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TRES  

Tensor Regression with Envelope Structure
View on CRAN: Click here


Download and install TRES package within the R console
Install from CRAN:
install.packages("TRES")

Install from Github:
library("remotes")
install_github("cran/TRES")

Install by package version:
library("remotes")
install_version("TRES", "1.1.5")



Attach the package and use:
library("TRES")
Maintained by
Jing Zeng
[Scholar Profile | Author Map]
First Published: 2018-11-26
Latest Update: 2021-10-20
Description:
Provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive. For details of TRR, see Li L, Zhang X (2017) <doi:10.1080/01621459.2016.1193022>. For details of TPR, see Zhang X, Li L (2017) <doi:10.1080/00401706.2016.1272495>. For details of 1D algorithm, see Cook RD, Zhang X (2016) <doi:10.1080/10618600.2015.1029577>. For details of ECD algorithm, see Cook RD, Zhang X (2018) <doi:10.5705/ss.202016.0037>. For more details of the package, see Zeng J, Wang W, Zhang X (2021) <doi:10.18637/jss.v099.i12>.
How to cite:
Jing Zeng (2018). TRES: Tensor Regression with Envelope Structure. R package version 1.1.5, https://cran.r-project.org/web/packages/TRES. Accessed 07 May. 2025.
Previous versions and publish date:
0.1.0 (2018-11-26 21:20), 1.0.0 (2019-10-22 10:20), 1.1.0 (2019-11-17 15:20), 1.1.1 (2020-02-05 10:50), 1.1.2 (2020-07-23 23:00), 1.1.3 (2020-10-16 08:20), 1.1.4 (2021-06-25 09:50)
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Complete documentation for TRES
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