diff --git a/DESCRIPTION b/DESCRIPTION
index 59fad67a128540b694134a4d5ea64e9439aacabd..0a0e00a7b5d251bb4bc10f25a32bb9a05feb703b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -36,6 +36,7 @@ Imports:
     s2dverification,
     rainfarmr,
     multiApply,
+    ClimProjDiags,
     ncdf4,
     plyr,
     abind,
diff --git a/NAMESPACE b/NAMESPACE
index 724d8fc506e9aa241dc0e06ac9520dd9ee368ce5..8067b1e25c11744b1458a83760de00da86b35bf5 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,5 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
+export(Analogs)
+export(CST_Analogs)
 export(BEI_PDFBest)
 export(BEI_Weights)
 export(CST_Anomaly)
@@ -18,6 +20,7 @@ export(PlotForecastPDF)
 export(PlotMostLikelyQuantileMap)
 export(RFSlope)
 export(RainFARM)
+import(ClimProjDiags)
 export(as.s2dv_cube)
 export(s2dv_cube)
 import(abind)
diff --git a/R/CST_Analogs.R b/R/CST_Analogs.R
new file mode 100644
index 0000000000000000000000000000000000000000..0127b10fc6d078a306ae1d8c7a1a5ff7e4d6a84d
--- /dev/null
+++ b/R/CST_Analogs.R
@@ -0,0 +1,840 @@
+#'@rdname CST_Analogs
+#'@title Downscaling using Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields in the local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height 
+#'(Yiou et al, 2013) but the function gives the possibility to use another 
+#'field. The local scale will be usually given by precipitation or temperature 
+#'fields, but might be another variable.The analogs function will find the best
+#'analogs based in three criterias:
+#' (1) Minimal distance in the large scale pattern (i.e. SLP)
+#' (2) Minimal distance in the large scale pattern (i.e. SLP) and minimal 
+#' distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimal distance in the large scale pattern (i.e. SLP), minimal 
+#' distance in the local scale pattern (i.e. SLP) and maxima correlation in the 
+#' local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. This is 
+#'important since it is necessary to have a good representation of the 
+#'possible states of the field in the past, and therefore, to get better 
+#'analogs. Once the search of the analogs is complete, and in order to used 
+#'the three criterias the user can select a number of analogs, using parameter
+#''nAnalogs' to restrict the selection of the best analogs in a short number 
+#'of posibilities, the best ones.
+#'This function has not constrains of specific regions, variables to downscale,
+#'or data to be used (seasonal forecast data, climate projections data, 
+#'reanalyses data). The regrid into a finner scale is done interpolating with 
+#'CST_Load. Then, this interpolation is corrected selecting the analogs in the 
+#'large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#' and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#' from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#' \email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an 's2dv_cube' object containing the experimental field on the 
+#'large scale for which the analog is aimed. This field is used to in all the
+#'criterias. If parameter 'expVar' is not provided, the function will return 
+#'the expL analog. The element 'data' in the 's2dv_cube' object must have, at
+#'least, latitudinal and longitudinal dimensions. The object is expect to be 
+#'already subset for the desired large scale region.
+#'@param obsL an 's2dv_cube' object containing the observational field on the
+#'large scale. The element 'data' in the 's2dv_cube' object must have the same 
+#'latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
+#'dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param expVar an 's2dv_cube' object containing the experimental field on the
+#'local scale, usually a different variable to the parameter 'expL'. If it is 
+#'not NULL (by default, NULL), the returned field by this function will be the 
+#'analog of parameter 'expVar'.
+#'@param obsVar an 's2dv_cube' containing the field of the same variable as the 
+#'passed in parameter 'expVar' for the same region.
+#'@param region a vector of length four indicating the minimum longitude, the 
+#'maximum longitude, the minimum latitude and the maximum latitude.
+#'@param criteria a character string indicating the criteria to be used for the 
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimal distance in the large scale pattern;
+#'\item{Local_dist} minimal distance in the large scale pattern and minimal 
+#' distance in the local scale pattern; and
+#'\item{Local_cor} minimal distance in the large scale pattern, minimal 
+#' distance in the local scale pattern and maxima correlation in the 
+#' local variable to downscale.}
+#' 
+#'@import multiApply
+#'@import ClimProjDiags
+#'@import abind
+#'
+#'@seealso code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
+#'\code{\link[s2dverification]{CDORemap}}
+#'
+#'@return An 's2dv_cube' object containing the dowscaled values of the best 
+#'analogs in the criteria selected. 
+#'
+#'@examples
+#'res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
+#'
+#'@export
+CST_Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
+                        region = NULL, criteria = "Large_dist") {
+  if (!inherits(expL, 's2dv_cube') || !inherits(obsL, 's2dv_cube')) {
+    stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
+         "as output by CSTools::CST_Load.")
+  }
+  if (!is.null(expVar) || !is.null(obsVar)) {
+    if (!inherits(expVar, 's2dv_cube') || !inherits(obsVar, 's2dv_cube')) {
+      stop("Parameter 'expVar' and 'obsVar' must be of the class 's2dv_cube',
+           ","as output by CSTools::CST_Load.")
+    }
+  }
+  timevector <- obsL$Dates$start
+  if (!is.null(expVar)) {
+    region <- c(min(expVar$lon), max(expVar$lon), min(expVar$lat), 
+                max(expVar$lon))
+    lonVar <- expVar$lon
+    latVar <- expVar$lat
+  } else {
+    region <- c(min(expL$lon), max(expL$lon), min(expL$lat), max(expL$lon))
+    lonVar <- expL$lon
+    latVar <- expL$lat
+  }      
+  result <- Analogs(expL$data, obsL$data, time_obsL = timevector, 
+                    expVar = expVar$data, obsVar = obsVar$data,
+                    criteria = criteria, 
+                    lonVar = expVar$lon, latVar = expVar$lat, 
+                    region = region, nAnalogs = 1, return_list = FALSE)
+  if (!is.null(obsVar)) {
+    obsVar$data <- result$AnalogsFields
+    return(obsVar)
+  } else {
+    obsL$data <- result$AnalogsFields
+    return(obsL)
+  }
+}
+#'@rdname Analogs
+#'@title Analogs based on large scale fields.
