Add references to documentation

.Rbuildignore

@@ -1,3 +1,5 @@
+^Meta$
+^doc$
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^\vignettes$

.gitignore

@@ -1,3 +1,5 @@
+Meta
+doc
 # History files
 .Rhistory
 
@@ -22,4 +24,3 @@ tardis.f
 *.o
 *.so
 Makefile
-

NAMESPACE

@@ -81,6 +81,7 @@ import(rgdal)
 import(snow)
 import(zoo)
 importFrom(RColorBrewer,brewer.pal)
+importFrom(Rdpack,reprompt)
 importFrom(caret,createDataPartition)
 importFrom(dtw,asymmetric)
 importFrom(dtw,rabinerJuangStepPattern)

R/class-crossValidation.R

@@ -38,6 +38,11 @@
 #'  \item{\code{accuracy}:}{A list with the accuracy and other TWDTW information for each 
 #'  data partitions.}
 #' }
+#' 
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
 #'
 #' @examples 
 #' \dontrun{

R/class-twdtwAssessment.R

@@ -42,6 +42,11 @@
 #' If the twdtwRaster is unprojected (longitude/latitude) the estimated area is the sum of the approximate 
 #' surface area in km2 of each cell (pixel). If the twdtwRaster is projected the estimated area is calculated 
 #' using the the pixel resolution in the map unit.
+#' 
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
 #'
 NULL
 setClass(

R/class-twdtwMatches.R

@@ -67,6 +67,11 @@
 #' \code{\link[dtwSat]{twdtwTimeSeries-class}}, and 
 #' \code{\link[dtwSat]{twdtwRaster-class}}
 #' 
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#' 
 #' @examples 
 #' ts = twdtwTimeSeries(timeseries=MOD13Q1.ts.list)
 #' patterns = twdtwTimeSeries(timeseries=MOD13Q1.patterns.list)

R/class-twdtwRaster.R

@@ -76,6 +76,11 @@
 #' \code{\link[dtwSat]{twdtwMatches-class}}, and 
 #' \code{\link[dtwSat]{twdtwTimeSeries-class}}
 #'
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#'   
 #' @examples 
 #' # Creating a new object of class twdtwTimeSeries 
 #' evi = brick(system.file("lucc_MT/data/evi.tif", package="dtwSat"))

R/class-twdtwTimeSeries.R

@@ -37,6 +37,11 @@
 #' \code{\link[dtwSat]{getTimeSeries}}, and
 #' \code{\link[dtwSat]{twdtwApply}}
 #'
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#'
 #' @examples 
 #' # Creating a new object of class twdtwTimeSeries  
 #' ptt = new("twdtwTimeSeries", timeseries = MOD13Q1.patterns.list, 

R/createPatterns.R

@@ -56,6 +56,11 @@
 #' \code{\link[dtwSat]{getTimeSeries}}, and 
 #' \code{\link[dtwSat]{twdtwApply}}
 #' 
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#' 
 #' @export
 setGeneric("createPatterns", function(x, from=NULL, to=NULL, freq=1, attr=NULL, split=TRUE, formula, ...) standardGeneric("createPatterns"))
 

R/data.R

@@ -22,7 +22,8 @@
 #' 
 #' @description This dataset has a list of patterns with the phenological cycle of: Soybean,
 #' Cotton, and Maize. These time series are based on the MODIS product 
-#' MOD13Q1 250 m 16 days [1]. The patterns were built from ground truth samples of each 
+#' MOD13Q1 250 m 16 days \insertCite{Didan:2015}{dtwSat}. The patterns were built 
+#' from ground truth samples of each 
 #' crop using Generalized Additive Models (GAM), see \link[dtwSat]{createPatterns}.
 #' 
 #' @docType data
@@ -36,13 +37,15 @@
 #' \link[dtwSat]{MOD13Q1.ts.list}, and 
 #' \link[dtwSat]{createPatterns}.
 #'  
-#' @seealso MOD13Q1 documentation: See 
-#' \url{https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1_v006}.
+#' @seealso For details about MOD13Q1 see \insertCite{Didan:2015}{dtwSat}.
 #' 
 #' @references 
-#' [1] Friedl MA, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang X. (2010).
-#' MODIS Collection 5 global land cover: Algorithm refinements and characterization of new
-#' datasets. Remote Sensing of Environment, 114(1), 168 182.
+#'   \insertAllCited{}
+#'   
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+
 #' 
 "MOD13Q1.patterns.list"
 
