Package: tensr 1.0.2

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>.
Authors:
tensr_1.0.2.tar.gz
tensr_1.0.2.zip(r-4.7)tensr_1.0.2.zip(r-4.6)tensr_1.0.2.zip(r-4.5)
tensr_1.0.2.tgz(r-4.6-any)tensr_1.0.2.tgz(r-4.5-any)
tensr_1.0.2.tar.gz(r-4.7-any)tensr_1.0.2.tar.gz(r-4.6-any)
tensr_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tensr/json (API)
| # Install 'tensr' in R: |
| install.packages('tensr', repos = c('https://dcgerard.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/dcgerard/tensr/issues
Last updated from:5dec41f93d. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 127 | ||
| source / vignettes | OK | 207 | ||
| linux-release-x86_64 | OK | 117 | ||
| macos-release-arm64 | OK | 94 | ||
| macos-oldrel-arm64 | OK | 73 | ||
| windows-devel | OK | 84 | ||
| windows-release | OK | 71 | ||
| windows-oldrel | OK | 77 | ||
| wasm-release | OK | 98 |
Exports:amprodanorm_cdarray_bic_aicarrIndicesatranscollapse_modeconvert_covdemean_tensorequi_mcmcfnormget_equi_bayesget_isvdholqhooihosvdihopKomldanlistprodlqlrt_null_dist_dim_samelrt_statmatmhalfmle_from_holqmulti_stein_lossmulti_stein_loss_covmultiway_takemurapolarrandom_orthormirror_wishartstart_identstart_residstrtrimtsum
Dependencies:assertthat
Last update: 2025-07-23
Started: 2016-01-19
Last update: 2025-07-23
Started: 2016-01-19
