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:
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