Merge branch 'devel'

DESCRIPTION

@@ -17,8 +17,8 @@ Suggests:
     ecomsUDG.Raccess,
 Type: Package
 Title: Climate data manipulation and statistical downscaling
-Version: 0.8-0
-Date: 26-Jun-2015
+Version: 0.8-1
+Date: 29-Jun-2015
 Authors@R: as.person(c(
     "Joaquin Bedia <[email protected]> [ctb, cre]",
     "Antonio Cofino <[email protected]> [ctb]",

NEWS

@@ -1,7 +1,6 @@
-downscaleR 0.8-0
+downscaleR 0.8-1
 ================
 
-* New Generalized Quantile Mapping method (Gutjahr and Heinemann 2013)
-* New extrapolation feature of Quantile-quantile mapping method for unprecedent values in the simulation period
-* Minor bug fixes and documentation improvements
+* Bug fix in retrieval of annual seasons (Jan-Dec)
+* Other minor bug fixes and documentation improvements
 

R/biasCorrection.R

@@ -26,12 +26,14 @@
 #' 
 #' This method consists on adding to the observations the mean change signal (delta method).
 #' This method is applicable to any kind of variable but it is preferable to avoid it for bounded variables
-#'  (e.g. precipitation, wind speed, etc.) because values out of the variable range could be obtained (e.g. negative wind speeds...).
+#'  (e.g. precipitation, wind speed, etc.) because values out of the variable range could be obtained
+#'   (e.g. negative wind speeds...).
 #' 
 #' \strong{Unbiasing}
 #' 
 #' This correction consists on adding to the simulation the mean diference between the observations 
-#' and the simulation in the train period. This method is preferably applicable to unbounded variables (e.g. temperature).
+#' and the simulation in the train period. This method is preferably applicable to unbounded
+#'  variables (e.g. temperature).
 #' 
 #' \strong{Scaling}
 #' 
@@ -54,7 +56,7 @@
 #'\strong{Generalized Quantile Mapping (gqm)}
 #'  
 #' This method is described in Gutjahr and Heinemann 2013. It is applicable only to precipitation and is similar to the Piani method. It applies a 
-#' gamma distribution to values under the threshold given by the 95th percentile (proosed by Yang et al. 2010) and a general Pareto 
+#' gamma distribution to values under the threshold given by the 95th percentile (following Yang et al. 2010) and a general Pareto 
 #' distribution (GPD) to values above the threshold.  
 #' 
 #' @seealso \code{\link{isimip}} for a trend-preserving method of model calibration

R/getTimeDomain.R

@@ -107,7 +107,7 @@ getTimeDomain <- function(grid, dic, season, years, time, aggr.d, aggr.m) {
 	            }
             } else {
                   dateSliceList <- lapply(unique(timeDates$year), function(x) timeDates[which(timeDates$year == x)])
-                  timeIndList <- lapply(unique(timeDates$year), function(x) timeInd[which(timeDates$year == x)])
+                  timeIndList <- lapply(unique(timeDates$year), function(x) timeInd[which(timeDates$year == x)] - 1)
             }	     
       } else {
 	      timeIndList[[1]] <- timeInd - 1

R/loadGridData.R

@@ -95,7 +95,7 @@ loadGridData <- function(dataset, var, dictionary = TRUE, lonLim = NULL,
             dic <- dictionaryLookup(dicPath, var, time)
             shortName <- dic$short_name          
       }
-      if (min(season) < 1 | max(season) > 12) {
+      if (!is.null(season) & (min(season) < 1 | max(season) > 12)) {
             stop("Invalid season definition")
       }
       gds <- J("ucar.nc2.dt.grid.GridDataset")$open(dataset)

man/biasCorrection.Rd

@@ -49,12 +49,14 @@ Next we make a brief description of each method:
 
 This method consists on adding to the observations the mean change signal (delta method).
 This method is applicable to any kind of variable but it is preferable to avoid it for bounded variables
- (e.g. precipitation, wind speed, etc.) because values out of the variable range could be obtained (e.g. negative wind speeds...).
+ (e.g. precipitation, wind speed, etc.) because values out of the variable range could be obtained
+  (e.g. negative wind speeds...).
 
 \strong{Unbiasing}
 
 This correction consists on adding to the simulation the mean diference between the observations
-and the simulation in the train period. This method is preferably applicable to unbounded variables (e.g. temperature).
+and the simulation in the train period. This method is preferably applicable to unbounded
+ variables (e.g. temperature).
 
 \strong{Scaling}
 
@@ -77,7 +79,7 @@ This method is described in Piani et al. 2010 and is applicable only to precipit
 \strong{Generalized Quantile Mapping (gqm)}
 
 This method is described in Gutjahr and Heinemann 2013. It is applicable only to precipitation and is similar to the Piani method. It applies a
-gamma distribution to values under the threshold given by the 95th percentile (proosed by Yang et al. 2010) and a general Pareto
+gamma distribution to values under the threshold given by the 95th percentile (following Yang et al. 2010) and a general Pareto
 distribution (GPD) to values above the threshold.
 }
 \examples{