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  "Title": "Bayesian Vector Heterogeneous Autoregressive Modeling",
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  "Authors@R": "c(person(given = \"Young Geun\",\nfamily = \"Kim\",\nemail = \"ygeunkimstat@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0001-8651-1167\")),\nperson(given = \"Changryong\",\nfamily = \"Baek\",\nrole = \"ctb\"))",
  "Description": "Tools to model and forecast multivariate time series\nincluding Bayesian Vector heterogeneous autoregressive (VHAR)\nmodel by Kim & Baek (2023)\n(<doi:10.1080/00949655.2023.2281644>). 'bvhar' can model Vector\nAutoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian\nVHAR (BVHAR) models.",
  "License": "MIT + file LICENSE",
  "URL": "https://bvhar.baeconverse.org, https://github.com/ygeunkim/bvhar",
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  "Repository": "https://ygeunkim.r-universe.dev",
  "Date/Publication": "2026-06-05 07:03:22 UTC",
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  "Author": "Young Geun Kim [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0001-8651-1167>),\nChangryong Baek [ctb]",
  "Maintainer": "Young Geun Kim <ygeunkimstat@gmail.com>",
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    "conf_fnr",
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    "irf",
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    "is.ngspec",
    "is.predbvhar",
    "is.ssvsinput",
    "is.stable",
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    "is.vharlse",
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    "mape",
    "mase",
    "mrae",
    "mse",
    "relmae",
    "relspne",
    "rmafe",
    "rmape",
    "rmase",
    "rmsfe",
    "set_bvar",
    "set_bvar_flat",
    "set_bvhar",
    "set_dl",
    "set_factor",
    "set_gdp",
    "set_horseshoe",
    "set_intercept",
    "set_lambda",
    "set_ldlt",
    "set_ng",
    "set_psi",
    "set_ssvs",
    "set_sv",
    "set_weight_bvhar",
    "sim_iw",
    "sim_matgaussian",
    "sim_mncoef",
    "sim_mniw",
    "sim_mnormal",
    "sim_mnvhar_coef",
    "sim_mvt",
    "sim_var",
    "sim_vhar",
    "spillover",
    "spne",
    "stableroot",
    "var_bayes",
    "var_lm",
    "VARtoVMA",
    "vhar_bayes",
    "vhar_lm",
    "VHARtoVMA"
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      "title": "CBOE ETF Volatility Index Dataset",
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      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
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        "OVXCLS",
        "VXFXICLS",
        "VXEEMCLS",
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        "EVZCLS",
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      "table": true,
      "tojson": true
    },
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      "title": "Time points and Financial Events",
      "object": "trading_day",
      "class": [
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      ],
      "fields": [],
      "table": false,
      "tojson": true
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  "_help": [
    {
      "page": "alpl",
      "title": "Evaluate the Density Forecast Based on Average Log Predictive Likelihood (APLP)",
      "topics": [
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        "alpl.bvharcv"
      ]
    },
    {
      "page": "autoplot.bvhardynsp",
      "title": "Dynamic Spillover Indices Plot",
      "topics": [
        "autoplot.bvhardynsp"
      ]
    },
    {
      "page": "autoplot.bvharirf",
      "title": "Plot Impulse Responses",
      "topics": [
        "autoplot.bvharirf"
      ]
    },
    {
      "page": "autoplot.bvharsp",
      "title": "Plot the Result of BVAR and BVHAR MCMC",
      "topics": [
        "autoplot.bvharsp"
      ]
    },
    {
      "page": "autoplot.normaliw",
      "title": "Residual Plot for Minnesota Prior VAR Model",
      "topics": [
        "autoplot.normaliw"
      ]
    },
    {
      "page": "autoplot.predbvhar",
      "title": "Plot Forecast Result",
      "topics": [
        "autolayer.predbvhar",
        "autoplot.predbvhar"
      ]
    },
    {
      "page": "autoplot.summary.bvharsp",
      "title": "Plot the Heatmap of SSVS Coefficients",
      "topics": [
        "autoplot.summary.bvharsp"
      ]
    },
    {
      "page": "autoplot.summary.normaliw",
      "title": "Density Plot for Minnesota Prior VAR Model",
      "topics": [
