Warping (TWDTW) method for land cover mapping. TWDTW computes the similarity between
satellite image time series with a set of known temporal patterns
(e.g. phenological cycles of the vegetation).
'dtwSat' offers the user methods to create temporal patterns for land cover types,
perform TWDTW analysis for satellite datasets, visualize the results of the analysis,
produce land cover maps, create temporal plots for land cover change, and compute
Depends: R (>= 3.5.0), zoo, raster, ggplot2
Suggests: gridExtra, grid, png, Hmisc, rbenchmark, doParallel, knitr,

Title: Time Series Clustering with Dynamic Time Warping
Imports: stats, methods, caTools, ggplot2, reshape2, modeltools
Description: Time series clustering using different techniques related to the Dynamic Time Warping distance
and its corresponding lower bounds. Additionally, an implementation of k-Shape clustering is available.

Imports: stats, methods, caTools, reshape2, modeltools

Description: Time series clustering using different techniques related to the
Dynamic Time Warping distance and its corresponding lower bounds. Additionally,
an implementation of k-Shape clustering is available.

Depends: flexclust, proxy, dtw, ggplot2, foreach
Imports: stats, methods, caTools, reshape2, modeltools, doRNG, parallel

Title: Time Series Clustering with Dynamic Time Warping Distance
Depends: flexclust, proxy, dtw, ggplot2, doRNG
Imports: stats, methods, caTools, reshape2, foreach, parallel
an implementation of k-Shape clustering is available, and the package can be
easily extended with custom distance measures and centroid definitions.
Collate: 'DBA.R' 'NCCc.R' 'SBD.R' 'TADPole.R' 'all_cent.R' 'dtw-lb.R'

Title: Time Series Clustering with Optimizations for the Dynamic Time
Imports: stats, utils, methods, flexclust, caTools, reshape2, foreach,
Suggests: TSdist, TSclust, doParallel, testthat
Description: Time series clustering with optimized techniques related to the
Dynamic Time Warping distance and its corresponding lower bounds.
Implementations of k-Shape, TADPole and fuzzy clustering are available.
The functionality can be easily extended with custom distance measures
Collate: 'DBA.R' 'NCCc.R' 'SBD.R' 'TADPole.R' 'all_cent.R' 'ddist.R'