Description: An alternative to carrying out phenotypic adaptability and stability analyses, taking into account nonparametric statistics. Can be used as a robust approach, less sensitive to departures from common genotypic, environmental, and GxE effects data assumptions (e.g., normal distribution of errors).

Title: Estimate Phenological Metrics using Presence-Only Data
Authors@R: c(person("Michael", "Belitz", email = "mbelitz@ufl.edu", role = c("aut", "cre")),
person("Caitlin", "Campbell", email = "caitjcampbell@gmail.com", role = "ctb"))
Description: Generates Weibull-parameterized estimates of phenology for any percentile of
a distribution using the framework established in Cooke (1979)
<doi.org/10.1093/biomet/66.2.367>. Non-parametric bootstrapping can be used
to generate confidence intervals around those estimates.
Additionally, this package offers an easy way to perform non-parametric
bootstrapping to generate confidence intervals for quantile estimates or

person("Caitlin", "Campbell", email = "caitjcampbell@gmail.com", role = "ctb"),
person("Daijinag", "Li", email = "daijianglee@gmail.com", role = "ctb"))
<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.
Non-parametric bootstrapping can be used to generate confidence intervals
around those estimates. 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.

Authors@R: c(person("Michael", "Belitz", email = "mbelitz@ufl.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-8162-5998")),
person("Caitlin", "Campbell", email = "caitjcampbell@gmail.com", role = "ctb", comment = c(ORCID = "0000-0002-8199-7775")),
person("Daijiang", "Li", email = "daijianglee@gmail.com", role = "ctb", comment = c(ORCID = "0000-0002-0925-3421")))
<doi:10.1093/biomet/66.2.367>. Extensive testing against other
generating more accurate and less biased estimates of onset and offset
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.
Author: Michael Belitz [aut, cre] (<https://orcid.org/0000-0002-8162-5998>),