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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 
[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 |
| CRAN checks: |
BKTR results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=BKTR
to link to this page.
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.
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