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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 |
Reference manual: | BKTR.pdf |
Vignettes: |
BKTR Package Presentation (source) |
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 |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.