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  "Title": "Dynamic ICAR Spatiotemporal Factor Models",
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  "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\nreduction. This is especially applicable to multivariate\nlarge-scale datasets. It allows researchers to understand the\nlatent factors of the data which are the linear or non-linear\ncombination of the variables. Dynamic Intrinsic Conditional\nAutocorrelative Priors (ICAR) Spatiotemporal Factor Models\n'DIFM' package provides function to run Markov Chain Monte\nCarlo (MCMC), evaluation methods and visual plots from Shin and\nFerreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method\nis a class of Bayesian factor model which can account for\nspatial and temporal correlations. By incorporating these\ncorrelations, the model can capture specific behaviors and\nprovide predictions.",
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      "title": "Dynamic ICAR Spatiotemporal Factor Models",
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        "DIFM"
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      "page": "buildH",
      "title": "Spatial dependence matrix of the factor loadings",
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