The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

TRES: Tensor Regression with Envelope Structure

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>.

Version: 1.1.5
Depends: R (≥ 3.6.0), ManifoldOptim (≥ 1.0.0)
Imports: MASS, methods, pracma (≥ 2.2.5), rTensor (≥ 1.4), stats
Suggests: testthat (≥ 2.1.0)
Published: 2021-10-20
DOI: 10.32614/CRAN.package.TRES
Author: Wenjing Wang [aut], Jing Zeng [aut, cre], Xin Zhang [aut]
Maintainer: Jing Zeng <jing.zeng at stat.fsu.edu>
BugReports: https://github.com/leozeng15/TRES/issues
License: GPL-3
URL: https://github.com/leozeng15/TRES
NeedsCompilation: no
Language: en-US
Citation: TRES citation info
Materials: README NEWS
CRAN checks: TRES results

Documentation:

Reference manual: TRES.pdf

Downloads:

Package source: TRES_1.1.5.tar.gz
Windows binaries: r-devel: TRES_1.1.5.zip, r-release: TRES_1.1.5.zip, r-oldrel: TRES_1.1.5.zip
macOS binaries: r-release (arm64): TRES_1.1.5.tgz, r-oldrel (arm64): TRES_1.1.5.tgz, r-release (x86_64): TRES_1.1.5.tgz, r-oldrel (x86_64): TRES_1.1.5.tgz
Old sources: TRES archive

Reverse dependencies:

Reverse imports: TensorClustering

Linking:

Please use the canonical form https://CRAN.R-project.org/package=TRES to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.