setCgram: small improvements in the help-page

c6149a3e · Pospiech, Solveig (FWGB) - 133453 · 26ccbe0f · c6149a3e
Commit c6149a3e authored 3 years ago by Pospiech, Solveig (FWGB) - 133453
--- a/R/variograms.R
+++ b/R/variograms.R
 #### theoretical variogram ---------------------
-#' gmGeostats Variogram models
+#' Generate D-variate variogram models
-#' set up a D-variate variogram models 
 #'
-#' @param type constant indicating the model of correlation function
+#' @description Function to set up D-variate variogram models based on model type, the variogram parameters sill and nugget and a matrix describing the anisotropy of the range.
-#' @param nugget (DxD)-matrix for the nugget effect
+#'
+#' @param type model of correlation function. The function expects a constant, e.g. the internal constants 'vg.Gau' for Gaussian model or 'vg.Exp'. for exponential models. See examples for usage.
+#' @param nugget (DxD)-matrix for the nugget effect. Default is a muted nugget (0).
 #' @param sill (DxD)-matrix for the partial sills of the correlation function
 #' @param anisRanges 2x2 or 3x3 matrix of ranges (see details)
 #' @param extraPar for certain correlation functions, extra parameters (smoothness, period, etc)
 #'
 #' @return an object of class "gmCgram" containing the linear model of corregionalization
-#' of the nugget and the structure given. 
+#' of the nugget and the structure given.
-#' @details The argument `type` must be an integer indicating the model to be used. 
+#' @details The argument `type` must be an integer indicating the model to be used.
-#' Some constants are available to make reading code more understandable. That is, you can 
+#' Some constants are available to make reading code more understandable. That is, you can
-#' either write `1`, `vg.Sph` or `vg.Spherical`, they will all work and produce
+#' either write `1`, `vg.sph`, `vg.Sph` or `vg.Spherical`, they will all work and produce
-#' a spherical model. The same applies for the following models: `vg.Gauss=0`; 
+#' a spherical model. The same applies for the following models:
-#' `vg.Exp=vg.Exponential=2`. These constants are available after calling
+#' `vg.Gauss = vg.Gau = vg.gau = 0`;
-#' `data("variogramModels")`. 
+#' `vg.Exponential = vg.Exp = vg. exp = 2`.
-#' No other model is currently available, but this data object will be 
+#' These constants are available after calling `data("variogramModels")`.
+#' No other model is currently available, but this data object will be
 #' regularly updated.
 #' The constant vector `gsi.validModels` contains all currently valid models.
-#' 
+#'
-#' Argument `anisRange` expects a matrix $M$ such that 
+#' Argument `anisRange` expects a matrix $M$ such that
 #' \deqn{
 #' h^2 = (\mathbf{x}_i-\mathbf{x}_j)\cdot M^{-1}\cdot (\mathbf{x}_i-\mathbf{x}_j)^t
 #' }
 #' is the (square of) the lag distance to be fed into the correlation function.
-#' @family gmCgram 
+#' @family gmCgram
 #' @export
-#' @aliases vg.Exp vg.exp vg.Exponential vg.Gau vg.gauss 
+#' @aliases vg.Exp vg.exp vg.Exponential vg.Gau vg.gauss
 #' vg.Gauss vg.Sph vg.sph vg.Spherical gsi.validModels
 #'
 #' @examples
@@ -39,7 +41,7 @@
 #' plot(vm)
 setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
  utils::data("variogramModels")
-  stopifnot(all(dim(sill)==dim(nugget)), 
+  stopifnot(all(dim(sill)==dim(nugget)),
            ncol(sill)==nrow(sill),
            type %in% gsi.validModels)
  dim(sill) = c(1,dim(sill))
@@ -57,18 +59,18 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
 #' Subsetting of gmCgram variogram structures
-#' 
+#'
 #' Extraction or combination of nested structures of a gmCgram object
 #'
 #' @param x `gmCgram` variogram object
 #' @param i indices of the structures that are desired to be kept (0=nugget) or removed (see details)
 #' @param ... extra arguments for generic functionality
 #'
-#' @return a `gmCgram` variogram object with the desired structures only. 
+#' @return a `gmCgram` variogram object with the desired structures only.
-#' @details This function can be used to: extract the nugget (i=0), extract some 
+#' @details This function can be used to: extract the nugget (i=0), extract some
 #' structures (i=indices of the structures, possibly including 0 for the nugget),
-#' or filter some structures out (i=negative indices of the structures to remove; 
+#' or filter some structures out (i=negative indices of the structures to remove;
-#' nugget will always removed in this case). If you want to extract "slots" or 
+#' nugget will always removed in this case). If you want to extract "slots" or
 #' "elements" of the variogram, use the $-notation. If you want to extract variables of the
 #' variogram matrices, use the `[`-notation. The contrary operation (adding structures together)
 #' is obtained by summing (+) two `gmCgram` objects.
