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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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'))

Peer review:

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

On CRAN:

6.48 score 5 stars 4 packages 50 scripts 145 downloads 36 exports 1 dependencies

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

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Doc / VignettesOKDec 22 2024
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R-4.5-linuxOKDec 22 2024
R-4.4-winOKDec 22 2024
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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.Rmdusingknitr::rmarkdownon Dec 22 2024.

Last update: 2018-08-15
Started: 2016-01-19

Likelihood Inference in Kronecker Structured Covariance Models

Rendered frommaximum_likelihood.Rmdusingknitr::rmarkdownon Dec 22 2024.

Last update: 2018-08-15
Started: 2016-01-19

Readme and manuals

Help Manual

Help pageTopics
k-mode product.amprod
Array normal conditional distributions.anorm_cd
Calculate the AIC and BIC.array_bic_aic
Array indices.arrIndices
Tucker product.atrans
Collapse multiple modes into one mode.collapse_mode
Convert the output from 'equi_mcmc' to component covariance matrices.convert_cov
Demeans array data.demean_tensor
Gibbs sampler using an invariant prior.equi_mcmc
Frobenius norm of an array.fnorm
Get the Bayes rule under multiway Stein's loss.get_equi_bayes
Calculate the incredible SVD (ISVD).get_isvd
Calculate the incredible higher-order LQ decomposition (HOLQ).holq
Calculate the higher-order orthogonal iteration (HOOI).hooi
Calculate the (truncated) higher-order SVD (HOSVD).hosvd
The incredible higher-order polar decomposition (IHOP).ihop
Kendall's tau measure of association.kendalltau
Commutation matrix.Kom
Log-likelihood of array normal model.ldan
Element-wise matrix products between two lists.listprod
LQ decomposition.lq
Draw from null distribution of likelihood ratio test statistic.lrt_null_dist_dim_same
Calculate the likelihood ratio test statistic.lrt_stat
Unfold a matrix.mat
The symmetric square root of a positive definite matrix.mhalf
Get MLE from output of 'holq'.mle_from_holq
Calculate multiway Stein's loss from square root matrices.multi_stein_loss
Calculate multiway Stein's loss from component covariance matrices.multi_stein_loss_cov
Calculate a truncated multiway Takemura estimator.multiway_takemura
The left polar decomposition.polar
QR Decomposition.qr2
Generate a list of orthogonal matrices drawn from Haar distribution.random_ortho
Sample from the mirror-Wishart distribution.rmirror_wishart
Multivariate normal simulation.rmvnorm
Standard normal array.rsan
Wishart simulation.rwish
Gibbs update of 'Phi_inv'.sample_right_wishart
Update for total variation parameter in 'equi_mcmc'.sample_sig
Get list of identity matrices.start_ident
Sample covariance matrices for each mode.start_resids
tensr: A package for Kronecker structured covariance inference.tensr
Top K elements of a vector.topK
Trace of a matrix.tr
Truncates small numbers to 0.trim
Tucker sum.tsum
Normal scores.zscores