Suggests: knitr, markdown, qpdf, rmarkdown

Title: Spatial Watershed Aggregation and Spatial Drainage Network
Maintainer: J. A. Torres <arturo.torres@uni-muenster.de>
Description: Methods for watersheds aggregation and spatial drainage network analysis.
Depends: R (>= 2.10), methods, sp, maptools, rgeos, lattice, splancs

Maintainer: J. A. Torres-Matallana <arturo.torres@list.lu>
Packaged: 2016-02-08 14:03:40 UTC; arturo.torres

Description: Wrap-around Time Series (WATS) plots for interrupted time series
"wibeasley@hotmail.com"), person("Joe", "Rodgers", role = "aut", email =
"joseph.l.rodgers@vanderbilt.edu"), person("Matthew", "Schuelke", role =
"ctb", email = "schuelkem@slu.edu"), person("Ronnie",
"Coleman", role = "ctb"), person("Mark Joseph", "Lachowicz", role =
Author: Will Beasley [aut, cre], Joe Rodgers [aut], Matthew Schuelke [ctb],
Ronnie Coleman [ctb], Mark Joseph Lachowicz [ctb]
Imports: colorspace, ggplot2, grid, lubridate, plyr, RColorBrewer,
Suggests: BayesSingleSub, boot, devtools, knitr, scales, testthat

Description: Wrap-around Time Series (WATS) Plots for Interrupted Time Series
"ctb", email = "matthew.schuelke.ctr@us.af.mil"), person("Ronnie",

Imports: boot, colorspace, ggplot2, grid, lubridate, plyr,
Suggests: BayesSingleSub, devtools, knitr, testthat

Title: Wavelet change point detection for array CGH data
Depends: R (>= 2.0.0), waveslim, lattice

Title: Wavelet Changepoint Detection for Array CGH
Depends: R (>= 2.0.0), waveslim, lattice, leaps, FitAR, qvalue
Description: Detects changepoints and finds gain/loss regions

Title: Estimating Long Memory Parameter using Wavelet
Author: Sandipan Samanta and Dr. Ranjit Kumar Paul
Description: Estimation of the long memory parameter using wavelets. Other estimation techniques like
and Semiparametric methods(Robinson, P. M.,1995, <DOI:10.1214/aos/1176324317>) also have included.
Packaged: 2016-12-12 07:09:06 UTC; Samanta Sandipan

Packaged: 2017-05-11 07:06:56 UTC; ranjitstat

Packaged: 2017-10-23 10:54:22 UTC; ranjitstat

Title: Weakly Associated Vectors (WAVE) Sampling
person("Raphaël", "Jauslin", email = "raphael.jauslin@unine.ch",role = c("aut", "cre"),
Description: Spatial data are generally auto-correlated, meaning that if two
units selected are close to each other, then it is likely that they share the
same properties. For this reason, when sampling in the population it is often
needed that the sample is well spread over space. A new method to draw a sample
from a population with spatial coordinates is proposed. This method is called
wave (Weakly Associated Vectors) sampling. It uses the less correlated vector
to a spatial weights matrix to update the inclusion probabilities vector
Suggests: knitr, rmarkdown, ggplot2, ggvoronoi, sampling,
Author: Raphaël Jauslin [aut, cre] (<https://orcid.org/0000-0003-1088-3356>),