+#' 
+#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+#'        
+#'@description This function perform a downscaling using Analogs. To compute 
+#'the analogs, the function search for days with similar large scale conditions
+#'to downscaled fields in the local scale. The large scale and the local scale 
+#'regions are defined by the user. The large scale is usually given by 
+#'atmospheric circulation as sea level pressure or geopotential height (Yiou 
+#'et al, 2013) but the function gives the possibility to use another field. The
+#'local scale will be usually given by precipitation or temperature fields, but
+#'might be another variable. 
+#'The analogs function will find the best analogs based in three criterias:
+#' (1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+#' (2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
+#' and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
+#' (3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+#' distance in the local scale pattern (i.e. SLP) and highest correlation in the
+#' local variable to downscale (i.e Precipitation).
+#'The search of analogs must be done in the longest dataset posible. This is 
+#'important since it is necessary to have a good representation of the 
+#'possible states of the field in the past, and therefore, to get better 
+#'analogs. Once the search of the analogs is complete, and in order to used the 
+#'three criterias the user can select a number of analogs , using parameter 
+#''nAnalogs' to restrict 
+#'the selection of the best analogs in a short number of posibilities, the best
+#'ones. This function has not constrains of specific regions, variables to 
+#'downscale, or data to be used (seasonal forecast data, climate projections 
+#'data, reanalyses data). The regrid into a finner scale is done interpolating 
+#'with CST_Load. Then, this interpolation is corrected selecting the analogs in
+#'the large and local scale in based of the observations. The function is an 
+#'adapted version of the method of Yiou et al 2013.       
+#'
+#'@references Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+#'and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+#'from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+#'\email{pascal.yiou@lsce.ipsl.fr}
+#'
+#'@param expL an array of N named dimensions containing the experimental field
+#'on the large scale for which the analog is aimed. This field is used to in 
+#'all the criterias. If parameter 'expVar' is not provided, the function will
+#'return the expL analog. The element 'data' in the 's2dv_cube' object must 
+#'have, at least, latitudinal and longitudinal dimensions. The object is expect
+#'to be already subset for the desired large scale region.
+#'@param obsL an array of N named dimensions containing the observational field 
+#'on the large scale. The element 'data' in the 's2dv_cube' object must have 
+#'the same latitudinal and longitudinal dimensions as parameter 'expL' and a
+#' temporal dimension with the maximum number of available observations.
+#'@param time_obsL a character string indicating the date of the observations 
+#'in the format "dd/mm/yyyy"
+#'@param expVar an array of N named dimensions containing the experimental 
+#'field on the local scale, usually a different variable to the parameter 
+#''expL'. If it is not NULL (by default, NULL), the returned field by this 
+#'function will be the analog of parameter 'expVar'.
+#'@param obsVar an array of N named dimensions containing the field of the 
+#'same variable as the passed in parameter 'expVar' for the same region.
+#'@param criteria a character string indicating the criteria to be used for the
+#'selection of analogs:
+#'\itemize{
+#'\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
+#'\item{Local_dist} minimum Euclidean distance in the large scale pattern 
+#'and minimum Euclidean distance in the local scale pattern; and
+#'\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
+#'minimum Euclidean distance in the local scale pattern and highest correlation
+#' in the local variable to downscale.} 
+#'@param lonVar a vector containing the longitude of parameter 'expVar'.
+#'@param latVar a vector containing the latitude of parameter 'expVar'.
+#'@param region a vector of length four indicating the minimum longitude, 
+#'the maximum longitude, the minimum latitude and the maximum latitude.
+#'@param return_list TRUE to get a list with the best analogs. FALSE
+#'to get a single analog, the best analog. For Downscaling return_list must be
+#'FALSE. 
+#'@param nAnalogs number of Analogs to be selected to apply the criterias 
+#''Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
+#'that the user can get, but the number of events with minimum distance in 
+#'which perform the search of the best Analog. The default value for the 
+#''Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
+#' be selected by the user otherwise the default value will be set as 10. 
+#'@import multiApply
+#'@import ClimProjDiags
+#'@import abind
+#'@return AnalogsFields, dowscaled values of the best analogs for the criteria 
+#'selected.
+#'@return AnalogsInfo, a dataframe with information about the number of the 
+#'best analogs, the corresponding value of the metric used in the selected 
+#'criteria (distance values for Large_dist and Local_dist,correlation values 
+#'for Local_cor), date of the analog). The analogs are listed in decreasing 
+#'order, the first one is the best analog (i.e if the selected criteria 
+#'is Local_cor the best analog will be the one with highest correlation, while
+#'for Large_dist criteria the best analog will be the day with minimum 
+#'Euclidean distance)
+#'@examples
+#'require(zeallot)
+#'
+#'# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
+#'# The best analog is found using a single variable (i.e. Sea level pressure, 
+#'# SLP). The downscaling will be done in the same variable used to study the 
+#'# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
+#'# expL respectively region, lonVar and latVar not necessary here. 
+#'# return_list=FALSE 
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*1.2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP)
+#'str(downscale_field)
+#'
+#'# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
+#'# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
+#'# find the best analog day (defined in criteria 'Large_dist' as the day, in 
+#'# obsL, with the minimum Euclidean distance to the day of interest in expL) 
+#'# The second variable will be the variable to donwscale (i.e. precipitation, 
+#'# obsVar). Parameter obsVar must be different to obsL.The downscaled 
+#'# precipitation will be the precipitation that belongs to the best analog day
+#'# in SLP. Region not needed here since will be the same for both variables.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP)
+#'str(downscale_field)
+#'
+#'# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
+#'# variable: same as Example 1 but getting a list of best analogs (return_list
+#'# =TRUE) with the corresponding downscaled values, instead of only 1 single 
+#'# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
+#'# (number of analogs to do the search of the best Analogs). obsVar and expVar
+#'# NULL or equal to obsL and expL,respectively.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:1980),expSLP*1.5)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
+#'time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
+#'downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
+#'                          nAnalogs=5,return_list = TRUE)
+#'str(downscale_field)
+#'
+#'# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
+#'# same as example 2 but getting a list of best analogs (return_list =TRUE) 
+#'# with the values instead of only a single downscaled value. Imposing 
+#'# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
+#'# and expVar must be different to obsL and expL.
