diff --git a/DESCRIPTION b/DESCRIPTION
index 3a9b1611d95f42823781c1f0ac9bbf1a1a92964f..ff80217de2f6b2532821a4857b1d905add63bd36 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: gmGeostats
-Version: 0.10-7.9006
+Version: 0.10-8
 Date: 2020-10-05
 Title: Geostatistics for Compositional Analysis
 Authors@R: c(person(given = "Raimon", 
@@ -63,8 +63,8 @@ Collate:
     'compositionsCompatibility.R'
     'gstatCompatibility.R'
     'variograms.R'
-    'gmValidationStrategy.R'
     'gmAnisotropy.R'
+    'gmValidationStrategy.R'
     'abstractClasses.R'
     'accuracy.R'
     'data.R'
diff --git a/NAMESPACE b/NAMESPACE
index 1268eb1da8a9adcdf17839a26caa94003a018dcf..9874ed5a5c3fa0eebfac65136f0d2f90ba3fb871 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -124,8 +124,6 @@ S3method(swath,rcomp)
 S3method(unmask,DataFrameStack)
 S3method(unmask,SpatialPixels)
 S3method(unmask,data.frame)
-S3method(validate,LeaveOneOut)
-S3method(validate,NfoldCrossValidation)
 S3method(variogramModelPlot,gmEVario)
 S3method(variogramModelPlot,gstatVariogram)
 S3method(variogramModelPlot,logratioVariogram)
@@ -213,6 +211,7 @@ export(swarmPlot)
 export(swath)
 export(unmask)
 export(validate)
+export(validate_gmSpatialModel)
 export(variogram)
 export(variogramModelPlot)
 export(variogram_gmSpatialModel)
@@ -236,6 +235,7 @@ exportMethods(as.gstat)
 exportMethods(logratioVariogram)
 exportMethods(predict)
 exportMethods(stackDim)
+exportMethods(validate)
 exportMethods(variogram)
 import(RColorBrewer)
 import(compositions)
diff --git a/R/abstractClasses.R b/R/abstractClasses.R
index 52939afe7f80722d1cf7cec4d745a1f3fb0d178f..51c9155f842b9817cdcb07d079fdc37e0ecb7e19 100644
--- a/R/abstractClasses.R
+++ b/R/abstractClasses.R
@@ -12,8 +12,7 @@ setOldClass("gmDirectSamplingParameters")
 setOldClass("gmTurningBands")
 setOldClass("gmSequentialSimulation")
 setOldClass("gmCholeskyDecomposition")
-setOldClass("NfoldCrossValidation")
-setOldClass("LeaveOneOut")
+
 
 # abstract classes
 # cat("creating spatial method parameter classes: superclass creation\n")
@@ -72,8 +71,7 @@ setClassUnion(name="gmGaussianMethodParameters",
 #' 
 #' @export
 setClassUnion(name="gmSpatialMethodParameters", 
-              members=c("NULL",
-                        "gmNeighbourhoodSpecification",
+              members=c("gmNeighbourhoodSpecification",
                         "gmMPSParameters",
                         "gmValidationStrategy")
 )
diff --git a/R/gmSpatialModel.R b/R/gmSpatialModel.R
index c489ab1ff5d87830a14f94014718c3dfd606b385..d4cb2d2f32f00a14e399a68605a8f5450712e8a2 100644
--- a/R/gmSpatialModel.R
+++ b/R/gmSpatialModel.R
@@ -306,7 +306,7 @@ as.gstat.gmSpatialModel <- function(object, ...){
     if(any(is.infinite(beta))) beta = NULL
     # neighbourhood
     ng = object@parameters
-    if(!is(ng, "gmGaussianMethodParameters")) stop("as.gstat: object@parameters must be of class 'gmGaussianMethodParameters'!")
+    if(!is(ng, "gmKrigingNeighbourhood")) stop("as.gstat: object@parameters must be of class 'gmGaussianMethodParameters'!")
     res = compo2gstatLR(coords=coords, compo=compo, V=Vinv, lrvgLMC=lrvgLMC, 
                         nscore=FALSE, formulaterm = formulaterm, prefix=prefix, beta=beta, 
                         nmax=ng$nmax, nmin=ng$nmin, omax=ng$omax, maxdist=ng$maxdist, force=ng$force)
diff --git a/R/gmValidationStrategy.R b/R/gmValidationStrategy.R
index 256a2bf4a3db6bd980f2dd2a68bff3a6e537167c..11cd8bba23b5d302cfbe3c1280fc3019aabd97cc 100644
--- a/R/gmValidationStrategy.R
+++ b/R/gmValidationStrategy.R
@@ -45,6 +45,9 @@ LeaveOneOut = function(){
   return(res)
 }
 
