TRES: Tensor Regression with Envelope Structure and Three Generic Envelope Estimation Approaches

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>.

Version: 1.1.0
Depends: R (≥ 3.5.0), ManifoldOptim
Imports: MASS, methods, pracma, rTensor, stats
Suggests: testthat
Published: 2019-11-17
Author: Wenjing Wang [aut], Jing Zeng [aut, cre], Xin Zhang [aut]
Maintainer: Jing Zeng <jing.zeng at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: TRES results


Reference manual: TRES.pdf
Package source: TRES_1.1.0.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: TRES_1.1.0.tgz, r-oldrel: TRES_1.1.0.tgz
Old sources: TRES archive


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