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+#'                           obsVar=obs.pr,
+#'                           time_obsL=time_obsSLP,nAnalogs=5,
+#'                           return_list = TRUE)
+#'str(downscale_field)
+#'
+#'# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
+#'# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
+#'# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
+#'# downscaled precipitation will be the precipitation that belongs to the best 
+#'# analog day in SLP. lonVar, latVar and Region must be given here to select 
+#'# the local scale
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       obsVar=obs.pr,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region, 
+#'                       nAnalogs = 10, return_list = FALSE)
+#'str(Local_scale)
+#'
+#'# Example 6: list of best analogs using criteria 'Local_dist' and 2 
+#'# variables:
+#'# The best analog is found using 2 variables. Parameter obsVar must be 
+#'# different to obsL in this case.The downscaled precipitation will be the 
+#'# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
+#'# and Region needed. return_list=TRUE
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       obsVar=obs.pr,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region, 
+#'                       nAnalogs = 5, return_list = TRUE)
+#'str(Local_scale)
+#'
+#'# Example 7: Downscaling using Local_dist criteria
+#'# without parameters obsVar and expVar. return list =FALSE 
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),region=region, 
+#'                       nAnalogs = 10, return_list = TRUE)
+#'str(Local_scale)
+#'
+#'# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
+#'# The best analog is found using 2 variables. Parameter obsVar and expVar 
+#'# are necessary and must be different to obsL and expL, respectively.
+#'# The downscaled precipitation will be the precipitation that belongs to
+#'# the best analog day in SLP large and local scales, and to the day with 
+#'# the higher correlation in precipitation. return_list=FALSE. two options 
+#'# for nAnalogs
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'exp.pr <- c(rnorm(1:20)*0.001)
+#'dim(exp.pr)=dim(expSLP)
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
+#'                          return_list = FALSE)
+#'Local_scalecor$AnalogsInfo
+#'Local_scalecor$DatesAnalogs
+#'# same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),region=region, 
+#'                          return_list = FALSE)
+#'Local_scalecor$AnalogsInfo
+#'Local_scalecor$DatesAnalogs
+#'
+#'# Example 9: List of best analogs in the three criterias Large_dist, 
+#'# Local_dist, and Local_cor return list TRUE same variable
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'# analogs of large scale using criteria 1
+#'Large_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Large_dist", 
+#'                       nAnalogs = 7, return_list = TRUE)
+#'str(Large_scale)
+#'Large_scale$AnalogsInfo
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                       return_list = TRUE)
+#'str(Local_scale)
+#'Local_scale$AnalogsInfo
+#'# analogs of local scale using criteria 3
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obsSLP,expVar=expSLP,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                          return_list = TRUE)
+#'str(Local_scalecor)
+#'Local_scalecor$AnalogsInfo
+#'
+#'# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
+#'# Local_cor return list FALSE, different variable
+#'
+#'expSLP <- rnorm(1:20)
+#'dim(expSLP) <- c(lat = 4, lon = 5)
+#'obsSLP <- c(rnorm(1:180),expSLP*2)
+#'dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+#'time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+#'exp.pr <- c(rnorm(1:20)*0.001)
+#'dim(exp.pr)=dim(expSLP)
+#'obs.pr <- c(rnorm(1:200)*0.001)
+#'dim(obs.pr)=dim(obsSLP)
+#'# analogs of large scale using criteria 1
+#'Large_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       criteria="Large_dist", 
+#'                       nAnalogs = 7, return_list = FALSE)
+#'str(Large_scale)
+#'Large_scale$AnalogsInfo
+#'# analogs of local scale using criteria 2
+#'lonmin=-1
+#'lonmax=2
+#'latmin=30
+#'latmax=33
+#'region=c(lonmin,lonmax,latmin,latmax)
+#'Local_scale <- Analogs(expL=expSLP,
+#'                       obsL=obsSLP, time_obsL=time_obsSLP,
+#'                       obsVar=obs.pr,
+#'                       criteria="Local_dist",lonVar=seq(-1,5,1.5),
+#'                       latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                       return_list = FALSE)
+#'str(Local_scale)
+#'Local_scale$AnalogsInfo
+#'# analogs of local scale using criteria 3
+#'Local_scalecor <- Analogs(expL=expSLP,
+#'                          obsL=obsSLP, time_obsL=time_obsSLP,
+#'                          obsVar=obs.pr,expVar=exp.pr,
+#'                          criteria="Local_cor",lonVar=seq(-1,5,1.5),
+#'                          latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+#'                          return_list = FALSE)
+#'str(Local_scalecor)
+#'Local_scalecor$AnalogsInfo
+#'
+#'@export 
+Analogs <- function(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL, 
+                    criteria = "Large_dist",
+                    lonVar = NULL, latVar = NULL, region = NULL, 
+                    nAnalogs = NULL, return_list = FALSE) {
+  # checks
+  if (!all(c('lon', 'lat') %in% names(dim(expL)))) {
+    stop("Parameter 'expL' must have the dimensions 'lat' and 'lon'.")
+  }
+  
+  if (!all(c('lat', 'lon') %in% names(dim(obsL)))) {
+    stop("Parameter 'obsL' must have the dimension 'lat' and 'lon'.")
+  }
+  
+  if (any(is.na(expL)))  {
+    warning("Parameter 'exp' contains NA values.")
+  }
+  
+  if (any(is.na(obsL))) {
+    warning("Parameter 'obs' contains NA values.")
+  }
+  if (!is.null(expVar) & is.null(obsVar)) {
+    expVar <- NULL
+    warning("Parameter 'expVar' is set to NULL as parameter 'obsVar', 
+            large scale field will be returned.")
+  }
+  if (is.null(expVar) & is.null(obsVar)) {
+    warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing 
+            same variable as obsL and expL'.")
+  }
+  if(!is.null(obsVar) & is.null(expVar) & criteria=="Local_cor"){
+    stop("parameter 'expVar' cannot be NULL")
+  }
+  if(is.null(nAnalogs) & criteria!="Large_dist"){
+    nAnalogs=10
+    warning("Parameter 'nAnalogs' is NULL and is set to 10 by default")
+  }
+  if(is.null(nAnalogs) & criteria=="Large_dist"){
+    nAnalogs=1
+  }
+  
+  if (any(names(dim(obsL)) %in% 'ftime')) {
+    if (any(names(dim(obsL)) %in% 'time')) {
+      stop("Multiple temporal dimensions ('ftime' and 'time') found",
+           "in parameter 'obsL'.")
