Title: Chronomics Analysis Toolkit (CAT): Periodicity Analysis
Description: Calculates auto- and cross-correlation functions and plots an actogram and a smoothing function from a time series to identify and visualize periodic components. Tests presence of anticipated rhythm and estimates rhythm parameters; fits model consisting of multiple rhythmic components to data; performs least squares spectral analysis and other cosinor-based analyses, including population-mean cosinor (pmc).

grDevices, signal, assertr, CombMSC, Hmisc
Description: Calculates auto- and cross-correlation functions and plots an actogram and a smoothing function from a time series to identify and visualize periodic components. Tests presence of anticipated rhythm and estimates rhythm parameters; fits model consisting of multiple rhythmic components to data; performs least squares spectral analysis and other cosinor-based analyses, including population-mean cosinor (PMC) and population-mean cosinor parameter tests (PMCtest).

Title: Cambial Activity and wood formation data processing,
Description: Allows processing, visualisation and analysis of data
coming from cambial activity and wood formation monitoring
Packaged: 2010-03-11 13:08:20 UTC; cyrillerathgeber

Packaged: 2010-07-19 09:58:24 UTC; cyrillerathgeber

Packaged: 2011-01-04 16:44:38 UTC; cyrillerathgeber

Title: Collection of Functions for Processing, Visualizing and
Analyzing Cambial Activity and Wood Formation Monitoring Data
Authors@R: c(person("Cyrille", "Rathgeber", role=c("aut", "cre"),
Packaged: 2012-01-16 09:35:50 UTC; cyrillerathgeber

Authors@R: person("Ruben", "Remelgado", role = c("aut", "cre"), email="remelgado.ruben@gmail.com")
Description: Tools to process ground-truth data on crop types and perform a phenology based crop type classification. These tools were developed in the scope of the CAWa project and extend on the work of Conrad et al. (2011) <doi:10.1080/01431161.2010.550647>. Moreover, they introduce an innovative classification and validation scheme that utilizes spatially independent samples as proposed by Remelgado et al. (2017) <doi:10.1002/rse2.70>.
Imports: raster, sp, rgdal, ggplot2, grDevices, spatialEco, rgeos,
Suggests: knitr, rmarkdown, kableExtra, imager, lattice