| aar | Additive nonlinear autoregressive model |
| accuracy_stat | Forecasting accuracy measures. |
| addRegime | addRegime test |
| ar_mean | Long-term mean of an AR(p) process |
| autopairs | Bivariate time series plots |
| autotriples | Trivariate time series plots |
| autotriples.rgl | Interactive trivariate time series plots |
| availableModels | Available models |
| barry | Time series of PPI used as example in Bierens and Martins... |
| BBCTest | Test of unit root against SETAR alternative |
| charac_root | Characteristic roots of the AR coefficients |
| coefB | Extract cointegration parameters A, B and PI |
| computeGradient | computeGradient |
| DataUsUnemp | US unemployment series used in Caner and Hansen (2001) |
| delta | delta test of conditional independence |
| delta.lin | delta test of linearity |
| fevd.nlVar | Forecast Error Variance Decomposition |
| fitted.nlVar | fitted method for objects of class nlVar, i.e. VAR and VECM... |
| getTh | Extract threshold(s) coefficient |
| GIRF | Generalized Impulse response Function (GIRF) |
| IIPUs | US monthly industrial production from Hansen (1999) |
| irf.nlVar | Impulse response function |
| isLinear | isLinear |
| KapShinTest | Test of unit root against SETAR alternative with |
| lags.select | Selection of the lag with Information criterion. |
| linear | Linear AutoRegressive models |
| lineVar | Multivariate linear models: VAR and VECM |
| llar | Locally linear model |
| logLik.nlVar | Extract Log-Likelihood |
| lstar | Logistic Smooth Transition AutoRegressive model |
| MakeThSpec | Specification of the threshold search |
| MAPE | Mean Absolute Percent Error |
| mse | Mean Square Error |
| m.unrate | Monthly US unemployment |
| nlar | Non-linear time series model, base class definition |
| nlar-methods | NLAR methods |
| nlar.struct | NLAR common structure |
| nnet | Neural Network nonlinear autoregressive model |
| oneStep | oneStep |
| plot_ECT | Plot the Error Correct Term (ECT) response |
| plot-methods | Plotting methods for SETAR and LSTAR subclasses |
| predict.nlar | Predict method for objects of class "nlar". |
| predict_rolling | Rolling forecasts |
| predict.VAR | Predict method for objects of class "VAR", "VECM" or "TVAR" |
| rank.select | Selection of the cointegrating rank with Information... |
| rank.test | Test of the cointegrating rank |
| reexports | Objects exported from other packages |
| regime | Extract a variable showing the regime |
| resample_vec | Resampling schemes |
| resVar | Residual variance |
| selectHyperParms | Automatic selection of model hyper-parameters |
| selectSETAR | Automatic selection of SETAR hyper-parameters |
| setar | Self Threshold Autoregressive model |
| setar.sim | Simulation and bootstrap of Threshold Autoregressive model... |
| setarTest | Test of linearity against threshold (SETAR) |
| setarTest_IIPUs_results | Results from the setarTest, applied on Hansen (1999) data |
| sigmoid | sigmoid functions |
| star | STAR model |
| toLatex.setar | Latex representation of fitted setar models |
| tsDyn-package | Getting started with the tsDyn package |
| TVAR | Multivariate Threshold Vector Autoregressive model |
| TVAR.LRtest | Test of linearity |
| TVAR.sim | Simulation of a multivariate Threshold Autoregressive model... |
| TVECM | Threshold Vector Error Correction model (VECM) |
| TVECM.HStest | Test of linear cointegration vs threshold cointegration |
| TVECM.SeoTest | No cointegration vs threshold cointegration test |
| TVECM.sim | Simulation and bootstrap a VECM or bivariate TVECM |
| VAR.boot | Simulate or bootstrap a VAR model |
| VARrep | VAR representation |
| VECM | Estimation of Vector error correction model (VECM) |
| VECM_symbolic | Virtual VECM model |
| zeroyld | zeroyld time series |