@@ -51,7 +54,8 @@
 #' @author Victor Maus, \email{vwmaus1@@gmail.com}
 #' 
 #' @description This dataset has a time series based on the 
-#' MODIS product MOD13Q1 250 m 16 days [1]. It is an irregularly sampled time series 
+#' MODIS product MOD13Q1 250 m 16 days \insertCite{Didan:2015}{dtwSat}. 
+#' It is an irregularly sampled time series 
 #' using the real date of each pixel from ''2009-08-05'' to ''2013-07-31''.
 #' 
 #' @docType data
@@ -64,13 +68,15 @@
 #' \link[dtwSat]{MOD13Q1.patterns.list}. 
 #' 
 #' 
-#' @seealso MOD13Q1 documentation: 
-#' \url{https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1_v006}.
+#' @seealso For details about MOD13Q1 see \insertCite{Didan:2015}{dtwSat}.
 #' 
 #' @references 
-#' [1] Friedl MA, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang X. (2010).
-#' MODIS Collection 5 global land cover: Algorithm refinements and characterization of new
-#' datasets. Remote Sensing of Environment, 114(1), 168 182.
+#'   \insertAllCited{}
+#'   
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+
 #' 
 "MOD13Q1.ts"
 
@@ -93,7 +99,8 @@
 #' @author Victor Maus, \email{vwmaus1@@gmail.com}
 #' 
 #' @description This dataset has a list of time series based on the 
-#' MODIS product MOD13Q1 250 m 16 days [1]. It is an irregularly sampled time series 
+#' MODIS product MOD13Q1 250 m 16 days \insertCite{Didan:2015}{dtwSat}. 
+#' It is an irregularly sampled time series 
 #' using the real date of each pixel from ''2009-08-05'' to ''2013-07-31''.
 #' 
 #' @docType data
@@ -106,36 +113,39 @@
 #' \link[dtwSat]{MOD13Q1.patterns.list}. 
 #' 
 #' 
-#' @seealso MOD13Q1 documentation: 
-#' \url{https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1_v006}.
+#' @seealso For details about MOD13Q1 see \insertCite{Didan:2015}{dtwSat}.
 #' 
 #' @references 
-#' [1] Friedl MA, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang X. (2010).
-#' MODIS Collection 5 global land cover: Algorithm refinements and characterization of new
-#' datasets. Remote Sensing of Environment, 114(1), 168 182.
+#'   \insertAllCited{}
+#'   
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+
 #' 
 "MOD13Q1.ts.list"
 
-
 #' @title Data: Pattern time series
 #' @author Victor Maus, \email{vwmaus1@@gmail.com}
 #' 
 #' @description This dataset has a list of patterns with the phenological cycle of: Water,
 #' Cotton-Fallow, Forest, Low vegetation, Pasture, Soybean-Cotton, Soybean-Maize, Soybean-Millet, 
 #' Soybean-Sunflower, and Wetland. These time series are based on the MODIS product 
-#' MOD13Q1 250 m 16 days [1]. The patterns were built from ground truth samples of each 
+#' MOD13Q1 250 m 16 days \insertCite{Didan:2015}{dtwSat}. 
+#' The patterns were built from ground truth samples of each 
 #' crop using Generalized Additive Models (GAM), see \link[dtwSat]{createPatterns}.
 #' 
 #' @docType data
 #' @format A \link[dtwSat]{twdtwTimeSeries} object.
 #' 
-#' @seealso MOD13Q1 documentation: See 
-#' \url{https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1_v006}.
+#' @seealso For details about MOD13Q1 see \insertCite{Didan:2015}{dtwSat}.
 #' 
 #' @references 
-#' [1] Friedl MA, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang X. (2010).
-#' MODIS Collection 5 global land cover: Algorithm refinements and characterization of new
-#' datasets. Remote Sensing of Environment, 114(1), 168 182.
+#'   \insertAllCited{}
+#'   
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
 #' 
 "MOD13Q1.MT.yearly.patterns"
 

R/dwtSat.R

@@ -17,7 +17,8 @@
 #' @author Victor Maus, \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 [1]. 
+#' (TWDTW) method for land use and land cover mapping using satellite image time series 
+#' \insertCite{Maus:2016,Maus:2019}{dtwSat}. 
 #' 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 
@@ -26,12 +27,8 @@
 #' for satellite datasets, visualize the results of the time series analysis, produce 
 #' land cover maps, and create temporal plots for land cover change analysis.
 #' 
-#'
 #' @references 
-#' [1] Maus  V,  Camara  G,  Cartaxo  R,  Sanchez  A,  Ramos  FM,  de Queiroz, GR.
-#' (2016). A Time-Weighted Dynamic Time Warping method for land use and land cover 
-#' mapping. Selected Topics in Applied Earth Observations and Remote Sensing, 
-#' IEEE Journal of, vol.PP, no.99, pp.1-11.
+#'   \insertAllCited{}
 #' 
 #' @seealso \code{\link[dtwSat]{twdtwApply}}
 #' 

R/getInternals.R

@@ -42,7 +42,13 @@ setGeneric("getMatches", function(object, timeseries.labels=NULL, patterns.label
 #' getInternals(mat)
 #'
 #' @return a list with TWDTW results or an object \code{\link[dtwSat]{twdtwTimeSeries-class}}. 
-#'
+#' 
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#'   
+#'   
 NULL
 
 #' @aliases getAlignments

R/getTimeSeries.R

@@ -51,6 +51,11 @@ setGeneric("getPatterns", function(object, ...) standardGeneric("getPatterns"))
 #'
 #' @return a list with TWDTW results or an object \code{\link[dtwSat]{twdtwTimeSeries-class}}. 
 #'
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#'   
 #' @examples
 #' # Getting time series from objects of class twdtwTimeSeries
 #' ts = twdtwTimeSeries(MOD13Q1.ts.list)

