Package: ZIM 1.1.0.1809
Ming Yang
ZIM: Zero-Inflated Models (ZIM) for Count Time Series with Excess Zeros
Analyze count time series with excess zeros. Two types of statistical models are supported: Markov regression by Yang et al. (2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al. (2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and parameter-driven models respectively in the time series literature. The functions used for Markov regression or observation-driven models can also be used to fit ordinary regression models with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, quantile, and generate random numbers from ZIP and ZINB distributions.
Authors:
ZIM_1.1.0.1809.tar.gz
ZIM_1.1.0.1809.zip(r-4.5)ZIM_1.1.0.1809.zip(r-4.4)ZIM_1.1.0.1809.zip(r-4.3)
ZIM_1.1.0.1809.tgz(r-4.4-any)ZIM_1.1.0.1809.tgz(r-4.3-any)
ZIM_1.1.0.1809.tar.gz(r-4.5-noble)ZIM_1.1.0.1809.tar.gz(r-4.4-noble)
ZIM_1.1.0.1809.tgz(r-4.4-emscripten)ZIM_1.1.0.1809.tgz(r-4.3-emscripten)
ZIM.pdf |ZIM.html✨
ZIM/json (API)
| # Install 'ZIM' in R: |
| install.packages('ZIM', repos = c('https://mingstat.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/biostatstudio/zim/issues
Last updated 1 years agofrom:9bda73e7af. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 31 2024 |
| R-4.5-win | OK | Oct 31 2024 |
| R-4.5-linux | OK | Oct 31 2024 |
| R-4.4-win | OK | Oct 31 2024 |
| R-4.4-mac | OK | Oct 31 2024 |
| R-4.3-win | OK | Oct 31 2024 |
| R-4.3-mac | OK | Oct 31 2024 |
Exports:bshiftdzimdzim.controldzim.filterdzim.fitdzim.plotdzim.simdzim.smoothdzinbdzippvaluepzinbpzipqzinbqziprzinbrzipzimzim.controlzim.fit
Dependencies:MASS
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Zero-Inflated Models for Count Time Series with Excess Zeros | ZIM-package ZIM |
| Backshift Operator Apply the backshift operator or lag operator to a time series objective. | bshift |
| Fitting Dynamic Zero-Inflated Models | dzim |
| Auxiliary for Controlling DZIM Fitting | dzim.control |
| Particle Filtering for DZIM | dzim.filter |
| Fitter Function for Dynamic Zero-Inflated Models | dzim.fit |
| Trace Plots from DZIM | dzim.plot |
| Simulate Data from DZIM | dzim.sim |
| Particle Smoothing for DZIM | dzim.smooth |
| Example: Injury Series from Occupational Health | injury |
| Function to Compute P-value. | pvalue |
| Example: Syphilis Series | syph |
| Fitting Zero-Inflated Models | zim |
| Auxiliary for Controlling ZIM Fitting | zim.control |
| Fitter Function for Zero-Inflated Models | zim.fit |
| The Zero-Inflated Negative Binomial Distribution | dzinb pzinb qzinb rzinb ZINB |
| The Zero-Inflated Poisson Distribution | dzip pzip qzip rzip ZIP |