@@ -87,24 +89,24 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
  nSo = length(i)
  nSi = dim(x$M)[1]
  if(i==0){# case only nugget wanted
-    out = with(x, list(type=type[i], 
+    out = with(x, list(type=type[i],
-                       data=data[i], 
+                       data=data[i],
-                       nugget=nugget, 
+                       nugget=nugget,
-                       sill=structure(0*sill[1,,], dim=c(nSo, nD, nD)), 
+                       sill=structure(0*sill[1,,], dim=c(nSo, nD, nD)),
                       M=structure(M[1,,], dim=c(nSo, nG, nG))))
  }else if( (0 %in% i) & !any(i<0)){
    j = i[i>0] # case some structures wanted, including nugget
-    out = with(x, list(type=type[j], 
+    out = with(x, list(type=type[j],
-                       data=data[j], 
+                       data=data[j],
-                       nugget=nugget, 
+                       nugget=nugget,
-                       sill=structure(sill[j,,], dim=c(nSo-1, nD, nD)), 
+                       sill=structure(sill[j,,], dim=c(nSo-1, nD, nD)),
                       M=structure(M[j,,], dim=c(nSo-1, nG, nG))))
  }else if(all(i>0) | all(i<0)){# case some structured (un)wanted, nugget surely not wanted
    if(all(i<0)) nSo = nSi-nSo
-    out = with(x, list(type=type[i], 
+    out = with(x, list(type=type[i],
-                       data=data[i], 
+                       data=data[i],
-                       nugget=nugget*0, 
+                       nugget=nugget*0,
-                       sill=structure(sill[i,,], dim=c(nSo, nD, nD)), 
+                       sill=structure(sill[i,,], dim=c(nSo, nD, nD)),
                       M=structure(M[i,,], dim=c(nSo, nG, nG))))
  }else{# unsolvable case
    stop("index set i cannot merge 0 and negative numbers")
@@ -115,7 +117,7 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
 #' Combination of gmCgram variogram structures
-#' 
+#'
 #' combination of nested structures of a gmCgram object
 #'
 #' @param x `gmCgram` variogram object
@@ -130,7 +132,7 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
 #' vm = v1+v2
 "+.gmCgram" <- function(x,y) {
  y = as.gmCgram(y)
-  stopifnot(class(y)=="gmCgram", 
+  stopifnot(class(y)=="gmCgram",
            dim(x$sill)[-1]==dim(y$sill)[-1],
            dim(x$M)[-1]==dim(y$M)[-1])
  myfun = function(A,B){
@@ -155,7 +157,7 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
 #' Subsetting of gmCgram variogram structures
-#' 
+#'
 #' Extraction of some variables of a gmCgram object
 #'
 #' @param x \code{gmCgram} variogram object
@@ -164,15 +166,15 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
 #' is specified, \code{j} will be taken as equal to \code{i}!)
 #' @param ...  extra arguments for generic functionality
 #'
-#' @return a \code{gmCgram} variogram object with the desired variables only. 
+#' @return a \code{gmCgram} variogram object with the desired variables only.
-#' @details This function can be used to extract the model for a a subset of variables. 
+#' @details This function can be used to extract the model for a a subset of variables.
-#' If only \code{i} is specified, \code{j} will be taken as equal to \code{i}. 
+#' If only \code{i} is specified, \code{j} will be taken as equal to \code{i}.
 #' If you want to select all \code{i}'s for certain \code{j}'s or vice versa, give
-#' \code{i=1:dim(x$nugget)[1]} and \code{j=} your desired indices, respectively  
+#' \code{i=1:dim(x$nugget)[1]} and \code{j=} your desired indices, respectively
 #' \code{j=1:dim(x$nugget)[2]} and \code{i=} your desired indices; replace \code{x} by the
-#' object you are giving. If \code{i!=j}, the output will be a \code{c("gmXCgram","gmCgram")} 
+#' object you are giving. If \code{i!=j}, the output will be a \code{c("gmXCgram","gmCgram")}
 #' object, otherwise it will be a regular class \code{"gmCgram"} object.
-#' If you want to extract "slots" or 
+#' If you want to extract "slots" or
 #' "elements" of the variogram, use the $-notation. If you want to extract variables of the
 #' variogram matrices, use the `[`-notation.
 #' @export
@@ -188,10 +190,10 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
  nDi = length(i)
  nDj = length(j)
  nS = dim(x$M)[1]
-  out = with(x, list(type=type, 
+  out = with(x, list(type=type,
-                     data=data, 
+                     data=data,
-                     nugget=structure(nugget[i,j, drop=F], dim=c(nDi, nDj)), 
+                     nugget=structure(nugget[i,j, drop=F], dim=c(nDi, nDj)),
-                     sill=structure(sill[,i,j, drop=F], dim=c(nS, nDi, nDj)), 
+                     sill=structure(sill[,i,j, drop=F], dim=c(nS, nDi, nDj)),
                     M=M))
  class(out) = class(x)
  if(!all(i==j)) class(out) = unique(c("gmXCgram", class(out)))
@@ -199,8 +201,8 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
 }
 #' Length, and number of columns or rows
-#' 
+#'
-#' Provide number of structures, and nr of variables of an LMC of class gmCgram 
+#' Provide number of structures, and nr of variables of an LMC of class gmCgram
 #'
 #' @param x gmCgram object
 #'
@@ -234,14 +236,14 @@ nrow.gmCgram = function(x) nrow(x$nugget)
 #' Convert a gmCgram object to an (evaluable) function
-#' 
+#'
 #' Evaluate a gmCgram on some h values, or convert the gmCgram object into an evaluable function
 #'
 #' @param x a gmCgram object
 #' @param ... extra arguments for generic functionality
 #'
 #' @return a \code{function} that can be evaluated normally, with an argument \code{X}
-#' and possibly another argument \code{Y}; both must have the same number of columns 
+#' and possibly another argument \code{Y}; both must have the same number of columns
 #' than the geographic dimension of the variogram (i.e. \code{dim(x$M)[3]}).
 #' @export
 #' @method as.function gmCgram
@@ -280,55 +282,55 @@ predict.gmCgram = function(object, newdata, ...){
 }
-#' Convert theoretical structural functions to gmCgram format 
+#' Convert theoretical structural functions to gmCgram format
-#' 
+#'
-#' Convert covariance function or variogram models to the format gmCgram 
+#' Convert covariance function or variogram models to the format gmCgram
 #' of package gmGeostats
-#' 
+#'
 #' @param m model to be converted
 #' @param ... further parameters
 #'
-#' @return the covariance/variogram model, recasted to class \code{gmCgram}. 