+    } else {
+      time_pos_obsL <-  which(names(dim(obsL)) == 'ftime')
+      names(dim(obsL))[time_pos_obsL] <- 'time'
+      if (any(names(dim(expL)) %in% 'ftime')) {
+        time_pos_expL <-  which(names(dim(expL)) == 'ftime')
+        names(dim(expL))[time_pos_expL] <- 'time'
+      }
+    }
+  }
+  if (any(names(dim(obsVar)) %in% 'ftime')) {
+    if (any(names(dim(obsVar)) %in% 'time')) {
+      stop("Multiple temporal dimensions ('ftime' and 'time') found",
+           "in parameter 'obsVar'.")
+    } else {
+      time_pos_obsVar <-  which(names(dim(obsVar)) == 'ftime')
+      names(dim(obsVar))[time_pos_obsVar] <- 'time'
+      if (any(names(dim(expVar)) %in% 'ftime')) {
+        time_pos_expVar <-  which(names(dim(expVar)) == 'ftime')
+        names(dim(expVar))[time_pos_expVar] <- 'time'
+      }
+    }
+  }
+  if (any(names(dim(obsL)) %in% 'sdate')) {
+    if (any(names(dim(obsL)) %in% 'time')) {
+      dims_obsL <- dim(obsL)
+      pos_sdate <- which(names(dim(obsL)) == 'sdate')
+      pos_time <- which(names(dim(obsL)) == 'time')
+      pos <- 1 : length(dim(obsL))
+      pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+      obsL <- aperm(obsL, pos)
+      dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
+                     dims_obsL[-c(pos_time, pos_sdate)])
+    } else {
+      stop("Parameter 'obsL' must have a temporal dimension.")
+    }
+  }
+  if (any(names(dim(obsVar)) %in% 'sdate')) {
+    if (any(names(dim(obsVar)) %in% 'time')) {
+      dims_obsVar <- dim(obsVar)
+      pos_sdate <- which(names(dim(obsVar)) == 'sdate')
+      pos_time <- which(names(dim(obsVar)) == 'time')
+      pos <- 1 : length(dim(obsVar))
+      pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
+      obsVar <- aperm(obsVar, pos)
+      dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
+                     dims_obsVar[-c(pos_time, pos_sdate)])
+    } else {
+      stop("Parameter 'obsVar' must have a temporal dimension.")
+    }
+  } 
+  
+  if (is.null(region) & criteria!="Large_dist") {
+    if (!is.null(lonVar) & !is.null(latVar)) {
+      region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
+    }else{
+      stop("Parameters 'lonVar' and 'latVar' must be given in criteria 
+           'Local_dist' and 'Local_cor'")
+    }
+  }
+  position <- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+                     obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                     latVar = latVar, region = region)$position
+  metrics<- Select(expL = expL, obsL = obsL,  expVar = expVar, 
+                    obsVar = obsVar, criteria = criteria, lonVar = lonVar, 
+                    latVar = latVar, region = region)$metric.original
+  best <- Apply(list(position), target_dims = c('time', 'pos'), 
+                fun = BestAnalog, criteria = criteria, 
+                return_list = return_list, nAnalogs = nAnalogs)$output1
+  Analogs_dates <- time_obsL[best]
+  dim(Analogs_dates) <- dim(best)
+  if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
+    if (is.null(obsVar)) {
+      obsVar <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+      expVar <- SelBox(expL, lon = lonVar, lat = latVar, region=region)$data
+      Analogs_fields <- Subset(obsVar, 
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+      warning("obsVar is NULL, 
+          obsVar will be computed from obsL (same variable)")
+      
+    } else {
+      obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar, 
+                         region = region)$data
+      Analogs_fields <- Subset(obslocal, 
+                               along = which(names(dim(obslocal)) == 'time'),
+                               indices = best)
+    }
+  } else {
+    warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
+            " are NULL and the large scale field will be returned.")
+    if (is.null(obsVar)) {
+      Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
+                               indices = best)
+    } else {
+      Analogs_fields <- Subset(obsVar,
+                               along = which(names(dim(obsVar)) == 'time'),
+                               indices = best)
+    }
+  }
+  
+  lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
+  lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
+  if (lon_dim < lat_dim) {
+  dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lon_dim, lat_dim)], dim(best))
+  } else if (lon_dim > lat_dim) {
+  dim(Analogs_fields) <- c(dim(Analogs_fields)[c(lat_dim, lon_dim)], dim(best))
+  } else {
+    stop("Dimensions 'lat' and 'lon' not found.")
+  }
+  Analogs_metrics <- Subset(metrics,
+                            along = which(names(dim(metrics)) == 'time'),
+                           indices = best)
+  DistCorr <- data.frame(as.numeric(1:nrow(Analogs_metrics)),(Analogs_metrics),
+                    Analogs_dates)
+  if(dim(DistCorr)[2]==3){names(DistCorr) <- c("Analog","LargeDist","Dates")}
+  if(dim(DistCorr)[2]==4){names(DistCorr) <- c("Analog","LargeDist",
+                                                  "LocalDist","Dates")}
+  if(dim(DistCorr)[2]==5){names(DistCorr) <- c("Analog","LargeDist",
+                                                  "LocalDist","LocalCorr","Dates")}
+  return(list(AnalogsFields = Analogs_fields,
+              AnalogsInfo=DistCorr))
+  }
+
+BestAnalog <- function(position, criteria = 'Large_dist', return_list = FALSE, 
+                       nAnalogs = nAnalogs) {
+  pos_dim <- which(names(dim(position)) == 'pos')
+  if (dim(position)[pos_dim] == 1) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    if (criteria != 'Large_dist') {
+      warning("Dimension 'pos' in parameter 'position' has length 1,",
+              " criteria 'Large_dist' will be used.")
