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 + fileLICENSE |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| CRAN checks: | BKTR results |
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