+#' @return the covariance/variogram model, recasted to class \code{gmCgram}.
 #' This is a generic function. Methods exist for objects of class
 #' \code{LMCAnisCompo} (for compositional data) and \code{variogramModelList}
-#' (as provided by package \code{gstat}).  
+#' (as provided by package \code{gstat}).
 #' @export
 #' @family gmCgram functions
-as.gmCgram <- function(m, ...)  UseMethod("as.gmCgram",m) 
+as.gmCgram <- function(m, ...)  UseMethod("as.gmCgram",m)
-#' @describeIn as.gmCgram Convert theoretical structural functions to gmCgram format 
+#' @describeIn as.gmCgram Convert theoretical structural functions to gmCgram format
 #' @method as.gmCgram default
 #' @export
-as.gmCgram.default <- function(m,...) m 
+as.gmCgram.default <- function(m,...) m
 #' Draw cuves for covariance/variogram models
-#' 
+#'
 #' Represent a gmCgram object as a matrix of lines in several plots
 #'
 #' @param x object to draw, of class gmCgram // curently only valid for symmetric functions
 #' @param xlim.up range of lag values to use in plots of the upper triangle
 #' @param xlim.lo range of lag values to use in plots of the lower triangle
-#' @param vdir.up geograohic directions to represent in the upper triangle 
+#' @param vdir.up geograohic directions to represent in the upper triangle
 #' @param vdir.lo geograohic directions to represent in the lower triangle
 #' @param xlength number of discretization points to use for the curves (defaults to 200)
 #' @param varnames string vector, variable names to use in the labelling of axes
 #' @param add logical, should a new plot be created or stuff be added to an existing one?
 #' @param commonAxis logical, is a common Y axis for all plots in a row desired?
-#' @param cov logical, should the covariance function (=TRUE) or the variogram (=FALSE) be plotted?   
+#' @param cov logical, should the covariance function (=TRUE) or the variogram (=FALSE) be plotted?
-#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)? 
+#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)?
 #' defaults to TRUE
 #' @param ... further graphical parameters for the plotting function
 #'
 #' @return This function is called for its side effect of producing a plot: the plot will be
 #' open to further changes if you provide `closeplot=FALSE`. Additionally, the function
-#' invisibly returns the graphical parameters that were active before starting the plot. Hence, 
+#' invisibly returns the graphical parameters that were active before starting the plot. Hence,
 #' if you want to freeze a plot and not add anymore to it, you can do \code{par(plot(x, closeplot=FALSE, ...))},
 #' or \code{plot(x, closeplot=TRUE, ...)}.
 #' If you want to further add stuff to it, better just call \code{plot(x, closeplot=FALSE,...)}. The difference
-#' is only relevant when working with the screen graphical device. 
+#' is only relevant when working with the screen graphical device.
-#' @export 
+#' @export
 #' @method plot gmCgram
 #' @family gmCgram functions
 #' @examples
@@ -338,7 +340,7 @@ as.gmCgram.default <- function(m,...) m
 #' vm = v1+v2
 #' plot(vm)
 #' plot(vm, cov=FALSE)
-plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= NULL, xlength=200, varnames = colnames(x$nugget), 
+plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= NULL, xlength=200, varnames = colnames(x$nugget),
                        add=FALSE, commonAxis=TRUE, cov =TRUE, closeplot=TRUE, ...){
  Dg = dim(x$M)[2]
  Ns = dim(x$M)[1]
@@ -386,12 +388,12 @@ plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= N
  if(!is.null(xlim.lo)){
    xseq.lo = seq(from=xlim.lo[1], to=xlim.lo[2], length.out=xlength)
  }else{ xseq.lo=NULL}
  opar = par()
  opar = par_remove_readonly(opar)
  if(closeplot) on.exit(par(opar))
  getVdens = function(vdir, xseq){
    if(is.null(vdir)|is.null(xseq)) return(NULL)
    Vdens = sapply(1:nrow(vdir), function(k){
@@ -407,16 +409,16 @@ plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= N
      dim(C0) = c(Dv,Dv)
      Vdens = sweep(-Vdens, c(1,3), C0, "+") ## this must be corrected when we allow non-symmetric covariances
    }
-    return(Vdens)    
+    return(Vdens)
  }
  Vdens.up = getVdens(vdir.up, xseq.up)
  Vdens.lo = getVdens(vdir.lo, xseq.lo)
  myplot = function(...) matplot(type="l",ylab="", xlab="",xaxt="n", ...)
  if(add) myplot = function(...) matlines(...)
  if(!add){
    par(mfrow=c(Dv+1,Dv+1), mar=c(2,3,0,0), oma=c(1,4,1,1), xpd=NA)
-    myplot(c(0,0), c(0,0), pch="", ann=FALSE, bty="n", yaxt="n") 
+    myplot(c(0,0), c(0,0), pch="", ann=FALSE, bty="n", yaxt="n")
  }
  for(i in 1:Dv){
      for(j in 1:Dv){
@@ -441,8 +443,8 @@ plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= N
              mtext(text=varnames[i], side = 1, line=3)
              mtext(text=varnames[i], side = 2, line=3)
            }
-        } 
+        }
-      }}  
+      }}
  mtext(text="lag distance", side=1, outer = TRUE, line=0)
  mtext(text=c("semivariogram","covariance")[cov+1], side=2, outer = TRUE, line=2)
  invisible(opar)
@@ -452,11 +454,11 @@ plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= N
 #' Check for anisotropy of a theoretical variogram
-#' 
+#'
 #' Checks for anisotropy of a theoretical variogram or covariance function model
 #' @param x variogram or covariance model object
-#' @param tol tolerance for 
+#' @param tol tolerance for
 #' @param ... extra arguments for generic  functionality
 #'
 #' @return Generic function. Returns of boolean answering the question of the name,
@@ -511,7 +513,7 @@ is.isotropic.LMCAnisCompo = function(x, tol=1e-10, ...){
 #' Variogram method for gmSpatialModel objects
-#' 
+#'
 #' Compute the empirical variogram of the conditioning data contained in a [gmSpatialModel-class] object
 #'
 #' @param object a gmSpatialModel object containing spatial data.