+      criteria <- 'Large_dist'
+    }
+  } else if (dim(position)[pos_dim] == 2) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    if (criteria == 'Local_cor') {
+      warning("Dimension 'pos' in parameter 'position' has length 2,",
+              " criteria 'Local_dist' will be used.")
+      criteria <- 'Local_dist'
+    }
+  } else if (dim(position)[pos_dim] == 3) {
+    pos1 <- Subset(position, along = pos_dim, indices = 1)
+    pos2 <- Subset(position, along = pos_dim, indices = 2)
+    pos3 <- Subset(position, along = pos_dim, indices = 3)
+    if (criteria != 'Local_cor') {
+      warning("Parameter 'criteria' is set to", criteria, ".")
+    }
+  } else {
+    stop("Parameter 'position' has dimension 'pos' of different ",
+         "length than expected (from 1 to 3).")
+  }
+  if (criteria == 'Large_dist') {
+    if (return_list == FALSE) {
+      pos <- pos1[1]
+    } else {
+      pos <- pos1[1 : nAnalogs]
+    }
+  } else if (criteria== 'Local_dist') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    if(length(best)==1){
+      warning("Just 1 best analog matching Large_dist and ", 
+              "Local_dist criteria")
+    } 
+    if(length(best)==1 & is.na(best[1])==TRUE){
+      stop("no best analogs matching Large_dist and Local_dist criterias") 
+    }
+    pos <- pos2[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    if (return_list == FALSE) { 
+      pos <- pos[1]
+    }else {
+        pos <- pos}
+  } else if (criteria == 'Local_cor') {
+    pos1 <- pos1[1 : nAnalogs]
+    pos2 <- pos2[1 : nAnalogs]
+    best <- match(pos1, pos2)
+    pos <- pos1[as.logical(best)]
+    pos <- pos[which(!is.na(pos))]
+    pos3 <- pos3[1 : nAnalogs]
+    best <- match(pos, pos3)
+    pos <- pos[order(best, decreasing = F)]
+    pos <- pos[which(!is.na(pos))]
+    if (return_list == FALSE) { 
+      pos <- pos[1]
+    } else{
+      pos <- pos
+    }
+    return(pos)
+  }
+}
+Select <- function(expL, obsL,  expVar = NULL, obsVar = NULL, 
+                   criteria = "Large_dist",
+                   lonVar = NULL, latVar = NULL, region = NULL) {
+names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)), names(dim(obsL)))
+  metric1 <- Apply(list(obsL), target_dims = list(c('lat', 'lon')), 
+                   fun = .select, expL, metric = "dist")$output1
+  metric1.original=metric1
+  if (length(dim(metric1)) > 1) {
+    dim_time_obs <- which(names(dim(metric1)) == 'time' | 
+                            names(dim(metric1)) == 'ftime')
+    dim(metric1) <- c(dim(metric1), metric=1)
+    margins <- c(1 : (length(dim(metric1))))[-dim_time_obs]
+    pos1 <- apply(metric1, margins, order)      
+    names(dim(pos1))[1] <- 'time'
+    metric1.original=metric1
+    metric1 <-  apply(metric1, margins, sort)
+    names(dim(metric1))[1] <- 'time'
+    names(dim(metric1.original))=names(dim(metric1))
+  } else {
+    pos1 <- order(metric1)
+    dim(pos1) <- c(time = length(pos1))
+    metric1 <- sort(metric1)
+    dim(metric1) <- c(time = length(metric1))
+    dim(metric1.original)=dim(metric1)
+    dim_time_obs=1
+  }
+  if (criteria == "Large_dist") {
+    dim(metric1) <- c(dim(metric1), metric = 1)
+    dim(pos1) <- c(dim(pos1), pos = 1)
+    dim(metric1.original)=dim(metric1)
+    return(list(metric = metric1, metric.original=metric1.original,position = pos1))
+  }
+  if (criteria == "Local_dist" | criteria == "Local_cor") {
+    obs <- SelBox(obsL, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(expL, lon = lonVar, lat = latVar, region = region)$data
+    metric2 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "dist")$output1
+    metric2.original=metric2
+    dim(metric2) <- c(dim(metric2), metric=1)
+    margins <- c(1 : (length(dim(metric2))))[-dim_time_obs]
+    pos2 <- apply(metric2, margins, order)
+    dim(pos2) <- dim(pos1)
+    names(dim(pos2))[1] <- 'time'
+    metric2 <-  apply(metric2, margins, sort)
+    names(dim(metric2))[1] <- 'time'
+    if (criteria == "Local_dist") {
+      metric <- abind(metric1, metric2, along = length(dim(metric1))+1)
+      metric.original <- abind(metric1.original,metric2.original,along=length(dim(metric1))+1)
+      position <- abind(pos1, pos2, along = length(dim(pos1))+1) 
+      names(dim(metric)) <- c(names(dim(pos1)), 'metric')
+      names(dim(position)) <- c(names(dim(pos1)), 'pos')
+      names(dim(metric.original)) = names(dim(metric)) 
+      return(list(metric = metric, metric.original=metric.original,position = position))
+    }   
+  }
+  if (criteria == "Local_cor") {
+    obs <- SelBox(obsVar, lon = lonVar, lat = latVar, region = region)$data
+    exp <- SelBox(expVar, lon = lonVar, lat = latVar, region = region)$data
+    metric3 <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                     fun = .select, exp, metric = "cor")$output1
+    metric3.original=metric3
+    dim(metric3) <- c(dim(metric3), metric=1)
+    margins <- c(1 : (length(dim(metric3))))[-dim_time_obs]
+    pos3 <- apply(abs(metric3), margins, order, decreasing = TRUE)
+    names(dim(pos3))[1] <- 'time'
+    #metric3 <-  apply(abs(metric3), margins, sort)
+    metricsort <- metric3[pos3]
+    dim(metricsort)=dim(metric3)
+    names(dim(metricsort))[1] <- 'time'
+    metric <- abind(metric1, metric2, metricsort, 
+                    along = length(dim(metric1)) + 1)
+    metric.original <- abind(metric1.original, metric2.original, metric3.original, 
+                    along = length(dim(metric1)) + 1)
+    position <- abind(pos1, pos2, pos3, along = length(dim(pos1)) + 1)   
+    names(dim(metric)) <- c(names(dim(metric1)), 'metric')
+    names(dim(position)) <- c(names(dim(pos1)), 'pos')
+    names(dim(metric.original)) = names(dim(metric)) 
+    return(list(metric = metric, metric.original=metric.original,position = position))
+  }
+  else {
+    stop("Parameter 'criteria' must to be one of the: 'Large_dist', ",
+         "'Local_dist','Local_cor'.")
