Package: DIFM
Type: Package
Title: Dynamic ICAR Spatiotemporal Factor Models
Version: 1.0.1
Date: 2025-11-07
Authors@R: c(person("Hwasoo", "Shin", role = c("aut", "cre"), email = "hshin2@hfhs.org"), person("Marco", "Ferreira", role = "aut") )
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.
License: GPL (>= 2)
Imports: Rcpp (>= 1.0.10), Matrix, LaplacesDemon, spdep, gridExtra, sp
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.2.3
Encoding: UTF-8
Depends: R (>= 2.10)
LazyData: true
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-06-21 08:22:12 UTC; root
Author: Hwasoo Shin [aut, cre], Marco Ferreira [aut]
Maintainer: Hwasoo Shin <hshin2@hfhs.org>
Repository: https://cran.r-universe.dev
Date/Publication: 2025-11-11 08:10:22 UTC
RemoteUrl: https://github.com/cran/DIFM
RemoteRef: HEAD
RemoteSha: a5f9d508ebedc07ae357886cb1e895db44728dee