The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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.
| Version: | 1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp (≥ 1.0.10), Matrix, LaplacesDemon, spdep, gridExtra, sp |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2024-04-12 |
| Author: | Hwasoo Shin [aut, cre], Marco Ferreira [aut] |
| Maintainer: | Hwasoo Shin <shwasoo at vt.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | DIFM results |
| Reference manual: | DIFM.pdf |
| Vignettes: |
DIFM vignette |
| Package source: | DIFM_1.0.tar.gz |
| Windows binaries: | r-devel: DIFM_1.0.zip, r-release: DIFM_1.0.zip, r-oldrel: DIFM_1.0.zip |
| macOS binaries: | r-release (arm64): DIFM_1.0.tgz, r-oldrel (arm64): DIFM_1.0.tgz, r-release (x86_64): DIFM_1.0.tgz, r-oldrel (x86_64): DIFM_1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=DIFM 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.
Health stats visible at Monitor.