Tensor Regression with Stochastic Low-Rank Updates
Simple rank comparison via in-sample RMSE
Posterior Mean Using C++
posterior mean for tensor regression
Predict Response Using Tensor Regression C++
Predict tensor regression (wrapper)
Prediction from tensor regression (S3 method)
Inverse-gamma random number generator
root-mean-square error (RMSE)
Tensor Regression using Rcpp
Update Beta Matrices Using C++ Random Walk
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.