Bayesian Kernelized Tensor Regression
BIXI Data Class
R6 class encapsulating the BKTR regression elements
Kernel Composition Operations
Base R6 class for Kernels
R6 class for Kernels Composed via Addition
R6 class for Composed Kernels
R6 class for Matern Kernels
R6 class for Kernels Composed via Multiplication
R6 class for kernel's hyperparameter
R6 class for Periodic Kernels
R6 class for Rational Quadratic Kernels
R6 class for Square Exponential Kernels
R6 class for White Noise Kernels
Plot Beta Coefficients Distribution
Plot Beta Coefficients Distribution Regrouped by Covariates
Plot Hyperparameters Distributions
Plot Hyperparameters Traceplot
Plot Spatial Beta Coefficients
Plot Temporal Beta Coefficients
Plot Y Estimates
Operator overloading for kernel addition
Print the summary of a BKTRRegressor instance
Function used to transform covariates coming from two dataframes one f...
Simulate Spatiotemporal Data Using Kernel Covariances.
Summarize a BKTRRegressor instance
R6 singleton that contains the configuration for the tensor backend
Operator overloading for kernel multiplication
Tensor Operator Singleton
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.