@@ -519,7 +521,7 @@ is.isotropic.LMCAnisCompo = function(x, tol=1e-10, ...){
 #' @param ... further parameters to [gstat::variogram()]
 #'
 #' @return Currently the function is just a convenience wrapper on
-#' the variogram calculation functionalities of package "gstat", 
+#' the variogram calculation functionalities of package "gstat",
 #' and returns objects of class "\code{gstatVariogram}". Check the
 #' help of \code{gstat::variogram} for further information.
 #' In the near future, methods will be created, which will depend on
@@ -534,15 +536,15 @@ variogram_gmSpatialModel <-  function(object, methodPars=NULL, ...){
 # Variogram calculations
-# 
+#
 # Compute empricial variograms out of a spatial data object
-# 
+#
 # @param object spatial data container
 # @param ... further parameters for variogram calculation
-# 
+#
-# @return depending on the input data, different kinds of empirical variograms 
+# @return depending on the input data, different kinds of empirical variograms
 # will be produced. See appropriate method descriptions.
-# 
+#
 # @importFrom sp variogram
 # @export
 ##variogram <- function(object, ...) UseMethod("variogram", object)
@@ -558,25 +560,25 @@ variogram_gmSpatialModel <-  function(object, methodPars=NULL, ...){
 #' Empirical variogram or covariance function in 2D
-#' 
+#'
-#' compute the empirical variogram or covariance function in a 2D case study 
+#' compute the empirical variogram or covariance function in a 2D case study
-#' 
+#'
 #' @param X matrix of Nx2 columns with the geographic coordinates
 #' @param Z matrix or data.frame of data with dimension (N,Dv)
-#' @param Ff for variogram, matrix of basis functions with nrow(Ff)=N (can be a N-vector of 1s); 
+#' @param Ff for variogram, matrix of basis functions with nrow(Ff)=N (can be a N-vector of 1s);
 #' for covariance function, a (N,Dv)-matrix or a  Dv-vector giving the mean values
 #' @param maxdist maximum lag distance to consider
 #' @param lagNr number of lags to consider
-#' @param lags if maxdist and lagNr are not specified, either: (a) a matrix of 2 columns giving 
+#' @param lags if maxdist and lagNr are not specified, either: (a) a matrix of 2 columns giving
-#' minimal and maximal lag distance defining the lag classes to consider, or (b) a vector of lag breaks 
+#' minimal and maximal lag distance defining the lag classes to consider, or (b) a vector of lag breaks
 #' @param azimuthNr number of azimuths to consider
-#' @param azimuths if azimuthNr is not specified, either: (a) a matrix of 2 columns giving 
+#' @param azimuths if azimuthNr is not specified, either: (a) a matrix of 2 columns giving
 #' minimal and maximal azimuth defining the azimuth classes to consider, or (b) a vector of azimuth breaks
 #' @param maxbreadth maximal breadth (in lag units) orthogonal to the lag direction
 #' @param minpairs minimal number of pairs falling in each class to consider the calculation sufficient; defaults to 10
 #' @param cov boolean, is covariance (TRUE) or variogram (FALSE) desired? defaults to variogram
 #'
-#' @return An empirical variogram for the provided data. NOTE: avoid using directly gsi.* functions! They 
+#' @return An empirical variogram for the provided data. NOTE: avoid using directly gsi.* functions! They
 #' represent either internal functions, or preliminary, not fully-tested functions. Use \code{\link{variogram}} instead.
 #' @export
 #' @family gmEVario functions
@@ -594,7 +596,7 @@ variogram_gmSpatialModel <-  function(object, methodPars=NULL, ...){
 #' vge["npairs",1]
 #' vge["lags",1]
 gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
-                      maxdist= max(dist(X[sample(nrow(X),min(nrow(X),1000)),]))/2, 
+                      maxdist= max(dist(X[sample(nrow(X),min(nrow(X),1000)),]))/2,
                      lagNr = 15, lags = seq(from=0, to=maxdist, length.out=lagNr+1),
                      azimuthNr=4, azimuths = seq(from=0, to=180, length.out=azimuthNr+1)[1:azimuthNr],
                      maxbreadth=Inf, minpairs=10, cov=FALSE){
@@ -611,7 +613,7 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
  }
  # expand the information given into a set of columns stating conditions
  if(length(dim(lags))==0){
-    lags = data.frame(minlag=lags[-length(lags)], maxlag=lags[-1]) 
+    lags = data.frame(minlag=lags[-length(lags)], maxlag=lags[-1])
    if(maxbreadth!=Inf) lags[,"maxbreadth"]=maxbreadth
  }else if(dim(lags)==2){
    lags = data.frame(lags)
@@ -621,21 +623,21 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
  if(length(dim(azimuths))==0){
    tol = (azimuths[2]-azimuths[1])/2
    if(is.na(tol)) tol=180
-    azimuths = data.frame(minaz=azimuths-tol, maxaz=azimuths+tol) 
+    azimuths = data.frame(minaz=azimuths-tol, maxaz=azimuths+tol)
  }else if(dim(azimuths)==2){
    azimuths = data.frame(azimuths)
    colnames(azimuths) = c("minaz","maxaz")
  }else stop("azimuths can be either a vector of lags or a data.frame, see ?gsi.EVario2D")
  # compute pairs of locations
  ij = expand.grid(1:nrow(X), 1:nrow(X))# indices
  XX = X[ij[,1],]-X[ij[,2],] # locations
-  XXabs = gmApply(XX, 1, function(x) sqrt(sum(x^2))) 
+  XXabs = gmApply(XX, 1, function(x) sqrt(sum(x^2)))
  XXaz = gmApply(XX, 1, function(x) pi/2-atan2(x[2],x[1])) +2*pi
  XXaz = XXaz %% pi  # residual to 180??
