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Description

Survival Modeling with a Periodic Hazard Function.

Modeling periodic mortality (or other time-to event) processes from right-censored data. Given observations of a process with a known period (e.g. 365 days, 24 hours), functions determine the number, intensity, timing, and duration of peaks of periods of elevated hazard within a period. The underlying model is a mixed wrapped Cauchy function fitted using maximum likelihoods (details in Gurarie et al. (2020) <doi:10.1111/2041-210X.13305>). The development of these tools was motivated by the strongly seasonal mortality patterns observed in many wild animal populations, such that the respective periods of higher mortality can be identified as "mortality seasons".

cyclomort package

Parametric periodic survival modeling in R

The cyclomort package provides tools for modeling and analysis of periodic mortality (or other time-to event) processes from right-censored data. The package was inspired by the need to detect annual seasonal patterns in mortality of wildlife, thus, the central assumption is that the period itself is known (e.g. 1 year, 24 hours, etc.), but the number, intensity and duration of the peaks of increased hazard are unknown. The central function estimates these parameters from right-censored data. Other functions allow for simulation, visualization of fits, and a priori hypothesis testing tools.

Examples are provided in the package vignette

Also - for fun - a blog post!

To install from GitHub:

library(devtools)
install_github("https://github.com/EliGurarie/cyclomort", vignettes = TRUE)

This project is fairly mature, and will soon be uploaded to CRAN, but GitHub is a good place to report issues for further development.

References

E. Gurarie, P. Thompson, A. Kelly, N. Larter, W. Fagan and K. Joly. 2020. For Everything There is a Season: Estimating periodic hazard functions with the cyclomort R package. Methods in Ecology and Evolution, 11(1):129-139. https://doi.org/10.1111/2041-210X.13305

Metadata

Version

1.0.2

License

Unknown

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