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:	0.2.0
Depends:	R (≥ 4.0.0)
Imports:	torch (≥ 0.13.0), R6, R6P, ggplot2, ggmap, data.table
Suggests:	knitr, rmarkdown, R.rsp
Published:	2024-08-18
DOI:	10.32614/CRAN.package.BKTR
Author:	Julien Lanthier [aut, cre, cph], Mengying Lei [aut], Aurélie Labbe [aut], Lijun Sun [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 NEWS
CRAN checks:	BKTR results

Documentation:

Reference manual:	BKTR.pdf
Vignettes:	BKTR Package Presentation (source)

Downloads:

Package source:	BKTR_0.2.0.tar.gz
Windows binaries:	r-devel: BKTR_0.2.0.zip, r-release: BKTR_0.2.0.zip, r-oldrel: BKTR_0.2.0.zip
macOS binaries:	r-release (arm64): BKTR_0.2.0.tgz, r-oldrel (arm64): BKTR_0.2.0.tgz, r-release (x86_64): BKTR_0.2.0.tgz, r-oldrel (x86_64): BKTR_0.2.0.tgz
Old sources:	BKTR archive

Linking:

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