@article{JSSv099i12, title={TRES: An R Package for Tensor Regression and Envelope Algorithms}, volume={99}, url={https://www.jstatsoft.org/index.php/jss/article/view/v099i12}, doi={10.18637/jss.v099.i12}, abstract={Recently, there has been a growing interest in tensor data analysis, where tensor regression is the cornerstone of statistical modeling for tensor data. The R package TRES provides the standard least squares estimators and the more efficient envelope estimators for the tensor response regression (TRR) and the tensor predictor regression (TPR) models. Envelope methodology provides a relatively new class of dimension reduction techniques that jointly models the regression mean and covariance parameters. Three types of widely applicable envelope estimation algorithms are implemented and applied to both TRR and TPR models.}, number={12}, journal={Journal of Statistical Software}, author={Zeng, Jing and Wang, Wenjing and Zhang, Xin}, year={2021}, pages={1–31} }