+  }
+}
+.select <- function(exp, obs, metric = "dist") {
+  if (metric == "dist") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {sum((x - exp) ^ 2)})$output1
+  } else if (metric == "cor") {
+    result <- Apply(list(obs), target_dims = list(c('lat', 'lon')), 
+                    fun = function(x) {cor(as.vector(x), 
+                                           as.vector(exp))})$output1
+  } 
+  result
+}
+replace_repeat_dimnames <- function(names_exp, names_obs, lat_name = 'lat', 
+                                    lon_name = 'lon') {
+  if (!is.character(names_exp)) {
+    stop("Parameter 'names_exp' must be a vector of characters.")
+  }
+  if (!is.character(names_obs)) {
+    stop("Parameter 'names_obs' must be a vector of characters.")
+  }
+  latlon_dim_exp <- which(names_exp == lat_name | names_exp == lon_name)
+  latlon_dim_obs <- which(names_obs == lat_name | names_obs == lon_name)
+  if (any(unlist(lapply(names_exp[-latlon_dim_exp],
+                        function(x){x == names_obs[-latlon_dim_obs]})))) {
+    original_pos <- lapply(names_exp, 
+                           function(x) which(x == names_obs[-latlon_dim_obs]))
+    original_pos <- lapply(original_pos, length) > 0
+    names_exp[original_pos] <- paste0(names_exp[original_pos], "_exp")
+  }
+  return(names_exp)
+  ## Improvements: other dimensions to avoid replacement for more flexibility.
+}
diff --git a/man/Analogs.Rd b/man/Analogs.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..ee8a737ece7426357f9b4d7dc675d1de35ea79de
--- /dev/null
+++ b/man/Analogs.Rd
@@ -0,0 +1,392 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/CST_Analogs.R
+\name{Analogs}
+\alias{Analogs}
+\title{Analogs based on large scale fields.}
+\usage{
+Analogs(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
+  criteria = "Large_dist", lonVar = NULL, latVar = NULL, region = NULL,
+  nAnalogs = NULL, return_list = FALSE)
+}
+\arguments{
+\item{expL}{an array of N named dimensions containing the experimental field
+on the large scale for which the analog is aimed. This field is used to in 
+all the criterias. If parameter 'expVar' is not provided, the function will
+return the expL analog. The element 'data' in the 's2dv_cube' object must 
+have, at least, latitudinal and longitudinal dimensions. The object is expect
+to be already subset for the desired large scale region.}
+
+\item{obsL}{an array of N named dimensions containing the observational field 
+on the large scale. The element 'data' in the 's2dv_cube' object must have 
+the same latitudinal and longitudinal dimensions as parameter 'expL' and a
+temporal dimension with the maximum number of available observations.}
+
+\item{time_obsL}{a character string indicating the date of the observations 
+in the format "dd/mm/yyyy"}
+
+\item{expVar}{an array of N named dimensions containing the experimental 
+field on the local scale, usually a different variable to the parameter 
+'expL'. If it is not NULL (by default, NULL), the returned field by this 
+function will be the analog of parameter 'expVar'.}
+
+\item{obsVar}{an array of N named dimensions containing the field of the 
+same variable as the passed in parameter 'expVar' for the same region.}
+
+\item{criteria}{a character string indicating the criteria to be used for the
+selection of analogs:
+\itemize{
+\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
+\item{Local_dist} minimum Euclidean distance in the large scale pattern 
+and minimum Euclidean distance in the local scale pattern; and
+\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
+minimum Euclidean distance in the local scale pattern and highest correlation
+in the local variable to downscale.}}
+
+\item{lonVar}{a vector containing the longitude of parameter 'expVar'.}
+
+\item{latVar}{a vector containing the latitude of parameter 'expVar'.}
+
+\item{region}{a vector of length four indicating the minimum longitude, 
+the maximum longitude, the minimum latitude and the maximum latitude.}
+
+\item{nAnalogs}{number of Analogs to be selected to apply the criterias 
+'Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
+that the user can get, but the number of events with minimum distance in 
+which perform the search of the best Analog. The default value for the 
+'Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor'criterias must
+be selected by the user otherwise the default value will be set as 10.}
+
+\item{return_list}{TRUE to get a list with the best analogs. FALSE
+to get a single analog, the best analog. For Downscaling return_list must be
+FALSE.}
+}
+\value{
+AnalogsFields, dowscaled values of the best analogs for the criteria 
+selected.
+
+AnalogsInfo, a dataframe with information about the number of the 
+best analogs, the corresponding value of the metric used in the selected 
+criteria (distance values for Large_dist and Local_dist,correlation values 
+for Local_cor), date of the analog). The analogs are listed in decreasing 
+order, the first one is the best analog (i.e if the selected criteria 
+is Local_cor the best analog will be the one with highest correlation, while
+for Large_dist criteria the best analog will be the day with minimum 
+Euclidean distance)
+}
+\description{
+This function perform a downscaling using Analogs. To compute 
+the analogs, the function search for days with similar large scale conditions
+to downscaled fields in the local scale. The large scale and the local scale 
+regions are defined by the user. The large scale is usually given by 
+atmospheric circulation as sea level pressure or geopotential height (Yiou 
+et al, 2013) but the function gives the possibility to use another field. The
+local scale will be usually given by precipitation or temperature fields, but
+might be another variable. 
+The analogs function will find the best analogs based in three criterias:
+(1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
+(2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
+and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
+(3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), minimum 
+distance in the local scale pattern (i.e. SLP) and highest correlation in the
+local variable to downscale (i.e Precipitation).