  # compute residuals and pairs of variables appropriate to the structural function
-  if(cov){ 
+  if(cov){
    if(all(dim(Z)==dim(Ff))){
      Z = as.matrix(Z-Ff)
    }else if(Dv==length(c(unlist(Ff)))){
@@ -648,8 +650,8 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
    Z = lm(as.matrix(Z)~as.matrix(Ff)+0)$residuals ## ideally this should be a GLS fit
    ZZ = Z[ij[,1],]-Z[ij[,2],]
  }
  # output
  ## ATTENTION: needs to be changed to return a structure (3,Na)-matrix of objects,
  #     like logratioVariogramAnisotropy
@@ -667,7 +669,7 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
    if(ncol(lags)>2){
      tk_b = outer(xxabs * abs(sin((xxaz-(azs[i,2]-azs[i,1])))), lags[,3], "<=")
      tk_h = tk_h & tk_b
-    } 
+    }
    n[,i] = colSums(tk_h)
    for(j in 1:Nh){
      if(!is.na(n[j,i]) && n[j,i]>minpairs){
@@ -675,7 +677,7 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
          vg[j,,,i] = gmApply((ZZ[tk_a,,][tk_h[,j],,]), c(2,3),"sum")/(n[j,i])
        }else{   # semi-variogram
          aux = rowSums(
-            gmApply(X=ZZ[tk_a,][tk_h[,j],], 1, function(x)outer(x,x,"*")) 
+            gmApply(X=ZZ[tk_a,][tk_h[,j],], 1, function(x)outer(x,x,"*"))
          )/(2*n[j,i])
          vg[j,,,i] = aux
        }
@@ -685,7 +687,7 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
    }
    return(list(gamma=vg[,,,i], lags=gsi.lagClass(lags), npairs =n[,i]))
  })
  # output
  attr(res, "directions") = gsi.azimuthInterval(azimuths)
  # attr(res, "lags") = gsi.lagClass(lags)
@@ -695,34 +697,34 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
 }
 #' Empirical variogram or covariance function in 3D
-#' 
+#'
-#' compute the empirical variogram or covariance function in a 3D case study 
+#' compute the empirical variogram or covariance function in a 3D case study
-#' 
+#'
 #' @param X matrix of Nx3 columns with the geographic coordinates
 #' @param Z matrix or data.frame of data with dimension (N,Dv)
-#' @param Ff for variogram, matrix of basis functions with nrow(Ff)=N 
+#' @param Ff for variogram, matrix of basis functions with nrow(Ff)=N
-#' (can be a N-vector of 1s; should include the vector of 1s!!); 
+#' (can be a N-vector of 1s; should include the vector of 1s!!);
 #' for covariance function, a (N,Dv)-matrix or a Dv-vector giving the mean values
 #' @param maxdist maximum lag distance to consider
 #' @param lagNr number of lags to consider
-#' @param lags if maxdist and lagNr are not specified, either: (a) a matrix of 2 columns giving 
+#' @param lags if maxdist and lagNr are not specified, either: (a) a matrix of 2 columns giving
-#' minimal and maximal lag distance defining the lag classes to consider, or (b) a vector of lag breaks 
+#' minimal and maximal lag distance defining the lag classes to consider, or (b) a vector of lag breaks
-#' @param dirvecs matrix which rows are the director vectors along which variograms will be computed (these will be normalized!) 
+#' @param dirvecs matrix which rows are the director vectors along which variograms will be computed (these will be normalized!)
-#' @param angtol scalar, angular tolerance applied (in degrees; defaults to 90??, ie. isotropic) 
+#' @param angtol scalar, angular tolerance applied (in degrees; defaults to 90??, ie. isotropic)
 #' @param maxbreadth maximal breadth (in lag units) orthogonal to the lag direction (defaults to `Inf`, ie. not used)
 #' @param minpairs minimal number of pairs falling in each class to consider the calculation sufficient; defaults to 10
 #' @param cov boolean, is covariance (TRUE) or variogram (FALSE) desired? defaults to variogram
 #'
-#' @return An empirical variogram for the provided data. NOTE: avoid using directly gsi.* functions! They 
+#' @return An empirical variogram for the provided data. NOTE: avoid using directly gsi.* functions! They
 #' represent either internal functions, or preliminary, not fully-tested functions. Use \code{\link{variogram}} instead.
