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:
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-4.4-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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 years agofrom:0b89afdf66. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 25 2024 |
| R-4.5-win | OK | Oct 25 2024 |
| R-4.5-linux | OK | Oct 25 2024 |
| R-4.4-win | OK | Oct 25 2024 |
| R-4.4-mac | OK | Oct 25 2024 |
| R-4.3-win | OK | Oct 25 2024 |
| R-4.3-mac | OK | Oct 25 2024 |
Exports:ARorder.lognormARorder.normLS.lognormLS.normParam.lognormParam.normreg.thr.lognormreg.thr.normsimu.tar.lognormsimu.tar.norm
Dependencies:mvtnorm
