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:David Gerard [aut, cre], Peter Hoff [aut]

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Bug tracker:https://github.com/dcgerard/tensr/issues

On CRAN:

36 exports 5 stars 1.86 score 1 dependencies 4 dependents 49 scripts 192 downloads

Last updated 2 years agofrom:1701d56876. Checks:OK: 7. Indexed: yes.

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