BKTR: Bayesian Kernelized Tensor Regression

Facilitates scalable spatiotemporally varying coefficient modelling with Bayesian kernelized tensor regression. The important features of this package are: (a) Enabling local temporal and spatial modeling of the relationship between the response variable and covariates. (b) Implementing the model described by Lei et al. (2023) <doi:10.48550/arXiv.2109.00046>. (c) Using a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to sample from the posterior distribution of the model parameters. (d) Employing a tensor decomposition to reduce the number of estimated parameters. (e) Accelerating tensor operations and enabling graphics processing unit (GPU) acceleration with the 'torch' package.

Version: 0.1.1
Depends: R (≥ 4.0.0)
Imports: torch, R6, R6P, ggplot2, ggmap, data.table
Suggests: knitr, rmarkdown, R.rsp
Published: 2023-10-20
Author: Julien Lanthier ORCID iD [aut, cre, cph], Mengying Lei ORCID iD [aut], Aurélie Labbe ORCID iD [aut], Lijun Sun ORCID iD [aut]
Maintainer: Julien Lanthier <julien.lanthier at hec.ca>
BugReports: https://github.com/julien-hec/BKTR/issues
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: BKTR results

Documentation:

Reference manual: BKTR.pdf
Vignettes: BKTR Package Presentation

Downloads:

Package source: BKTR_0.1.1.tar.gz
Windows binaries: r-devel: BKTR_0.1.1.zip, r-release: BKTR_0.1.1.zip, r-oldrel: BKTR_0.1.1.zip
macOS binaries: r-release (arm64): BKTR_0.1.1.tgz, r-oldrel (arm64): BKTR_0.1.1.tgz, r-release (x86_64): BKTR_0.1.1.tgz

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