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DIFM
View on CRAN: Click
here
Download and install DIFM package within the R console
Install from CRAN:
install.packages("DIFM")
Install from Github:
library("remotes")
install_github("cran/DIFM") Install by package version:
library("remotes")
install_version("DIFM", "1.0.1") Attach the package and use:
library("DIFM")
Maintained by
Hwasoo Shin
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-04-12
Latest Update: 2024-04-12
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). 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.
How to cite:
Hwasoo Shin (2024). DIFM: Dynamic ICAR Spatiotemporal Factor Models. R package version 1.0.1, https://cran.r-project.org/web/packages/DIFM. Accessed 29 Oct. 2026.
Previous versions and publish date:
1.0 (2024-04-12 10:50), (2026-07-09 08:01)
Other packages that cited DIFM R package
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imports, suggests or enhances
Complete documentation for DIFM
Functions, R codes and Examples using
the DIFM R package
Full DIFM package
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