-#' 
+#'
-#' 
+#'
 #' @export
 #' @family gmEVario functions
 #'
 # @examples
 # dt <- readr::read_csv("~/Geochem_sum_imp2.csv")
-# 
+#
 # X = as.matrix(dt[,c("X","Y","Z")])
 # Z = as.matrix(compositions::clr(dt[,c("Cu","Zn","Pb", "As", "Cd", "In", "Other")]))
 # dirvecs = rbind( c(1,0,0), c(1,1,0), c(0,1,0), c(-1,1,0), c(0,0,1))
@@ -736,11 +738,11 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
 # vge["lags",1]
 # plot.gmEVario(vge, varnames = colnames(Z), commonAxis=FALSE,
 #    vdir.up = 1:4, vdir.lo=5, xlim.up=c(0,50), xlim.lo=c(0,15)
-#    )  
+#    )
 ## ie. the 4 planar directions on the upper triangle,
 ## the downhole direction in the lower triangle
 gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
-                        maxdist= max(dist(X[sample(nrow(X),min(nrow(X),1000)),]))/2, 
+                        maxdist= max(dist(X[sample(nrow(X),min(nrow(X),1000)),]))/2,
                        lagNr = 15, lags = seq(from=0, to=maxdist, length.out=lagNr+1),
                        dirvecs=t(c(1,0,0)), angtol=90,
                        maxbreadth=Inf, minpairs=10, cov=FALSE){
@@ -757,15 +759,15 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
  ## expand the information given into a set of columns stating conditions
  # prepare lags
  if(length(dim(lags))==0){
-    lags = data.frame(minlag=lags[-length(lags)], maxlag=lags[-1]) 
+    lags = data.frame(minlag=lags[-length(lags)], maxlag=lags[-1])
-    # if(maxbreadth!=Inf) 
+    # if(maxbreadth!=Inf)
    lags[,"maxbreadth"] = maxbreadth
  }else if(dim(lags)==2){
    lags = data.frame(lags)
    colnames(lags) = c("minlag","maxlag","maxbreadth")[1:ncol(lags)]
  }else stop("lags can be either a vector of lags or a data.frame, see ?gsi.EVario3D")
  lagsSq = lags^2
  # prepare directions with tolerance
  if(length(dim(dirvecs))==0){
    if(length(dirvecs)!=3) stop("dirvecs must be a matrix with 3 columns!")
@@ -775,15 +777,15 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
  if(length(angtol) %in% c(1, nrow(dirvecs))){
    dirvecs = cbind(dirvecs, tol=cos(angtol*pi/180) )
  }else stop("angtol can be either a scalar or a vector of length=nrow(dirvecs), see ?gsi.EVario3D")
  # compute pairs of locations
  ij = expand.grid(1:nrow(X), 1:nrow(X))# indices
  XX = X[ij[,1],]-X[ij[,2],] # locations
  XXmodSq = gmApply(XX, 1, function(x) sum(x^2))
  XXmod = sqrt(XXmodSq)
  # compute residuals and pairs of variables appropriate to the structural function
-  if(cov){ 
+  if(cov){
    if(all(dim(Z)==dim(Ff))){
      Z = as.matrix(Z-Ff)
    }else if(Dv==length(c(unlist(Ff)))){
@@ -796,7 +798,7 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
    Z = lm(as.matrix(Z)~as.matrix(Ff)+0)$residuals ## ideally this should be a GLS fit
    ZZ = Z[ij[,1],]-Z[ij[,2],]
  }
  # output
  ## ATTENTION: needs to be changed to return a structure (3,Na)-matrix of objects,
  #     like logratioVariogramAnisotropy
@@ -816,7 +818,7 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
      residualsSq = XXmodSq[tk_a]-projections[tk_a]^2
      tk_b = outer(residualsSq, lagsSq[,3], "<=")
      tk_h = tk_h & tk_b
-    } 
+    }
    n[,i] = colSums(tk_h)
    for(j in 1:Nh){
      if(!is.na(n[j,i]) && n[j,i]>minpairs){
@@ -824,7 +826,7 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
          vg[j,,,i] = gmApply((ZZ[tk_a,,][tk_h[,j],,]), c(2,3),"sum")/(n[j,i])
        }else{   # semi-variogram
          aux = rowSums(
-            gmApply(X=ZZ[tk_a,][tk_h[,j],], 1, function(x)outer(x,x,"*")) 
+            gmApply(X=ZZ[tk_a,][tk_h[,j],], 1, function(x)outer(x,x,"*"))
          )/(2*n[j,i])
          vg[j,,,i] = aux
        }
@@ -834,7 +836,7 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
    }
    return(list(gamma=vg[,,,i], lags=gmGeostats:::gsi.lagClass(lags), npairs =n[,i]))
  })
  # output
  attr(res, "directions") = gmGeostats:::gsi.directorVector(dirvecs[,1:3])
  # attr(res, "lags") = gsi.lagClass(lags)
@@ -848,13 +850,13 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
 #' Plot empirical variograms
-#' 
+#'
 #' Flexible plot of an empirical variogram of class gmEVario
-#' 
+#'
 #' @param x object to print, of class gmEVario
 #' @param xlim.up range of X values to be used for the diagrams of the upper triangle
 #' @param xlim.lo range of X values to be used for the diagrams of the lower triangle
-#' @param vdir.up in case of anisotropic variograms, indices of the directions to be plotted 
+#' @param vdir.up in case of anisotropic variograms, indices of the directions to be plotted
 #' on the upper triangle
 #' @param vdir.lo ..., indices of the directions to be plotted on the lower triangle
 #' @param varnames variable names to be used
@@ -862,16 +864,16 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
 #' @param add boolean, add stuff to an existing diagram?
 #' @param commonAxis boolean, should vertical axes be shared by all plots in a row?
 #' @param cov boolean, is this a covariance? (if FALSE, it is a variogram)
-#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)? 
+#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)?
 #' defaults to TRUE
 #' @param ... further parameters to \code{\link{matplot}}
 #'
-#' @return invisibly, the graphical parameters active before calling the function. 
+#' @return invisibly, the graphical parameters active before calling the function.
 #' This is useful for freezing the plot if you provided `closeplot=FALSE`.
