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:
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tensr.pdf |tensr.html✨
tensr/json (API)
NEWS
# 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 2 years agofrom:1701d56876. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 23 2024 |
R-4.5-win | OK | Oct 23 2024 |
R-4.5-linux | OK | Oct 23 2024 |
R-4.4-win | OK | Oct 23 2024 |
R-4.4-mac | OK | Oct 23 2024 |
R-4.3-win | OK | Oct 23 2024 |
R-4.3-mac | OK | Oct 23 2024 |
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
Equivariant Estimation of Kronecker Structured Covariance Matrices
Rendered fromequivariant_estimation.Rmd
usingknitr::rmarkdown
on Oct 23 2024.Last update: 2018-08-15
Started: 2016-01-19
Likelihood Inference in Kronecker Structured Covariance Models
Rendered frommaximum_likelihood.Rmd
usingknitr::rmarkdown
on Oct 23 2024.Last update: 2018-08-15
Started: 2016-01-19