Dynamic ICAR Spatiotemporal Factor Models
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-05-12
- ERROR12 OK, 1 ERROR2026-05-05
- OK12 OK2026-04-25
Show 3 earlier changesHide 3 earlier changes
- NOTE12 OK, 2 NOTE2026-04-22
- ERROR11 OK, 2 NOTE, 1 ERROR2026-04-18
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 18includes a package overview page
- Examples that run
- no examples
- Exported functions with examples
- 0%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 6%
- DIFM vignette HTML
Downloads
Dependencies
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 1.0.1Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.02024-04-12
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2024-04-12
- Total releases
- 2 / 2 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 27 KB / 3 files
- Download size
- 335 KB
Cite
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Run in R for the authors' preferred citation:
citation("DIFM")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
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