        "autoplot.summary.normaliw"
      ]
    },
    {
      "page": "bound_bvhar",
      "title": "Setting Empirical Bayes Optimization Bounds",
      "topics": [
        "bound_bvhar",
        "is.boundbvharemp",
        "knit_print.boundbvharemp",
        "print.boundbvharemp"
      ]
    },
    {
      "page": "bvar_flat",
      "title": "Fitting Bayesian VAR(p) of Flat Prior",
      "topics": [
        "AIC.bvarflat",
        "BIC.bvarflat",
        "bvar_flat",
        "is.bvarflat",
        "knit_print.bvarflat",
        "logLik.bvarflat",
        "print.bvarflat"
      ]
    },
    {
      "page": "bvar_minnesota",
      "title": "Fitting Bayesian VAR(p) of Minnesota Prior",
      "topics": [
        "AIC.bvarmn",
        "BIC.bvarmn",
        "bvar_minnesota",
        "is.bvarmn",
        "knit_print.bvarhm",
        "knit_print.bvarmn",
        "logLik.bvarmn",
        "print.bvarhm",
        "print.bvarmn"
      ]
    },
    {
      "page": "bvhar_minnesota",
      "title": "Fitting Bayesian VHAR of Minnesota Prior",
      "topics": [
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        "BIC.bvharmn",
        "bvhar_minnesota",
        "is.bvharmn",
        "knit_print.bvharhm",
        "knit_print.bvharmn",
        "logLik.bvharmn",
        "print.bvharhm",
        "print.bvharmn"
      ]
    },
    {
      "page": "choose_bayes",
      "title": "Finding the Set of Hyperparameters of Bayesian Model",
      "topics": [
        "choose_bayes"
      ]
    },
    {
      "page": "choose_bvar",
      "title": "Finding the Set of Hyperparameters of Individual Bayesian Model",
      "topics": [
        "choose_bvar",
        "choose_bvhar",
        "is.bvharemp",
        "knit_print.bvharemp",
        "print.bvharemp"
      ]
    },
    {
      "page": "choose_var",
      "title": "Choose the Best VAR based on Information Criteria",
      "topics": [
        "choose_var"
      ]
    },
    {
      "page": "coef",
      "title": "Coefficient Matrix of Multivariate Time Series Models",
      "topics": [
        "coef",
        "coef.bvarflat",
        "coef.bvarmn",
        "coef.bvharmn",
        "coef.bvharsp",
        "coef.summary.bvharsp",
        "coef.varlse",
        "coef.vharlse"
      ]
    },
    {
      "page": "compute_dic",
      "title": "Deviance Information Criterion of Multivariate Time Series Model",
      "topics": [
        "compute_dic",
        "compute_dic.bvarmn"
      ]
    },
    {
      "page": "compute_logml",
      "title": "Extracting Log of Marginal Likelihood",
      "topics": [
        "compute_logml",
        "compute_logml.bvarmn",
        "compute_logml.bvharmn"
      ]
    },
    {
      "page": "conf_fdr",
      "title": "Evaluate the Sparsity Estimation Based on FDR",
      "topics": [
        "conf_fdr",
        "conf_fdr.summary.bvharsp"
      ]
    },
    {
      "page": "conf_fnr",
      "title": "Evaluate the Sparsity Estimation Based on FNR",
      "topics": [
        "conf_fnr",
        "conf_fnr.summary.bvharsp"
      ]
    },
    {
      "page": "conf_fscore",
      "title": "Evaluate the Sparsity Estimation Based on F1 Score",
      "topics": [
        "conf_fscore",
        "conf_fscore.summary.bvharsp"
      ]
    },
    {
      "page": "conf_prec",
      "title": "Evaluate the Sparsity Estimation Based on Precision",
      "topics": [
        "conf_prec",
        "conf_prec.summary.bvharsp"
      ]
    },
    {
      "page": "conf_recall",
      "title": "Evaluate the Sparsity Estimation Based on Recall",
      "topics": [
        "conf_recall",
        "conf_recall.summary.bvharsp"
      ]
    },
    {
      "page": "confusion",
      "title": "Evaluate the Sparsity Estimation Based on Confusion Matrix",
      "topics": [
        "confusion",
        "confusion.summary.bvharsp"
      ]
    },
    {
      "page": "divide_ts",
      "title": "Split a Time Series Dataset into Train-Test Set",
      "topics": [
        "divide_ts"
      ]
    },
    {
      "page": "dynamic_spillover",
      "title": "Dynamic Spillover",
      "topics": [
        "dynamic_spillover",
        "dynamic_spillover.ldltmod",
        "dynamic_spillover.normaliw",
        "dynamic_spillover.olsmod",
        "dynamic_spillover.svmod",
        "knit_print.bvhardynsp",
        "print.bvhardynsp"
      ]
    },
    {
      "page": "etf_vix",
      "title": "CBOE ETF Volatility Index Dataset",
      "topics": [
        "etf_vix"
      ]
    },
    {
      "page": "fitted",
