time series with a set of known temporal patterns (e.g. phenological cycles
associated with the vegetation). Using 'dtwSat' the user can build temporal
patterns for land cover types, apply the TWDTW analysis for satellite datasets,
visualize the results of the time series analysis, produce land cover maps, and
Depends: R (>= 3.2.0), zoo, raster, ggplot2
Imports: methods, rgdal, dtw, proxy, scales, reshape2, grDevices,
RColorBrewer, plyr, stats, parallel, sp, lubridate, caret, mgcv

Authors@R: c(person('Victor', 'Maus', role = c('aut', 'cre'), email = 'vwmaus1@gmail.com'),
person('Marius', 'Appel', role = c('ctb')),
person('Toni',   'Giorgino', role = c('ctb')))
Warping (TWDTW) method for land cover mapping using satellite image time series.
TWDTW is based on the Dynamic Time Warping technique and has achieved high
accuracy for land cover classification using satellite data. The method is
based on comparing unclassified satellite image time series with a set of known
temporal patterns (e.g. phenological cycles associated with the vegetation).
Using 'dtwSat' the user can build temporal patterns for land cover types, apply
the TWDTW analysis for satellite datasets, visualize the results of the time
series analysis, produce land cover maps, create temporal plots for land cover
change, and compute accuracy assessment metrics.
RColorBrewer, plyr, stats, sp, lubridate, caret, mgcv, xtable

Authors@R: c(person('Victor', 'Maus', role = c('aut', 'cre'), email = 'vwmaus1@gmail.com', comment = c(ORCID = "0000-0002-7385-4723")),
Depends: R (>= 3.2.0), zoo, raster, snow, ggplot2
Suggests: knitr, rmarkdown, rticles, gridExtra, grid, png, Hmisc,
Author: Victor Maus [aut, cre] (<https://orcid.org/0000-0002-7385-4723>),

person('Nikolas', 'Kuschnig', role = c('ctb')),
RColorBrewer, plyr, stats, sp, lubridate, caret, mgcv, xtable,

person('Nikolas', 'Kuschnig', role = c('ctb'), comment = c(ORCID = "0000-0002-6642-2543")),
TWDTW compares unclassified satellite image time series with a set of known
Suggests: gridExtra, grid, png, Hmisc, rbenchmark