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.
- Version1.1.5
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Languageen-US
- TRES citation info
- Last release10/20/2021
Documentation
Team
Jing Zeng
Wenjing Wang
Show author detailsRolesAuthorXin Zhang
Show author detailsRolesAuthor
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends1 package
- Imports3 packages
- Suggests1 package
- Reverse Imports1 package