      "title": "Fitted Matrix from Multivariate Time Series Models",
      "topics": [
        "fitted",
        "fitted.bvarflat",
        "fitted.bvarmn",
        "fitted.bvharmn",
        "fitted.varlse",
        "fitted.vharlse"
      ]
    },
    {
      "page": "forecast_expand",
      "title": "Out-of-sample Forecasting based on Expanding Window",
      "topics": [
        "forecast_expand",
        "forecast_expand.ldltmod",
        "forecast_expand.normaliw",
        "forecast_expand.olsmod",
        "forecast_expand.svmod"
      ]
    },
    {
      "page": "forecast_roll",
      "title": "Out-of-sample Forecasting based on Rolling Window",
      "topics": [
        "forecast_roll",
        "forecast_roll.ldltmod",
        "forecast_roll.normaliw",
        "forecast_roll.olsmod",
        "forecast_roll.svmod",
        "is.bvharcv",
        "knit_print.bvharcv",
        "print.bvharcv"
      ]
    },
    {
      "page": "FPE",
      "title": "Final Prediction Error Criterion",
      "topics": [
        "FPE",
        "FPE.varlse",
        "FPE.vharlse"
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      "page": "fromse",
      "title": "Evaluate the Estimation Based on Frobenius Norm",
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        "fromse.bvharsp"
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      "page": "geom_eval",
      "title": "Adding Test Data Layer",
      "topics": [
        "geom_eval"
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      "page": "gg_loss",
      "title": "Compare Lists of Models",
      "topics": [
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      "page": "HQ",
      "title": "Hannan-Quinn Criterion",
      "topics": [
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        "HQ.bvarflat",
        "HQ.bvarmn",
        "HQ.bvharmn",
        "HQ.logLik",
        "HQ.varlse",
        "HQ.vharlse"
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    },
    {
      "page": "irf",
      "title": "Impulse Response Analysis",
      "topics": [
        "irf",
        "irf.bvarldlt",
        "irf.bvharldlt",
        "irf.varlse",
        "irf.vharlse",
        "is.bvharirf",
        "knit_print.bvharirf",
        "print.bvharirf"
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      "page": "is.stable",
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        "is.stable.bvarflat",
        "is.stable.bvarmn",
        "is.stable.bvharmn",
        "is.stable.varlse",
        "is.stable.vharlse"
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      "page": "mae",
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        "mae.bvharcv",
        "mae.predbvhar"
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      "page": "mape",
      "title": "Evaluate the Model Based on MAPE (Mean Absolute Percentage Error)",
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        "mape.bvharcv",
        "mape.predbvhar"
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      "title": "Evaluate the Model Based on MASE (Mean Absolute Scaled Error)",
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        "mase.bvharcv",
        "mase.predbvhar"
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      "page": "mrae",
      "title": "Evaluate the Model Based on MRAE (Mean Relative Absolute Error)",
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        "mrae.bvharcv",
        "mrae.predbvhar"
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        "mse.predbvhar"
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      "page": "predict",
      "title": "Forecasting Multivariate Time Series",
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        "predict",
        "predict.bvarflat",
        "predict.bvarldlt",
        "predict.bvarmn",
        "predict.bvarsv",
        "predict.bvharldlt",
        "predict.bvharmn",
        "predict.bvharsv",
        "predict.varlse",
        "predict.vharlse",
        "print.predbvhar"
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      "title": "Summarizing BVAR and BVHAR with Shrinkage Priors",
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        "summary.ssvsmod"
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      "title": "Evaluate the Model Based on RelMAE (Relative MAE)",
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      "title": "Residual Matrix from Multivariate Time Series Models",
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