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tensr
View on CRAN: Click
here
Download and install tensr package within the R console
Install from CRAN:
install.packages("tensr")
Install from Github:
library("remotes")
install_github("cran/tensr") Install by package version:
library("remotes")
install_version("tensr", "1.0.2") Attach the package and use:
library("tensr")
Maintained by
David Gerard
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-02-03
Latest Update: 2025-07-24
Description:
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>.
How to cite:
David Gerard (2016). tensr: Covariance Inference and Decompositions for Tensor Datasets. R package version 1.0.2, https://cran.r-project.org/web/packages/tensr. Accessed 05 Mar. 2026.
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Complete documentation for tensr
Functions, R codes and Examples using
the tensr R package
Some associated functions: Kom . amprod . anorm_cd . arrIndices . array_bic_aic . atrans . collapse_mode . convert_cov . demean_tensor . equi_mcmc . fnorm . get_equi_bayes . get_isvd . holq . hooi . hosvd . ihop . kendalltau . ldan . listprod . lq . lrt_null_dist_dim_same . lrt_stat . mat . mhalf . mle_from_holq . multi_stein_loss . multi_stein_loss_cov . multiway_takemura . polar . qr2 . random_ortho . rmirror_wishart . rmvnorm . rsan . rwish . sample_right_wishart . sample_sig . start_ident . start_resids . tensr . topK . tr . trim . tsum . zscores .
Some associated R codes: basic_tensor.R . equi_mcmc.R . hoff_functions.R . like_inference.R . loss_functions.R . post_equi_mcmc.R . starting_values.R . tensor_decompositions.R . tensr.R . Full tensr package functions and examples
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