+The search of analogs must be done in the longest dataset posible. This is 
+important since it is necessary to have a good representation of the 
+possible states of the field in the past, and therefore, to get better 
+analogs. Once the search of the analogs is complete, and in order to used the 
+three criterias the user can select a number of analogs , using parameter 
+'nAnalogs' to restrict 
+the selection of the best analogs in a short number of posibilities, the best
+ones. This function has not constrains of specific regions, variables to 
+downscale, or data to be used (seasonal forecast data, climate projections 
+data, reanalyses data). The regrid into a finner scale is done interpolating 
+with CST_Load. Then, this interpolation is corrected selecting the analogs in
+the large and local scale in based of the observations. The function is an 
+adapted version of the method of Yiou et al 2013.
+}
+\examples{
+require(zeallot)
+
+# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
+# The best analog is found using a single variable (i.e. Sea level pressure, 
+# SLP). The downscaling will be done in the same variable used to study the 
+# minimal distance (i.e. SLP). obsVar and expVar NULLS or equal to obsL and 
+# expL respectively region, lonVar and latVar not necessary here. 
+# return_list=FALSE 
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*1.2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP)
+str(downscale_field)
+
+# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
+# The best analog is found using 2 variables (i.e. Sea Level Pressure, SLP 
+# and precipitation, pr): one variable (i.e. sea level pressure, expL) to 
+# find the best analog day (defined in criteria 'Large_dist' as the day, in 
+# obsL, with the minimum Euclidean distance to the day of interest in expL) 
+# The second variable will be the variable to donwscale (i.e. precipitation, 
+# obsVar). Parameter obsVar must be different to obsL.The downscaled 
+# precipitation will be the precipitation that belongs to the best analog day
+# in SLP. Region not needed here since will be the same for both variables.
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+                          obsVar=obs.pr,
+                          time_obsL=time_obsSLP)
+str(downscale_field)
+
+# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
+# variable: same as Example 1 but getting a list of best analogs (return_list
+# =TRUE) with the corresponding downscaled values, instead of only 1 single 
+# donwscaled value instead of 1 single downscaled value. Imposing nAnalogs 
+# (number of analogs to do the search of the best Analogs). obsVar and expVar
+# NULL or equal to obsL and expL,respectively.
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:1980),expSLP*1.5)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
+time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
+downscale_field<- Analogs(expL=expSLP, obsL=obsSLP, time_obsSLP,
+                         nAnalogs=5,return_list = TRUE)
+str(downscale_field)
+
+# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
+# same as example 2 but getting a list of best analogs (return_list =TRUE) 
+# with the values instead of only a single downscaled value. Imposing 
+# nAnalogs (number of analogs to do the search of the best Analogs). obsVar 
+# and expVar must be different to obsL and expL.
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+downscale_field <- Analogs(expL=expSLP, obsL=obsSLP, 
+                          obsVar=obs.pr,
+                          time_obsL=time_obsSLP,nAnalogs=5,
+                          return_list = TRUE)
+str(downscale_field)
+
+# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
+# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
+# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
+# downscaled precipitation will be the precipitation that belongs to the best 
+# analog day in SLP. lonVar, latVar and Region must be given here to select 
+# the local scale
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+# analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      obsVar=obs.pr,
+                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
+                      latVar=seq(30,35,1.5),region=region, 
+                      nAnalogs = 10, return_list = FALSE)
+str(Local_scale)
+
+# Example 6: list of best analogs using criteria 'Local_dist' and 2 
+# variables:
+# The best analog is found using 2 variables. Parameter obsVar must be 
+# different to obsL in this case.The downscaled precipitation will be the 
+# precipitation that belongs to the best analog day in SLP. lonVar, latVar 
+# and Region needed. return_list=TRUE
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+# analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      obsVar=obs.pr,
+                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
+                      latVar=seq(30,35,1.5),region=region, 
+                      nAnalogs = 5, return_list = TRUE)
+str(Local_scale)
+
+# Example 7: Downscaling using Local_dist criteria
+# without parameters obsVar and expVar. return list =FALSE 
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+# analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
+                      latVar=seq(30,35,1.5),region=region, 
+                      nAnalogs = 10, return_list = TRUE)
+str(Local_scale)
+
+# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
+# The best analog is found using 2 variables. Parameter obsVar and expVar 
+# are necessary and must be different to obsL and expL, respectively.
+# The downscaled precipitation will be the precipitation that belongs to
+# the best analog day in SLP large and local scales, and to the day with 
+# the higher correlation in precipitation. return_list=FALSE. two options 
+# for nAnalogs
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+exp.pr <- c(rnorm(1:20)*0.001)
+dim(exp.pr)=dim(expSLP)
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+# analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scalecor <- Analogs(expL=expSLP,
+                         obsL=obsSLP, time_obsL=time_obsSLP,
+                         obsVar=obs.pr,expVar=exp.pr,
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         latVar=seq(30,35,1.5),nAnalogs=8,region=region, 
+                         return_list = FALSE)
+Local_scalecor$AnalogsInfo
+Local_scalecor$DatesAnalogs
+# same but without imposing nAnalogs, so nAnalogs will be set by default as 10 
+Local_scalecor <- Analogs(expL=expSLP,
+                         obsL=obsSLP, time_obsL=time_obsSLP,
+                         obsVar=obs.pr,expVar=exp.pr,
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         latVar=seq(30,35,1.5),region=region, 
+                         return_list = FALSE)
+Local_scalecor$AnalogsInfo
+Local_scalecor$DatesAnalogs
+
+# Example 9: List of best analogs in the three criterias Large_dist, 
+# Local_dist, and Local_cor return list TRUE same variable
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+# analogs of large scale using criteria 1
+Large_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      criteria="Large_dist", 
+                      nAnalogs = 7, return_list = TRUE)
+str(Large_scale)
+Large_scale$AnalogsInfo
+# analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
