Nonlinear Time Series Analysis

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Documentation for package ‘NTS’ version 1.1.3

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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