ZIM

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.

Last 30 days

This package has been downloaded 263 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 3,345 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Sep 11, 2024 with 40 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.