R: Nonlinear Time Series Analysis

# Nonlinear Time Series Analysis

## Documentation for package ‘NTS’ version 1.1.3

- DESCRIPTION file

## Help Pages

ACMx | Estimation of Autoregressive Conditional Mean Models
backTAR | Backtest for Univariate TAR Models
backtest | Backtest
clutterKF | Kalman Filter for Tracking in Clutter
cvlm | Check linear models with cross validation
est_cfar | Estimation of a CFAR Process
est_cfarh | Estimation of a CFAR Process with Heteroscedasticity and Irregualar Observation Locations
F.test | F Test for Nonlinearity
F_test_cfar | F Test for a CFAR Process
F_test_cfarh | F Test for a CFAR Process with Heteroscedasticity and Irregular Observation Locations
g_cfar | Generate a CFAR Process
g_cfar1 | Generate a CFAR(1) Process
g_cfar2 | Generate a CFAR(2) Process
g_cfar2h | Generate a CFAR(2) Process with Heteroscedasticity and Irregular Observation Locations
hfDummy | Create Dummy Variables for High-Frequency Intraday Seasonality
MKF.Full.RB | Full Information Propagation Step under Mixture Kalman Filter
MKFstep.fading | One Propagation Step under Mixture Kalman Filter for Fading Channels
MSM.fit | Fitting Univariate Autoregressive Markov Switching Models
MSM.sim | Generate Univariate 2-regime Markov Switching Models
mTAR | Estimation of a Multivariate Two-Regime SETAR Model
mTAR.est | Estimation of Multivariate TAR Models
mTAR.pred | Prediction of A Fitted Multivariate TAR Model
mTAR.sim | Generate Two-Regime (TAR) Models
NNsetting | Setting Up The Predictor Matrix in A Neural Network for Time Series Data
PRnd | ND Test
p_cfar | Prediction of CFAR Processes
p_cfar_part | Partial Curve Prediction of CFAR Processes
rankQ | Rank-Based Portmanteau Tests
rcAR | Estimating of Random-Coefficient AR Models
ref.mTAR | Refine A Fitted 2-Regime Multivariate TAR Model
simPassiveSonar | Simulate A Sample Trajectory
simuTargetClutter | Simulate A Moving Target in Clutter
simu_fading | Simulate Signals from A System with Rayleigh Flat-Fading Channels
SISstep.fading | Sequential Importance Sampling Step for Fading Channels
SMC | Generic Sequential Monte Carlo Method
SMC.Full | Generic Sequential Monte Carlo Using Full Information Proposal Distribution
SMC.Full.RB | Generic Sequential Monte Carlo Using Full Information Proposal Distribution and Rao-Blackwellization
SMC.Smooth | Generic Sequential Monte Carlo Smoothing with Marginal Weights
Sstep.Clutter | Sequential Monte Carlo for A Moving Target under Clutter Environment
Sstep.Clutter.Full | Sequential Importance Sampling under Clutter Environment
Sstep.Clutter.Full.RB | Sequential Importance Sampling under Clutter Environment
Sstep.Smooth.Sonar | Sequential Importance Sampling for A Target with Passive Sonar
Sstep.Sonar | Sequential Importance Sampling Step for A Target with Passive Sonar
thr.test | Threshold Nonlinearity Test
Tsay | Tsay Test for Nonlinearity
tvAR | Estimate Time-Varying Coefficient AR Models
tvARFiSm | Filtering and Smoothing for Time-Varying AR Models
uTAR | Estimation of a Univariate Two-Regime SETAR Model
uTAR.est | General Estimation of TAR Models
uTAR.pred | Prediction of A Fitted Univariate TAR Model
uTAR.sim | Generate Univariate SETAR Models
wrap.SMC | Sequential Monte Carlo Using Sequential Importance Sampling for Stochastic Volatility Models