Package: hystar 1.2.1

hystar: Fit the Hysteretic Threshold Autoregressive Model
Estimate parameters of the hysteretic threshold autoregressive (HysTAR) model, using conditional least squares. In addition, you can generate time series data from the HysTAR model. For details, see Li, Guan, Li and Yu (2015) <doi:10.1093/biomet/asv017>.
Authors:
hystar_1.2.1.tar.gz
hystar_1.2.1.zip(r-4.5)hystar_1.2.1.zip(r-4.4)hystar_1.2.1.zip(r-4.3)
hystar_1.2.1.tgz(r-4.5-x86_64)hystar_1.2.1.tgz(r-4.5-arm64)hystar_1.2.1.tgz(r-4.4-x86_64)hystar_1.2.1.tgz(r-4.4-arm64)hystar_1.2.1.tgz(r-4.3-x86_64)hystar_1.2.1.tgz(r-4.3-arm64)
hystar_1.2.1.tar.gz(r-4.5-noble)hystar_1.2.1.tar.gz(r-4.4-noble)
hystar_1.2.1.tgz(r-4.4-emscripten)hystar_1.2.1.tgz(r-4.3-emscripten)
hystar.pdf |hystar.html✨
hystar/json (API)
NEWS
# Install 'hystar' in R: |
install.packages('hystar', repos = c('https://daandejongen.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/daandejongen/hystar/issues
Pkgdown site:https://daandejongen.github.io
autoregressionestimationhysteresissimulationstatisticsthresholdtime-series-analysiscpp
Last updated 1 years agofrom:35c5563346. Checks:8 OK, 4 NOTE. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 07 2025 |
R-4.5-win-x86_64 | OK | Mar 07 2025 |
R-4.5-mac-x86_64 | OK | Mar 07 2025 |
R-4.5-mac-aarch64 | OK | Mar 07 2025 |
R-4.5-linux-x86_64 | OK | Mar 07 2025 |
R-4.4-win-x86_64 | NOTE | Mar 07 2025 |
R-4.4-mac-x86_64 | NOTE | Mar 07 2025 |
R-4.4-mac-aarch64 | NOTE | Mar 07 2025 |
R-4.4-linux-x86_64 | NOTE | Mar 07 2025 |
R-4.3-win-x86_64 | OK | Mar 07 2025 |
R-4.3-mac-x86_64 | OK | Mar 07 2025 |
R-4.3-mac-aarch64 | OK | Mar 07 2025 |
Exports:hystar_fithystar_infohystar_simz_sim
Dependencies:Rcpp
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Estimate the HysTAR model using conditional least squares estimation | hystar_fit |
Get more information about the hystar package | hystar_info |
Simulate data from the HysTAR model | hystar_sim |
Simulate the threshold/control variable Z | z_sim |