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.

Provides some easy-to-use functions for time series
        analyses of (plant-) phenological data sets. These functions
        mainly deal with the estimation of combined phenological time
        series and are usually wrappers for functions that are already
        implemented in other R packages adapted to the special
        structure of phenological data and the needs of phenologists.
        Some date conversion functions to handle Julian dates are also
        provided.

Programmatic interface to the 'PhenoCam' web services (<https://phenocam.nau.edu/webcam>).
    Allows for easy downloading of 'PhenoCam' data directly to your R workspace
    or your computer and provides post-processing routines for consistent and easy
    timeseries outlier detection, smoothing and estimation of phenological transition dates.
    Methods for this package are described in detail in Hufkens et. al (2018) <doi:10.1111/2041-210X.12970>.