+setOldClass("LeaveOneOut")
+setOldClass("NfoldCrossValidation")
+
 
 #### validation methods -------------
 #' Validate a spatial model
@@ -61,7 +64,7 @@ LeaveOneOut = function(){
 #' @export
 #' @family validation functions 
 #' @family accuracy functions
-#'
+#' @name validate_gmSpatialModel
 #' @examples
 #' data("Windarling")
 #' X = Windarling[,c("Easting","Northing")]
@@ -74,38 +77,41 @@ LeaveOneOut = function(){
 #' vs2 = NfoldCrossValidation(nfolds=sample(1:10, nrow(X), replace=TRUE))
 #' vs2
 #' \dontrun{ v2 = validate(gs, strategy=vs2) # quite slow }
-validate <- function(object, strategy, ...) UseMethod("validate", strategy)
+NULL
+
+#' @rdname validate_gmSpatialModel
+#' @export
+setGeneric("validate", function(object, strategy, ...){
+  standardGeneric("validate")
+})
 
 
-#' @describeIn validate Validate a spatial model
-#' @method validate LeaveOneOut
+#' @rdname validate_gmSpatialModel
 #' @export
-validate.LeaveOneOut = function(object, strategy, ...){
-  if("gstat" %in% class(object)){
-    n = nrow(object$data[[1]]$data)
-  }else if(is(object, "gmSpatialModel")){
-    n = nrow(object@data)
-  }else{
-    object = try(as.gmSpatialModel(object))
-    if(class(object)=="try-error") stop("validate.LeaveOneOut: provided object not interpretable")
-    n = nrow(object@data)
-  }
+setMethod("validate",signature(object="gstat", strategy="LeaveOneOut"),
+       function(object, strategy, ...){
+  n = nrow(object$data[[1]]$data)
   v = validate(object, NfoldCrossValidation(nfolds=n, doAll=TRUE))
   return(v)
-}
+})
 
-#' @describeIn validate Validate a spatial model
-#' @method validate NfoldCrossValidation
+#' @rdname validate_gmSpatialModel
 #' @export
-validate.NfoldCrossValidation = function(object, strategy, ...){
-  # manage "gstat" case
-  if("gstat" %in% class(object)){
-    warning("validate: object provided is of class 'gstat', attempting 'gstat.cv(..., remove.all=TRUE, all.residuals=TRUE)'")
-    return(gstat::gstat.cv(object, nfold=strategy$nfolds, remove.all = TRUE, all.residuals = TRUE))
-  }
+setMethod("validate",signature(object="gmSpatialModel", strategy="LeaveOneOut"),
+  function(object, strategy, ...){
+    n = nrow(object@data)
+    v = validate(object, NfoldCrossValidation(nfolds=n, doAll=TRUE))
+    return(v)
+})
+
+
+
+
+#' @rdname validate_gmSpatialModel
+#' @export
+setMethod("validate",signature(object="gmSpatialModel", strategy="NfoldCrossValidation"),
+          function(object, strategy, ...){
   # manage "gmSpatialModel" case
-  object = try(as.gmSpatialModel(object))
-  if(class(object)=="try-error") stop("validate.NfoldCrossValidation: provided object not interpretable")
   # interpret the information about the n-folds provided
   n = strategy$nfolds
   m = nrow(object@data)
@@ -138,7 +144,19 @@ validate.NfoldCrossValidation = function(object, strategy, ...){
   # reorder and return
   res = res[nms,] 
   return(res)
-}
+})
 
 
 
