Package: NTS 1.1.3

NTS: Nonlinear Time Series Analysis

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).

Authors:Ruey Tsay [aut], Rong Chen [aut], Xialu Liu [aut, cre]

NTS_1.1.3.tar.gz
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NTS.pdf |NTS.html✨
NTS/json (API)

# Install 'NTS' in R:
install.packages('NTS', repos = c('https://convfunctimeseries.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.93 score 2 stars 47 scripts 326 downloads 9 mentions 52 exports 8 dependencies

Last updated 1 years agofrom:1538405f77. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-winNOTENov 18 2024
R-4.5-linuxNOTENov 18 2024
R-4.4-winNOTENov 18 2024
R-4.4-macNOTENov 18 2024
R-4.3-winNOTENov 18 2024
R-4.3-macNOTENov 18 2024

Exports:ACMxbackTARbacktestclutterKFcvlmest_cfarest_cfarhF_test_cfarF_test_cfarhF.testg_cfarg_cfar1g_cfar2g_cfar2hhfDummyMKF.Full.RBMKFstep.fadingMSM.fitMSM.simmTARmTAR.estmTAR.predmTAR.simNNsettingp_cfarp_cfar_partPRndrankQrcARref.mTARsimPassiveSonarsimu_fadingsimuTargetClutterSISstep.fadingSMCSMC.FullSMC.Full.RBSMC.SmoothSstep.ClutterSstep.Clutter.FullSstep.Clutter.Full.RBSstep.Smooth.SonarSstep.Sonarthr.testTsaytvARtvARFiSmuTARuTAR.estuTAR.preduTAR.simwrap.SMC

Dependencies:dlmlatticeMASSMSwMnlmerbibutilsRdpacktensor

Readme and manuals

Help Manual

Help pageTopics
Estimation of Autoregressive Conditional Mean ModelsACMx
Backtest for Univariate TAR ModelsbackTAR
Backtestbacktest
Kalman Filter for Tracking in ClutterclutterKF
Check linear models with cross validationcvlm
Estimation of a CFAR Processest_cfar
Estimation of a CFAR Process with Heteroscedasticity and Irregualar Observation Locationsest_cfarh
F Test for a CFAR ProcessF_test_cfar
F Test for a CFAR Process with Heteroscedasticity and Irregular Observation LocationsF_test_cfarh
F Test for NonlinearityF.test
Generate a CFAR Processg_cfar
Generate a CFAR(1) Processg_cfar1
Generate a CFAR(2) Processg_cfar2
Generate a CFAR(2) Process with Heteroscedasticity and Irregular Observation Locationsg_cfar2h
Create Dummy Variables for High-Frequency Intraday SeasonalityhfDummy
Full Information Propagation Step under Mixture Kalman FilterMKF.Full.RB
One Propagation Step under Mixture Kalman Filter for Fading ChannelsMKFstep.fading
Fitting Univariate Autoregressive Markov Switching ModelsMSM.fit
Generate Univariate 2-regime Markov Switching ModelsMSM.sim
Estimation of a Multivariate Two-Regime SETAR ModelmTAR
Estimation of Multivariate TAR ModelsmTAR.est
Prediction of A Fitted Multivariate TAR ModelmTAR.pred
Generate Two-Regime (TAR) ModelsmTAR.sim
Setting Up The Predictor Matrix in A Neural Network for Time Series DataNNsetting
Prediction of CFAR Processesp_cfar
Partial Curve Prediction of CFAR Processesp_cfar_part
ND TestPRnd
Rank-Based Portmanteau TestsrankQ
Estimating of Random-Coefficient AR ModelsrcAR
Refine A Fitted 2-Regime Multivariate TAR Modelref.mTAR
Simulate A Sample TrajectorysimPassiveSonar
Simulate Signals from A System with Rayleigh Flat-Fading Channelssimu_fading
Simulate A Moving Target in CluttersimuTargetClutter
Sequential Importance Sampling Step for Fading ChannelsSISstep.fading
Generic Sequential Monte Carlo MethodSMC
Generic Sequential Monte Carlo Using Full Information Proposal DistributionSMC.Full
Generic Sequential Monte Carlo Using Full Information Proposal Distribution and Rao-BlackwellizationSMC.Full.RB
Generic Sequential Monte Carlo Smoothing with Marginal WeightsSMC.Smooth
Sequential Monte Carlo for A Moving Target under Clutter EnvironmentSstep.Clutter
Sequential Importance Sampling under Clutter EnvironmentSstep.Clutter.Full
Sequential Importance Sampling under Clutter EnvironmentSstep.Clutter.Full.RB
Sequential Importance Sampling for A Target with Passive SonarSstep.Smooth.Sonar
Sequential Importance Sampling Step for A Target with Passive SonarSstep.Sonar
Threshold Nonlinearity Testthr.test
Tsay Test for NonlinearityTsay
Estimate Time-Varying Coefficient AR ModelstvAR
Filtering and Smoothing for Time-Varying AR ModelstvARFiSm
Estimation of a Univariate Two-Regime SETAR ModeluTAR
General Estimation of TAR ModelsuTAR.est
Prediction of A Fitted Univariate TAR ModeluTAR.pred
Generate Univariate SETAR ModelsuTAR.sim
Sequential Monte Carlo Using Sequential Importance Sampling for Stochastic Volatility Modelswrap.SMC