R/linearWeight.R

@@ -29,17 +29,15 @@
 #' can be passed to \code{\link[dtwSat]{twdtwApply}} through the argument \code{weight.fun}.
 #' This will add a time-weight to the dynamic time warping analysis. The time weight 
 #' creates a global constraint useful to analyse time series with phenological cycles
-#' of vegetation that are usually bound to seasons. In previous studies by [1] the 
-#' logistic weight had better results than the linear for land cover classification. 
-#' See [1] for details about the method. 
+#' of vegetation that are usually bound to seasons. In previous studies by 
+#' \insertCite{Maus:2016;textual}{dtwSat} the logistic weight had better results than the 
+#' linear for land cover classification. See \insertCite{Maus:2016;textual}{dtwSat} and 
+#' \insertCite{Maus:2019;textual}{dtwSat}.
 #' 
 #' @seealso \code{\link[dtwSat]{twdtwApply}}
 #' 
 #' @references 
-#' [1] Maus  V,  Camara  G,  Cartaxo  R,  Sanchez  A,  Ramos  FM,  de Queiroz, GR.
-#' (2016). A Time-Weighted Dynamic Time Warping method for land use and land cover 
-#' mapping. Selected Topics in Applied Earth Observations and Remote Sensing, 
-#' IEEE Journal of, vol.PP, no.99, pp.1-11.
+#'   \insertAllCited{}
 #' 
 #' @examples
 #' lin_fun = linearWeight(a=0.1)

R/logisticWeight.R

@@ -29,17 +29,15 @@
 #' can be passed to \code{\link[dtwSat]{twdtwApply}} through the argument \code{weight.fun}. 
 #' This will add a time-weight to the dynamic time warping analysis. The time weight 
 #' creates a global constraint useful to analyse time series with phenological cycles
-#' of vegetation that are usually bound to seasons. In previous studies by [1] the 
-#' logistic weight had better results than the linear for land cover classification. 
-#' See [1] for details about the method. 
+#' of vegetation that are usually bound to seasons. In previous studies by 
+#' \insertCite{Maus:2016;textual}{dtwSat} the logistic weight had better results than the 
+#' linear for land cover classification. See \insertCite{Maus:2016;textual}{dtwSat} and 
+#' \insertCite{Maus:2019;textual}{dtwSat}.
 #' 
 #' @seealso \code{\link[dtwSat]{twdtwApply}}
 #' 
 #' @references 
-#' [1] Maus  V,  Camara  G,  Cartaxo  R,  Sanchez  A,  Ramos  FM,  de Queiroz, GR.
-#' (2016). A Time-Weighted Dynamic Time Warping method for land use and land cover 
-#' mapping. Selected Topics in Applied Earth Observations and Remote Sensing, 
-#' IEEE Journal of, vol.PP, no.99, pp.1-11.
+#'   \insertAllCited{}
 #' 
 #' @examples
 #' log_fun = logisticWeight(alpha=-0.1, beta=100)

R/miscellaneous.R

@@ -15,6 +15,11 @@
 #' 
 #' @seealso \link[dtwSat]{shiftDates} 
 #' 
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#'   
 #' @examples
 #' year = c(2000, 2001)
 #' doy = c(366, 365)
@@ -48,11 +53,22 @@ getDatesFromDOY = function(year, doy){
 #'
 #' @return An object of the same class as the input \code{object}. 
 #'
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#'   
 #' @export
 setGeneric("shiftDates", function(object, year=NULL) standardGeneric("shiftDates"))
 
 #' @rdname shiftDates
 #' @aliases shiftDates-twdtwTimeSeries
+#' 
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#'   
 #' @examples
 #' patt = twdtwTimeSeries(MOD13Q1.patterns.list)
 #' npatt = shiftDates(patt, year=2005)

R/plotAccuracy.R

@@ -28,6 +28,11 @@
 #' @seealso 
 #' \code{\link[dtwSat]{twdtwAssessment}} and \code{\link[dtwSat]{twdtwAssess}}
 #'  
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#'   
 #' @examples
 #' \dontrun{
 #' 

R/plotAdjustedArea.R

@@ -10,6 +10,11 @@
 #' @seealso 
 #' \code{\link[dtwSat]{twdtwAssessment}} and \code{\link[dtwSat]{twdtwAssess}}
 #'  
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#'   
 #' @examples
 #' \dontrun{
 #' 

R/plotAlignments.R

@@ -40,6 +40,11 @@
 #' \code{\link[dtwSat]{plotMatches}}, and
 #' \code{\link[dtwSat]{plotClassification}}.
 #' 
+#' @references
+#'   \insertRef{Maus:2019}{dtwSat}
+#'   
+#'   \insertRef{Maus:2016}{dtwSat}
+#'   
 #' @examples
 #' log_fun = logisticWeight(-0.1, 100)
 #' ts = twdtwTimeSeries(MOD13Q1.ts.list)