+                      latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+                      return_list = TRUE)
+str(Local_scale)
+Local_scale$AnalogsInfo
+# analogs of local scale using criteria 3
+Local_scalecor <- Analogs(expL=expSLP,
+                         obsL=obsSLP, time_obsL=time_obsSLP,
+                         obsVar=obsSLP,expVar=expSLP,
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+                         return_list = TRUE)
+str(Local_scalecor)
+Local_scalecor$AnalogsInfo
+
+# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and 
+# Local_cor return list FALSE, different variable
+
+expSLP <- rnorm(1:20)
+dim(expSLP) <- c(lat = 4, lon = 5)
+obsSLP <- c(rnorm(1:180),expSLP*2)
+dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
+time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
+exp.pr <- c(rnorm(1:20)*0.001)
+dim(exp.pr)=dim(expSLP)
+obs.pr <- c(rnorm(1:200)*0.001)
+dim(obs.pr)=dim(obsSLP)
+# analogs of large scale using criteria 1
+Large_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      criteria="Large_dist", 
+                      nAnalogs = 7, return_list = FALSE)
+str(Large_scale)
+Large_scale$AnalogsInfo
+# analogs of local scale using criteria 2
+lonmin=-1
+lonmax=2
+latmin=30
+latmax=33
+region=c(lonmin,lonmax,latmin,latmax)
+Local_scale <- Analogs(expL=expSLP,
+                      obsL=obsSLP, time_obsL=time_obsSLP,
+                      obsVar=obs.pr,
+                      criteria="Local_dist",lonVar=seq(-1,5,1.5),
+                      latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+                      return_list = FALSE)
+str(Local_scale)
+Local_scale$AnalogsInfo
+# analogs of local scale using criteria 3
+Local_scalecor <- Analogs(expL=expSLP,
+                         obsL=obsSLP, time_obsL=time_obsSLP,
+                         obsVar=obs.pr,expVar=exp.pr,
+                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
+                         latVar=seq(30,35,1.5),nAnalogs=7,region=region, 
+                         return_list = FALSE)
+str(Local_scalecor)
+Local_scalecor$AnalogsInfo
+
+}
+\author{
+M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+
+Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+}
+\references{
+Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+\email{pascal.yiou@lsce.ipsl.fr}
+}
+
diff --git a/man/CST_Analogs.Rd b/man/CST_Analogs.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..7c9a1e6f125266b510a3f9ccd11c4511d7a927b9
--- /dev/null
+++ b/man/CST_Analogs.Rd
@@ -0,0 +1,100 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/CST_Analogs.R
+\name{CST_Analogs}
+\alias{CST_Analogs}
+\title{Downscaling using Analogs based on large scale fields.}
+\usage{
+CST_Analogs(expL, obsL, time_obsL, expVar = NULL, obsVar = NULL,
+  region = NULL, criteria = "Large_dist")
+}
+\arguments{
+\item{expL}{an 's2dv_cube' object containing the experimental field on the 
+large scale for which the analog is aimed. This field is used to in all the
+criterias. If parameter 'expVar' is not provided, the function will return 
+the expL analog. The element 'data' in the 's2dv_cube' object must have, at
+least, latitudinal and longitudinal dimensions. The object is expect to be 
+already subset for the desired large scale region.}
+
+\item{obsL}{an 's2dv_cube' object containing the observational field on the
+large scale. The element 'data' in the 's2dv_cube' object must have the same 
+latitudinal and longitudinal dimensions as parameter 'expL' and a temporal 
+dimension with the maximum number of available observations.}
+
+\item{time_obsL}{a character string indicating the date of the observations 
+in the format "dd/mm/yyyy"}
+
+\item{expVar}{an 's2dv_cube' object containing the experimental field on the
+local scale, usually a different variable to the parameter 'expL'. If it is 
+not NULL (by default, NULL), the returned field by this function will be the 
+analog of parameter 'expVar'.}
+
+\item{obsVar}{an 's2dv_cube' containing the field of the same variable as the 
+passed in parameter 'expVar' for the same region.}
+
+\item{region}{a vector of length four indicating the minimum longitude, the 
+maximum longitude, the minimum latitude and the maximum latitude.}
+
+\item{criteria}{a character string indicating the criteria to be used for the 
+selection of analogs:
+\itemize{
+\item{Large_dist} minimal distance in the large scale pattern;
+\item{Local_dist} minimal distance in the large scale pattern and minimal 
+distance in the local scale pattern; and
+\item{Local_cor} minimal distance in the large scale pattern, minimal 
+distance in the local scale pattern and maxima correlation in the 
+local variable to downscale.}}
+}
+\value{
+An 's2dv_cube' object containing the dowscaled values of the best 
+analogs in the criteria selected.
+}
+\description{
+This function perform a downscaling using Analogs. To compute 
+the analogs, the function search for days with similar large scale conditions
+to downscaled fields in the local scale. The large scale and the local scale 
+regions are defined by the user. The large scale is usually given by 
+atmospheric circulation as sea level pressure or geopotential height 
+(Yiou et al, 2013) but the function gives the possibility to use another 
+field. The local scale will be usually given by precipitation or temperature 
+fields, but might be another variable.The analogs function will find the best
+analogs based in three criterias:
+(1) Minimal distance in the large scale pattern (i.e. SLP)
+(2) Minimal distance in the large scale pattern (i.e. SLP) and minimal 
+distance in the local scale pattern (i.e. SLP). 
+(3) Minimal distance in the large scale pattern (i.e. SLP), minimal 
+distance in the local scale pattern (i.e. SLP) and maxima correlation in the 
+local variable to downscale (i.e Precipitation).
+The search of analogs must be done in the longest dataset posible. This is 
+important since it is necessary to have a good representation of the 
+possible states of the field in the past, and therefore, to get better 
+analogs. Once the search of the analogs is complete, and in order to used 
+the three criterias the user can select a number of analogs, using parameter
+'nAnalogs' to restrict the selection of the best analogs in a short number 
+of posibilities, the best ones.
+This function has not constrains of specific regions, variables to downscale,
+or data to be used (seasonal forecast data, climate projections data, 
+reanalyses data). The regrid into a finner scale is done interpolating with 
+CST_Load. Then, this interpolation is corrected selecting the analogs in the 
+large and local scale in based of the observations. The function is an 
+adapted version of the method of Yiou et al 2013.
+}
+\examples{
+res <- CST_Analogs(expL = lonlat_data$exp, obsL = lonlat_data$obs)
+
+}
+\author{
+M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
+
+Nuria Perez-Zanon \email{nuria.perez@bsc.es}
+}
+\references{
+Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
+and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
+from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
+\email{pascal.yiou@lsce.ipsl.fr}
+}
+\seealso{
+code{\link{CST_Load}}, \code{\link[s2dverification]{Load}} and 
+\code{\link[s2dverification]{CDORemap}}
+}
+