8 KiB |
0.1.0 |
rolling linux/noble R-4.5 | TensorMCMC_0.1.0.tar.gz |
97.3 KiB |
0.1.0 |
rolling source/ R- | TensorMCMC_0.1.0.tar.gz |
37.6 KiB |
0.1.0 |
latest linux/jammy R-4.5 | TensorMCMC_0.1.0.tar.gz |
96.8 KiB |
0.1.0 |
latest linux/noble R-4.5 | TensorMCMC_0.1.0.tar.gz |
97.3 KiB |
0.1.0 |
latest source/ R- | TensorMCMC_0.1.0.tar.gz |
37.6 KiB |
0.1.0 |
2026-04-26 source/ R- | TensorMCMC_0.1.0.tar.gz |
37.6 KiB |
0.1.0 |
2026-04-23 source/ R- | TensorMCMC_0.1.0.tar.gz |
37.6 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | TensorMCMC_0.1.0.zip |
416.3 KiB |