doi:10.1093/biomet/66.2.367>. Extensive testing against other estimators suggest the weib_percentile() function is especially useful in generating more accurate and less biased estimates of onset and offset (Belitz et al. 2020) <doi:10.1111/2041-210X.13448>. Non-parametric bootstrapping can be used to generate confidence intervals around those estimates, although this is computationally expensive. Additionally, this package offers an easy way to perform non-parametric bootstrapping to generate confidence intervals for quantile estimates, mean estimates, or any statistical function of interest.">

phenesse: Estimate Phenological Metrics using Presence-Only Data (original) (raw)

Generates Weibull-parameterized estimates of phenology for any percentile of a distribution using the framework established in Cooke (1979) <doi:10.1093/biomet/66.2.367>. Extensive testing against other estimators suggest the weib_percentile() function is especially useful in generating more accurate and less biased estimates of onset and offset (Belitz et al. 2020) <doi:10.1111/2041-210X.13448>. Non-parametric bootstrapping can be used to generate confidence intervals around those estimates, although this is computationally expensive. Additionally, this package offers an easy way to perform non-parametric bootstrapping to generate confidence intervals for quantile estimates, mean estimates, or any statistical function of interest.

Version: 0.1.3
Depends: R (≥ 3.5)
Imports: boot, fitdistrplus, stats
Suggests: knitr, rmarkdown, testthat
Published: 2025-06-09
DOI: 10.32614/CRAN.package.phenesse
Author: Michael Belitz ORCID iD [aut, cre], Caitlin Campbell ORCID iD [ctb], Daijiang Li ORCID iD [ctb]
Maintainer: Michael Belitz
BugReports: https://github.com/mbelitz/phenesse/issues
License: CC0
URL: https://github.com/mbelitz/phenesse
NeedsCompilation: no
Citation: phenesse citation info
Materials: README, NEWS
CRAN checks: phenesse results

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