Description: We provide a set of functions to retrieve information about GIMMS
NDVI3g files currently available online; download and re-arrange the bi-monthly
datasets according to creation time; import downloaded files from native binary
(ENVI) format directly into R based on the widely applied 'raster' package;
calculate monthly value composites (e.g. maximum value composites, MVC) from the
bi-monthly input data; and derive long-term monotonous trends from the
Mann-Kendall trend test, optionally featuring pre-whitening to

person("Florian", "Detsch", email = "admin@environmentalinformatics-marburg.de", role = c("cre", "aut"))
Imports: parallel, doParallel, foreach, Kendall, zyp

Description: We provide a set of functions to retrieve information about GIMMS NDVI3g files currently available online; download and re-arrange the bi-monthly datasets according to creation time; import downloaded files from native binary (ENVI) format directly into R based on the widely applied 'raster' package; extract accompanying quality flags and perform quality control; calculate monthly value composites (e.g. maximum value composites, MVC) from the bi-monthly input data; and derive long-term monotonous trends from the Mann-Kendall trend test, optionally featuring pre-whitening to account for lag-1 autocorrelation.

person("Florian", "Detsch", email = "florian.detsch@staff.uni-marburg.de", role = c("cre", "aut"))
Description: We provide a set of functions to retrieve information about GIMMS NDVI3g files currently available online; download (and re-arrange, in the case of NDVI3g.v0) the half-monthly data sets; import downloaded files from ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based on the widespread 'raster' package; conduct quality control; and generate monthly composites (e.g., maximum values) from the half-monthly input data. As a special gimmick, a method is included to conveniently apply the Mann-Kendall trend test upon 'Raster*' images, optionally featuring trend-free pre-whitening to account for lag-1 autocorrelation.