| alpl | Evaluate the Density Forecast Based on Average Log Predictive... |
| autoplot.bvhardynsp | Dynamic Spillover Indices Plot |
| autoplot.bvharirf | Plot Impulse Responses |
| autoplot.bvharsp | Plot the Result of BVAR and BVHAR MCMC |
| autoplot.normaliw | Residual Plot for Minnesota Prior VAR Model |
| autoplot.predbvhar | Plot Forecast Result |
| autoplot.summary.bvharsp | Plot the Heatmap of SSVS Coefficients |
| autoplot.summary.normaliw | Density Plot for Minnesota Prior VAR Model |
| bound_bvhar | Setting Empirical Bayes Optimization Bounds |
| bvar_flat | Fitting Bayesian VAR(p) of Flat Prior |
| bvar_minnesota | Fitting Bayesian VAR(p) of Minnesota Prior |
| bvhar_minnesota | Fitting Bayesian VHAR of Minnesota Prior |
| bvhar-package | bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling |
| choose_bayes | Finding the Set of Hyperparameters of Bayesian Model |
| choose_bvar | Finding the Set of Hyperparameters of Individual Bayesian... |
| choose_var | Choose the Best VAR based on Information Criteria |
| coef | Coefficient Matrix of Multivariate Time Series Models |
| compute_dic | Deviance Information Criterion of Multivariate Time Series... |
| compute_logml | Extracting Log of Marginal Likelihood |
| conf_fdr | Evaluate the Sparsity Estimation Based on FDR |
| conf_fnr | Evaluate the Sparsity Estimation Based on FNR |
| conf_fscore | Evaluate the Sparsity Estimation Based on F1 Score |
| conf_prec | Evaluate the Sparsity Estimation Based on Precision |
| conf_recall | Evaluate the Sparsity Estimation Based on Recall |
| confusion | Evaluate the Sparsity Estimation Based on Confusion Matrix |
| divide_ts | Split a Time Series Dataset into Train-Test Set |
| dynamic_spillover | Dynamic Spillover |
| etf_vix | CBOE ETF Volatility Index Dataset |
| financial_history_appendix | Time points and Financial Events |
| fitted | Fitted Matrix from Multivariate Time Series Models |
| forecast_expand | Out-of-sample Forecasting based on Expanding Window |
| forecast_roll | Out-of-sample Forecasting based on Rolling Window |
| FPE | Final Prediction Error Criterion |
| fromse | Evaluate the Estimation Based on Frobenius Norm |
| geom_eval | Adding Test Data Layer |
| gg_loss | Compare Lists of Models |
| HQ | Hannan-Quinn Criterion |
| irf | Impulse Response Analysis |
| is.stable | Stability of the process |
| mae | Evaluate the Model Based on MAE (Mean Absolute Error) |
| mape | Evaluate the Model Based on MAPE (Mean Absolute Percentage... |
| mase | Evaluate the Model Based on MASE (Mean Absolute Scaled Error) |
| mrae | Evaluate the Model Based on MRAE (Mean Relative Absolute... |
| mse | Evaluate the Model Based on MSE (Mean Square Error) |
| predict | Forecasting Multivariate Time Series |
| reexports | Objects exported from other packages |
| relmae | Evaluate the Model Based on RelMAE (Relative MAE) |
| relspne | Evaluate the Estimation Based on Relative Spectral Norm Error |
| residuals | Residual Matrix from Multivariate Time Series Models |
| rmafe | Evaluate the Model Based on RMAFE |
| rmape | Evaluate the Model Based on RMAPE (Relative MAPE) |
| rmase | Evaluate the Model Based on RMASE (Relative MASE) |
| rmsfe | Evaluate the Model Based on RMSFE |
| set_bvar | Hyperparameters for Bayesian Models |
| set_dl | Dirichlet-Laplace Hyperparameter for Coefficients and... |
| set_gdp | Generalized Double Pareto Shrinkage Hyperparameters for... |
| set_horseshoe | Horseshoe Prior Specification |
| set_intercept | Prior for Constant Term |
| set_lambda | Hyperpriors for Bayesian Models |
| set_ldlt | Covariance Matrix Prior Specification |
| set_ng | Normal-Gamma Hyperparameter for Coefficients and... |
| set_ssvs | Stochastic Search Variable Selection (SSVS) Hyperparameter... |
| sim_iw | Generate Inverse-Wishart Random Matrix |
| sim_matgaussian | Generate Matrix Normal Random Matrix |
| sim_mncoef | Generate Minnesota BVAR Parameters |
| sim_mniw | Generate Normal-IW Random Family |
| sim_mnormal | Generate Multivariate Normal Random Vector |
| sim_mnvhar_coef | Generate Minnesota BVAR Parameters |
| sim_mvt | Generate Multivariate t Random Vector |
| sim_var | Generate Multivariate Time Series Process Following VAR(p) |
| sim_vhar | Generate Multivariate Time Series Process Following VAR(p) |
| spillover | h-step ahead Normalized Spillover |
| spne | Evaluate the Estimation Based on Spectral Norm Error |
| stableroot | Roots of characteristic polynomial |
| summary.bvharsp | Summarizing BVAR and BVHAR with Shrinkage Priors |
| summary.normaliw | Summarizing Bayesian Multivariate Time Series Model |
| summary.varlse | Summarizing Vector Autoregressive Model |
| summary.vharlse | Summarizing Vector HAR Model |
| var_bayes | Fitting Bayesian VAR with Coefficient and Covariance Prior |
| var_lm | Fitting Vector Autoregressive Model of Order p Model |
| VARtoVMA | Convert VAR to VMA(infinite) |
| vhar_bayes | Fitting Bayesian VHAR with Coefficient and Covariance Prior |
| vhar_lm | Fitting Vector Heterogeneous Autoregressive Model |
| VHARtoVMA | Convert VHAR to VMA(infinite) |
