Package: TAR 1.0

TAR: Bayesian Modeling of Autoregressive Threshold Time Series Models

Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <doi:10.1081/STA-200054435>.

Authors:Hanwen Zhang, Fabio H. Nieto

TAR_1.0.tar.gz
TAR_1.0.zip(r-4.5)TAR_1.0.zip(r-4.4)TAR_1.0.zip(r-4.3)
TAR_1.0.tgz(r-4.5-any)TAR_1.0.tgz(r-4.4-any)TAR_1.0.tgz(r-4.3-any)
TAR_1.0.tar.gz(r-4.5-noble)TAR_1.0.tar.gz(r-4.4-noble)
TAR_1.0.tgz(r-4.4-emscripten)TAR_1.0.tgz(r-4.3-emscripten)
TAR.pdf |TAR.html
TAR/json (API)

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

On CRAN:

Conda:

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

2.74 score 5 stars 11 scripts 430 downloads 12 mentions 10 exports 1 dependencies

Last updated 8 years agofrom:0b89afdf66. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 24 2025
R-4.5-winOKMar 24 2025
R-4.5-macOKMar 24 2025
R-4.5-linuxOKMar 24 2025
R-4.4-winOKMar 24 2025
R-4.4-macOKMar 24 2025
R-4.4-linuxOKMar 24 2025
R-4.3-winOKMar 24 2025
R-4.3-macOKMar 24 2025

Exports:ARorder.lognormARorder.normLS.lognormLS.normParam.lognormParam.normreg.thr.lognormreg.thr.normsimu.tar.lognormsimu.tar.norm

Dependencies:mvtnorm