-#' 
+#'
-#' How to use arguments `vdir.lo` and `vdir.up`? Each empirical variogram \code{x} has been 
+#' How to use arguments `vdir.lo` and `vdir.up`? Each empirical variogram \code{x} has been
 #' computed along certain distances, recorded in its attributes and retrievable with command
-#' \code{\link{ndirections}}.   
+#' \code{\link{ndirections}}.
 #' @export
 #' @family gmEVario functions
 #' @method plot gmEVario
@@ -884,8 +886,8 @@ gsi.EVario3D = function(X,Z,Ff=rep(1, nrow(X)),
 #' vge = gsi.EVario2D(X,Z)
 #' plot(vge)
 #' plot(vge, pch=22, lty=1, bg="grey")
-plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= NULL, 
+plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= NULL,
-                         varnames = dimnames(x$gamma)[[2]], type="o", 
+                         varnames = dimnames(x$gamma)[[2]], type="o",
                         add=FALSE, commonAxis=TRUE, cov =attr(x,"type")=="covariance",
                         closeplot=TRUE, ...){
  Dv = dim(x[1,1][[1]])[2]
@@ -912,12 +914,12 @@ plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo=
      xlim.lo = c(0, maxdist)
    }
  }
  opar = par()
  opar = par_remove_readonly(opar)
  if(closeplot) on.exit(par(opar))
  myplot = function(...) matplot(type=type, ylab="", xlab="",xaxt="n", ...)
  if(add) myplot = function(...) matpoints(type=type, ...)
  if(!add){
@@ -931,7 +933,7 @@ plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo=
        ylim = range(sapply(vdir.lo, function(kk) x["gamma",kk][[1]][,i,j]), na.rm = TRUE)
        if(commonAxis) ylim=range(sapply(vdir.lo, function(kk) x["gamma",kk][[1]][,,j]), na.rm=TRUE)
        myplot(
-          sapply(vdir.lo, function(kk) gsi.midValues.lagClass(x["lags",kk][[1]])), 
+          sapply(vdir.lo, function(kk) gsi.midValues.lagClass(x["lags",kk][[1]])),
          sapply(vdir.lo, function(kk) x["gamma",kk][[1]][,i,j]),  ylim=ylim, ...)
        if(i==j){
          axis(side = 3)
@@ -944,15 +946,15 @@ plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo=
        ylim = range(sapply(vdir.up, function(kk) x["gamma",kk][[1]][,i,j]), na.rm = TRUE)
        if(commonAxis) ylim=range(sapply(vdir.up, function(kk) x["gamma",kk][[1]][,,j]), na.rm=TRUE)
        myplot(
-          sapply(vdir.up, function(kk) gsi.midValues.lagClass(x["lags",kk][[1]])), 
+          sapply(vdir.up, function(kk) gsi.midValues.lagClass(x["lags",kk][[1]])),
          sapply(vdir.up, function(kk) x["gamma",kk][[1]][,i,j]),  ylim=ylim, ...)
        if(i==j){
          axis(side = 1)
          mtext(text=varnames[i], side = 1, line=3)
          mtext(text=varnames[i], side = 2, line=3)
        }
-      } 
+      }
-    }}  
+    }}
  mtext(text="lag distance", side=1, outer = TRUE, line=0)
  mtext(text=c("semivariogram","covariance")[cov+1], side=2, outer = TRUE, line=2)
  attr(opar, "vdir.up")=vdir.up
@@ -965,19 +967,19 @@ plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo=
-#' Convert empirical structural function to gmEVario format 
+#' Convert empirical structural function to gmEVario format
-#' 
+#'
-#' Convert empirical covariance functions or variograms to the format gmEVario 
+#' Convert empirical covariance functions or variograms to the format gmEVario
 #' of package gmGeostats
-#' 
+#'
 #' @param vgemp variogram/covariance function to be converted
 #' @param ... further parameters
 #'
-#' @return the empirical covariance function or variogram, recasted to class 
+#' @return the empirical covariance function or variogram, recasted to class
-#' \code{gmEVario}. This is a generic function. Methods exist for objects of 
+#' \code{gmEVario}. This is a generic function. Methods exist for objects of
-#' class \code{logratioVariogram}\code{logratioVariogramAnisotropy} 
+#' class \code{logratioVariogram}\code{logratioVariogramAnisotropy}
 #' (for compositional data) and \code{gstatVariogram}
-#' (from package \code{gstat}).  
+#' (from package \code{gstat}).
 #' @export
 #' @aliases as.gmEVario.gstatVariogram as.gmEVario.logratioVariogram
 #'  as.gmEVario.logratioVariogramAnisotropy
@@ -995,26 +997,26 @@ variogramModelPlot <- function(vg, ...)  UseMethod("variogramModelPlot", vg)
 #' Quick plotting of empirical and theoretical variograms
-#' Quick and dirty plotting of empirical variograms/covariances with or without their models 
+#' Quick and dirty plotting of empirical variograms/covariances with or without their models
 #' @param vg empirical variogram or covariance function
 #' @param model optional, theoretical variogram or covariance function
 #' @param col colors to use for the several directional variograms
 #' @param commonAxis boolean, should all plots in a row share the same vertical axis?
-#' @param newfig boolean, should a new figure be created? otherwise user should ensure the device space is appropriately managed  
+#' @param newfig boolean, should a new figure be created? otherwise user should ensure the device space is appropriately managed
-#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)? 
+#' @param closeplot logical, should the plot be left open (FALSE) for further changes, or be frozen (TRUE)?
