distance) mapping between two time series. This package
implements all common DTW variants, including local and
global constraints, arbitrary timeseries lenghts,
distance definitions, etc.  Methods provides cumulative
distance, warping functions, plots, etc.
Packaged: Mon Dec 31 14:57:48 2007; toni

(DTW) algorithms in R.  DTW computes the optimal (least cumulative
distance) alignment between points of two time series.  Common DTW
variants covered include local (slope) and global (window)
constraints, subsequence matches, arbitrary distance definitions,
normalizations, minimum variance matching, and so on.  Provides
cumulative distances, alignments, specialized plot styles, etc.,
as described in Giorgino (2009) <doi:10.18637/jss.v031.i07>.

mapping between two time series. This package implements all common
DTW variants, including local and global constraints, arbitrary
timeseries lenghts, distance definitions, etc.  Methods provides
cumulative distance, warping functions, plots, etc.
Packaged: Tue Jan  8 19:30:08 2008; toni

Packaged: Fri Feb 22 17:27:21 2008; toni

Packaged: Thu May 29 16:00:52 2008; toni

Packaged: Fri Jun 20 14:13:17 2008; toni

Title: Time-Weighted Dynamic Time Warping for Remote Sensing Time
Authors@R: c(person("Victor", "Maus", role = c("aut", "cre"),
Provides a Time-Weighted Dynamic Time Warping (TWDTW) algorithm to measure similarity between two temporal sequences. This adaptation of the classical Dynamic Time Warping (DTW) algorithm is flexible to compare events that have a strong time dependency, such as phenological stages of cropland systems and tropical forests. This package provides methods for visualization of minimum cost paths, time series alignment, and time intervals classification.
Depends: R (>= 3.0.0), methods, dtw, zoo
Imports: proxy, waveslim, graphics, ggplot2, scales, reshape2,

Title: Time-Weighted Dynamic Time Warping for Satellite Image Time
Authors@R: c(person('Victor', 'Maus', role = c('aut', 'cre'), email = 'vwmaus1@gmail.com'))
Description: Provides an implementation of the Time-Weighted Dynamic Time
Warping (TWDTW) method for land use and land cover mapping using satellite
image time series. TWDTW is based on the Dynamic Time Warping technique and
has achieved high accuracy for land use and 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, and
create temporal plots for land cover change analysis.
Depends: R (>= 3.0.0), zoo, raster, ggplot2, methods
Imports: dtw, proxy, scales, reshape2, grDevices, RColorBrewer, plyr,
stats, parallel, sp, lubridate, caret, mgcv
Suggests: testthat, knitr, rmarkdown, rticles, gridExtra, grid, png,