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.

Provides some easy-to-use functions for 
	spatial analyses of (plant-) phenological data 
	sets and satellite observations of vegetation.

Provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) <doi:10.1086/676622> to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984) <doi:10.1007/BF00275224>, bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices. Further details can be found in Torices & Muñoz-Pajares <doi:10.3732/apps.1400104>.

Provides functions for phenological data preprocessing, modelling and result handling. For more information, please refer to Lange et al. (2016) <doi:10.1007/s00484-016-1161-8>.

Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) <http://mural.maynoothuniversity.ie/view/ethesisauthor/Pollard=3ACiaran_P=2E=3A=3A.html>. A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour.