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NTS (version 1.1.3)

Nonlinear Time Series Analysis

Description

Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).

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Version

Install

install.packages('NTS')

Monthly Downloads

334

Version

1.1.3

License

GPL (>= 2)

Maintainer

Xialu Liu

Last Published

September 24th, 2023

Functions in NTS (1.1.3)

cvlm

Check linear models with cross validation
g_cfar1

Generate a CFAR(1) Process
est_cfarh

Estimation of a CFAR Process with Heteroscedasticity and Irregualar Observation Locations
g_cfar

Generate a CFAR Process
g_cfar2

Generate a CFAR(2) Process
backTAR

Backtest for Univariate TAR Models
Tsay

Tsay Test for Nonlinearity
clutterKF

Kalman Filter for Tracking in Clutter
p_cfar_part

Partial Curve Prediction of CFAR Processes
g_cfar2h

Generate a CFAR(2) Process with Heteroscedasticity and Irregular Observation Locations
mTAR.est

Estimation of Multivariate TAR Models
mTAR

Estimation of a Multivariate Two-Regime SETAR Model
Sstep.Sonar

Sequential Importance Sampling Step for A Target with Passive Sonar
backtest

Backtest
hfDummy

Create Dummy Variables for High-Frequency Intraday Seasonality
p_cfar

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
est_cfar

Estimation of a CFAR Process
tvARFiSm

Filtering and Smoothing for Time-Varying AR Models
uTAR

Estimation of a Univariate Two-Regime SETAR Model
simuTargetClutter

Simulate A Moving Target in Clutter
simu_fading

Simulate Signals from A System with Rayleigh Flat-Fading Channels
uTAR.pred

Prediction of A Fitted Univariate TAR Model
uTAR.est

General Estimation of TAR Models
mTAR.sim

Generate Two-Regime (TAR) Models
uTAR.sim

Generate Univariate SETAR Models
wrap.SMC

Sequential Monte Carlo Using Sequential Importance Sampling for Stochastic Volatility Models
mTAR.pred

Prediction of A Fitted Multivariate TAR Model
thr.test

Threshold Nonlinearity Test
tvAR

Estimate Time-Varying Coefficient AR Models
simPassiveSonar

Simulate A Sample Trajectory
MSM.sim

Generate Univariate 2-regime Markov Switching Models
MKFstep.fading

One Propagation Step under Mixture Kalman Filter for Fading Channels
F.test

F Test for Nonlinearity
MSM.fit

Fitting Univariate Autoregressive Markov Switching Models
MKF.Full.RB

Full Information Propagation Step under Mixture Kalman Filter
ACMx

Estimation of Autoregressive Conditional Mean Models
F_test_cfarh

F Test for a CFAR Process with Heteroscedasticity and Irregular Observation Locations
F_test_cfar

F Test for a CFAR Process
NNsetting

Setting Up The Predictor Matrix in A Neural Network for Time Series Data
PRnd

ND Test
SMC.Full.RB

Generic Sequential Monte Carlo Using Full Information Proposal Distribution and Rao-Blackwellization
SMC

Generic Sequential Monte Carlo Method
SISstep.fading

Sequential Importance Sampling Step for Fading Channels
SMC.Full

Generic Sequential Monte Carlo Using Full Information Proposal Distribution
SMC.Smooth

Generic Sequential Monte Carlo Smoothing with Marginal Weights
Sstep.Smooth.Sonar

Sequential Importance Sampling for A Target with Passive Sonar
Sstep.Clutter.Full.RB

Sequential Importance Sampling under Clutter Environment
Sstep.Clutter

Sequential Monte Carlo for A Moving Target under Clutter Environment
Sstep.Clutter.Full

Sequential Importance Sampling under Clutter Environment