| DIFM-package {DIFM} | R Documentation |
Dynamic ICAR Spatiotemporal Factor Models
Description
Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.
Details
Package: BCFM2
Type: Package
Version: 1.0
Date: 2023-02-20
License: GPL(>=2)
Author(s)
Hwasoo Shin [aut, cre], Marco Ferreira [aut]
Maintainer: Hwasoo Shin <shwasoo@vt.edu>
References
Shin, H. and Ferreira, M. (2023). "Dynamic ICAR Spatiotemporal Factor Models." Spatial Statistics, 56, 100763
Lopes, H. and West, M. (2004). “Bayesian Model Assessment in Factor Analysis.” Statistica Sinica, 14, 41–67.
Prado, R., Ferreira, M. A. R., and West, M. (2021). Time Series: Modeling, Computation, and Inference. 2nd ed. Boca Raton: CRC Press.