tensr: Covariance Inference and Decompositions for Tensor Datasets

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>.

Version:	1.0.1
Imports:	assertthat
Suggests:	knitr, rmarkdown, covr, testthat
Published:	2018-08-15
DOI:	10.32614/CRAN.package.tensr
Author:	David Gerard [aut, cre], Peter Hoff [aut]
Maintainer:	David Gerard <gerard.1787 at gmail.com>
BugReports:	http://github.com/dcgerard/tensr/issues
License:	GPL-3
NeedsCompilation:	no
Citation:	tensr citation info
Materials:	README
CRAN checks:	tensr results

Documentation:

Reference manual:	tensr.pdf
Vignettes:	Equivariant Estimation Likelihood Inference

Downloads:

Package source:	tensr_1.0.1.tar.gz
Windows binaries:	r-devel: tensr_1.0.1.zip, r-release: tensr_1.0.1.zip, r-oldrel: tensr_1.0.1.zip
macOS binaries:	r-release (arm64): tensr_1.0.1.tgz, r-oldrel (arm64): tensr_1.0.1.tgz, r-release (x86_64): tensr_1.0.1.tgz, r-oldrel (x86_64): tensr_1.0.1.tgz
Old sources:	tensr archive

Reverse dependencies:

Reverse imports:

catch, hwep, TensorClustering, TULIP

Linking:

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