AIC.varlse              Akaike's Information Criterion of Multivariate
                        Time Series Model
BIC.varlse              Bayesian Information Criterion of Multivariate
                        Time Series Model
FPE                     Final Prediction Error Criterion
FPE.varlse              Final Prediction Error Criterion of
                        Multivariate Time Series Model
HQ                      Hannan-Quinn Criterion
HQ.varlse               Hannan-Quinn Criterion of Multivariate Time
                        Series Model
VARtoVMA                Convert VAR to VMA(infinite)
VHARtoVMA               Convert VHAR to VMA(infinite)
analyze_ir.varlse       Impulse Response Analysis
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_horseshoe          Fitting Bayesian VAR(p) of Horseshoe Prior
bvar_minnesota          Fitting Bayesian VAR(p) of Minnesota Prior
bvar_niwhm              Fitting Hierarchical Bayesian VAR(p)
bvar_ssvs               Fitting Bayesian VAR(p) of SSVS Prior
bvar_sv                 Fitting Bayesian VAR-SV
bvhar_horseshoe         Fitting Bayesian VHAR of Horseshoe Prior
bvhar_minnesota         Fitting Bayesian VHAR of Minnesota Prior
bvhar_ssvs              Fitting Bayesian VHAR of SSVS Prior
bvhar_sv                Fitting Bayesian VHAR-SV
choose_bayes            Finding the Set of Hyperparameters of Bayesian
                        Model
choose_bvar             Finding the Set of Hyperparameters of
                        Individual Bayesian Model
choose_ssvs             Choose the Hyperparameters Set of SSVS-VAR
                        using a Default Semiautomatic Approach
choose_var              Choose the Best VAR based on Information
                        Criteria
coef.varlse             Coefficient Matrix of Multivariate Time Series
                        Models
compute_dic             Deviance Information Criterion of Multivariate
                        Time Series Model
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
etf_vix                 CBOE ETF Volatility Index Dataset
fitted.varlse           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
fromse                  Evaluate the Estimation Based on Frobenius Norm
geom_eval               Adding Test Data Layer
gg_loss                 Compare Lists of Models
init_ssvs               Initial Parameters of Stochastic Search
                        Variable Selection (SSVS) Model
is.stable               Stability of the process
is.stable.varlse        Stability of VAR Coefficient Matrix
is.varlse               See if the Object a class in this package
logLik.varlse           Extract Log-Likelihood of Multivariate Time
                        Series Model
lpl                     Evaluate the Model Based on Log Predictive
                        Likelihood
mae                     Evaluate the Model Based on MAE (Mean Absolute
                        Error)
mape                    Evaluate the Model Based on MAPE (Mean Absolute
                        Percentage Error)
mase                    Evaluate the Model Based on MASE (Mean Absolute
                        Scaled Error)
mrae                    Evaluate the Model Based on MRAE (Mean Relative
                        Absolute Error)
mse                     Evaluate the Model Based on MSE (Mean Square
                        Error)
oxfordman               Oxford-Man Institute Realized Library
predict.varlse          Forecasting Multivariate Time Series
print.summary.bvharsp   Summarizing BVAR and BVHAR with Shrinkage
                        Priors
relmae                  Evaluate the Model Based on RelMAE (Relative
                        MAE)
relspne                 Evaluate the Estimation Based on Relative
                        Spectral Norm Error
residuals.varlse        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_horseshoe           Horseshoe Prior Specification
set_intercept           Prior for Constant Term
set_lambda              Hyperpriors for Bayesian Models
set_ssvs                Stochastic Search Variable Selection (SSVS)
                        Hyperparameter for Coefficients Matrix and
                        Cholesky Factor
set_sv                  Stochastic Volatility Specification
sim_horseshoe_var       Generate Horseshoe Parameters
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_ssvs_var            Generate SSVS Parameters
sim_var                 Generate Multivariate Time Series Process
                        Following VAR(p)
sim_vhar                Generate Multivariate Time Series Process
                        Following VAR(p)
split_coef              Splitting Coefficient Matrix into List
spne                    Evaluate the Estimation Based on Spectral Norm
                        Error
stableroot              Roots of characteristic polynomial
stableroot.varlse       Characteristic polynomial roots for VAR
                        Coefficient Matrix
summary.normaliw        Summarizing Bayesian Multivariate Time Series
                        Model
summary.varlse          Summarizing Vector Autoregressive Model
summary.vharlse         Summarizing Vector HAR Model
var_lm                  Fitting Vector Autoregressive Model of Order p
                        Model
vhar_lm                 Fitting Vector Heterogeneous Autoregressive
                        Model
