Covariance Inference and Decompositions for Tensor Datasets
Overview
A collection of functions for Kronecker structured covariance estimation and testing under the array normal model. For estimation, maximum likelihood and Bayesian equivariant estimation procedures are implemented. For testing, a likelihood ratio testing procedure is available. This package also contains additional functions for manipulating and decomposing tensor data sets. This work was partially supported by NSF grant DMS-1505136. Details of the methods are described in Gerard and Hoff (2015) doi:10.1016/j.jmva.2015.01.020 and Gerard and Hoff (2016) doi:10.1016/j.laa.2016.04.033.
- Added a
NEWS.mdfile to track changes to the package. - Remove LazyData from DESCRIPTION.
- Use "_PACKAGE" in roxygen.
- Fix doi links.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- WARNING12 OK, 1 WARNING2026-06-08
- ERROR12 OK, 1 ERROR2026-06-07
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- OK14 OK2026-03-10
Documentation
- Help pages
- 46 (1 internal)includes a package overview page
- Examples that run
- 100%30 run in a plain check
- Exported functions with examples
- 67%
- Documented parameters
- 100%
- Return-value docs
- 83%
- Help pages citing references
- 50%
Downloads
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Dependencies
Installing tensr adds 1 package (0 with compiled code, 12 KB of source tarballs). Heaviest: assertthat 12 KB.
Code & Tests
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- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- RR 3.3.0 released · 2016-05-03
- 1.0.02016-02-03
- RR 3.2.0 released · 2015-04-16
Package metadata
- First published
- 2016-02-03
- Total releases
- 3 / 10 yrs
- License
- GPL-3 OSI
- Download size
- 381 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("tensr")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1016/j.laa.2016.04.033, 10.1016/j.jmva.2015.01.020.
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