+
+
+#' @rdname validate_gmSpatialModel
+#' @export
+setMethod("validate",signature(object="gstat", strategy="NfoldCrossValidation"),
+          function(object, strategy, ...){
+            # manage "gstat" case
+              warning("validate: object provided is of class 'gstat', attempting 'gstat.cv(..., remove.all=TRUE, all.residuals=TRUE)'")
+              return(gstat::gstat.cv(object, nfold=strategy$nfolds, remove.all = TRUE, all.residuals = TRUE))
+          })
+
+
diff --git a/R/gstatCompatibility.R b/R/gstatCompatibility.R
index 8b4e981f5d3c6db0b6cafb6f33b8ded4996a876a..c7ab6ceadc7097b885ee6ac31959b1688ed3bba7 100644
--- a/R/gstatCompatibility.R
+++ b/R/gstatCompatibility.R
@@ -1,4 +1,5 @@
 #### gstat easy/easier interface for multivariate data
+setOldClass("gstat")
 
 #' Fit an LMC to an empirical variogram
 #' 
diff --git a/vignettes/register_new_layer_datatype.Rmd b/vignettes/register_new_layer_datatype.Rmd
index 8b4c0a9b59642fa2db6821087d8db8c414bdb218..ce6fc0741f27529f28437c97eb5b0ed9d33b079b 100644
--- a/vignettes/register_new_layer_datatype.Rmd
+++ b/vignettes/register_new_layer_datatype.Rmd
@@ -150,10 +150,41 @@ image_cokriged.circular = function(x, ...){
 image_cokriged(theta.prds.back, ivar="theta")
 ```
 
+## An excursion on superclasses classes 
 
-## future work
+"gmGeostats" uses a mixture of S3 and S4 classes to manage the several kinds of objects, S3 classes mostly preferred for simple configuration objects, models and data elements, and S4 classes mostly in use for large compound spatial models and data containers. S4 classes, though being somewhat more complex to handle and slightly slower, have the advantage to allow for multiple dispatch, which this package extensively uses. S4 classes require its fields (called "slots") to strictly belong to a specific class. To handle this condition, and at the same time allow for multiple methods of specification, estimation, fitting and prediction of spatial models and random functions, "gmGeostats" provides a series of abstract classes controlling allowing certain fields to contain certain kinds of objects:
 
-In future extensions of this vignette we will discuss the way to create own structural functions (variograms) and estimation models/methods adapted to the nature of the data, and register them to the package.
+**gmNeighbourhoodSpecification** contains a description of the neighbourhood of a point during interpolation/simulation.
+
+**EmpiricalStructuralFunctionSpecification** contains a desciption of empirical structural functions, typically empirical variograms in different formats (e.g. "gstatVariogram" from package "gstat", or "logratioVariogram" from package "compositions").
+
+**ModelStructuralFunctionSpecification**, equivalent to the preceding one, this class contains specifications of models for structural functions (e.g. "variogramModel" or "CompLinModCoReg" for packages "gstat" resp. "compositions").
+
+**gmValidationStrategy** describes the way a model should be validated.
+
+**gmGaussianSimulationAlgorithm** specifies the exact gaussian simulation algorithm to be used, and provides its parameters (e.g. number of bands for Turning Bands).
+
+**gmTrainingImage** for multipoint statistics (MPS) methods, this abstract class gathers all ways to specify a gridded image.
+
+**gmUnconditionalSpatialModel** convenience class of the union of "gmTrainingImage" and "gmGaussianModel" (a concrete class containing "ModelStructuralFunctionSpecification" with some extra information), it is thought to contain all specifications of an unconditional random function.
+
+**gmMPSParameters**, analogous to "gmGaussianSimulationAlgorithm" or "gmValidationStrategy", this abstract class contains all specifications of MPS algorithms available.
+
+**gmSpatialMethodParameters** is a large container of both two-point and multipoint methods, i.e. descriptors of specific algorithms and their parameters. This union class should only contain other abstract claases!
+
+The package "methods" provides a way of checking the subclasses and superclasses of any specific class, thanks to the function `classesToAM()`:
+```{r}
+classesToAM("gmSpatialMethodParameters", includeSubclasses = TRUE)
+```
+The matrix contain a 1 if the row class is a subclass of the column class, and 0 otherwise.
+
+
+
+
+
+## Future work
+
+In future extensions of this vignette we will discuss the way to create own structural functions (variograms) and estimation models/methods adapted to the nature of the data, and register them to the package (usage of `setIs()` and coercion in conjunction with the abstract classes mention, `validate()`- and `predict()`-methods, creation of own `make.gm****Model()` data containers, etc).