Package: MultiwayRegression 1.2

Eric F. Lock

MultiwayRegression: Perform Tensor-on-Tensor Regression

Functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) <doi:10.1080/10618600.2017.1401544>]. Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model.

Authors:Eric F. Lock

MultiwayRegression_1.2.tar.gz
MultiwayRegression_1.2.tar.gz(r-4.5-noble)MultiwayRegression_1.2.tar.gz(r-4.4-noble)
MultiwayRegression_1.2.tgz(r-4.4-emscripten)MultiwayRegression_1.2.tgz(r-4.3-emscripten)
MultiwayRegression.pdf |MultiwayRegression.html✨
MultiwayRegression/json (API)

# Install 'MultiwayRegression' in R:
install.packages('MultiwayRegression', repos = 'https://cloud.r-project.org')
Datasets:
  • X - Simulated multi-way data for prediction
  • Y - Simulated multi-way data for prediction

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 1 packages 172 downloads 3 exports 1 dependencies

Last updated 6 years agofrom:60ff7e7928. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 15 2025
R-4.5-linuxOKMar 15 2025
R-4.4-linuxOKMar 15 2025

Exports:ctprodrrrrrrBayes

Dependencies:MASS

Citation

To cite package ‘MultiwayRegression’ in publications use:

Lock EF (2019). MultiwayRegression: Perform Tensor-on-Tensor Regression. R package version 1.2, https://CRAN.R-project.org/package=MultiwayRegression.

ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.

Corresponding BibTeX entry: 

  @Manual{,
    title = {MultiwayRegression: Perform Tensor-on-Tensor Regression},
    author = {Eric F. Lock},
    year = {2019},
    note = {R package version 1.2},
    url = {https://CRAN.R-project.org/package=MultiwayRegression},
  }