 #' defaults to TRUE
 #' @param ... further parameters to underlying plot or matplot functions
 #'
-#' @return The function is primarily called for producing a plot. However, it 
+#' @return The function is primarily called for producing a plot. However, it
-#' invisibly returns the graphical parameters active before the call 
+#' invisibly returns the graphical parameters active before the call
-#' occurred. This is useful for constructing complex diagrams, by adding layers 
+#' occurred. This is useful for constructing complex diagrams, by adding layers
 #' of info. If you want to "freeze" your plot, embed your call in another
 #' call to \code{\link{par}}, e.g. \code{par(variogramModelPlot(...))}; if you
 #' want to leave the plot open for further changes give the extra argument `closeplot=FALSE`.
 #' @export
-#' @family variogramModelPlot 
+#' @family variogramModelPlot
-#' @family gmEVario functions 
+#' @family gmEVario functions
-#' @family gmCgram functions 
+#' @family gmCgram functions
 #' @seealso [logratioVariogram()]
 #' @method variogramModelPlot gmEVario
 #'
@@ -1030,15 +1032,15 @@ variogramModelPlot <- function(vg, ...)  UseMethod("variogramModelPlot", vg)
 #' Z = as.matrix(jura.pred[,c("Zn","Cd","Pb")])
 #' vge = gsi.EVario2D(X,Z)
 #' variogramModelPlot(vge, vm)
-#' 
+#'
-#' 
+#'
 variogramModelPlot.gmEVario <- function(vg, model = NULL,   # gstat  or variogramModelList object containing a variogram model fitted to vg
                                        col = rev(rainbow(ndirections(vg))),
                                        commonAxis = FALSE,
                                        newfig = TRUE, closeplot=TRUE, ...){
  opar = plot(vg, commonAxis=commonAxis, add=!newfig, col=col, closeplot=is.null(model), ...)
  opar = par_remove_readonly(opar)
  if(closeplot) on.exit(par(opar))
  vdir.lo = attr(opar, "vdir.lo")
  vdir.up = attr(opar, "vdir.up")
@@ -1046,8 +1048,8 @@ variogramModelPlot.gmEVario <- function(vg, model = NULL,   # gstat  or variogra
    return(invisible(opar))
  # OTHERWISE: add the curves for the model
  aux = as.directorVector(attr(vg, "directions"))
-  if(!is.null(vdir.lo)) vdir.lo = aux[vdir.lo,] 
+  if(!is.null(vdir.lo)) vdir.lo = aux[vdir.lo,]
-  if(!is.null(vdir.up)) vdir.up = aux[vdir.up,] 
+  if(!is.null(vdir.up)) vdir.up = aux[vdir.up,]
  opar = plot(as.gmCgram(model), vdir.up= vdir.up, vdir.lo= vdir.lo, add=TRUE, cov =FALSE, ...)
  #f = as.function(as.gmCgram(gg))
  #Dv = dim(vg$gamma)[2]
@@ -1062,7 +1064,7 @@ variogramModelPlot.gmEVario <- function(vg, model = NULL,   # gstat  or variogra
  #      hd = seq(from=xlim[1], to=xlim[2], length.out = 200)
  #      Y = outer(hd, 1:nrow(dirs.lo), function(h,k){
  #        f(rep(0,Dg), dirs.lo[k,]*h)[i,j]
-  #      }) 
+  #      })
  #      matlines(hd, Y, col=col[vdir.lo], ...)
  #    }
  #    if((i<=j)&(!is.null(vdir.up))){
@@ -1072,26 +1074,26 @@ variogramModelPlot.gmEVario <- function(vg, model = NULL,   # gstat  or variogra
  #      hd = seq(from=xlim[1], to=xlim[2], length.out = 200)
  #      Y = outer(hd, 1:nrow(dirs.up), function(h,k){
  #        f(rep(0,Dg), dirs.up[k,]*h)[i,j]
-  #      }) 
+  #      })
  #      matlines(hd, Y, col=col[vdir.up], ...)
-  #    } 
+  #    }
-  #  }}  
+  #  }}
  invisible(opar)
-} 
+}
 #' Number of directions of an empirical variogram
-#' 
+#'
 #' Returns the number of directions at which an empirical variogram was computed
-#' 
+#'
 #' @param x empirical variogram object
 #'
-#' @return Generic function. It provides the 
+#' @return Generic function. It provides the
 #' number of directions at which an empirical variogram was computed
 #' @export
-#' @family gmEVario functions 
+#' @family gmEVario functions
-#' @family gmCgram functions 
+#' @family gmCgram functions
 #' @seealso [logratioVariogram()], [gstat::variogram()]
 ndirections <- function(x){ UseMethod("ndirections", x) }
@@ -1107,7 +1109,7 @@ ndirections.azimuth = function(x) length(x)
 #' @method ndirections azimuthInterval
 #' @export
 ndirections.azimuthInterval = function(x) length(x[[1]])
-#' @describeIn ndirections method for empirical logratio variograms with anisotropy 
+#' @describeIn ndirections method for empirical logratio variograms with anisotropy
 #' @method ndirections logratioVariogramAnisotropy
 #' @export
 ndirections.logratioVariogramAnisotropy = function(x) ndirections(attr(x,"directions"))
@@ -1124,7 +1126,7 @@ ndirections.gmEVario = function(x) ndirections(attr(x,"directions"))
 #' @export
 ndirections.gstatVariogram = function(x){
  length(unique(paste(x$dir.hor, x$dir.ver)))
-} 
+}
@@ -1135,12 +1137,12 @@ ndirections.gstatVariogram = function(x){
 gsi.azimuth = function(x){
   class(x) = c("azimuth","directionClass")
   return(x)
-} 
+}
 gsi.azimuthInterval = function(x){
  class(x) = c("azimuthInterval","directionClass")
  return(x)
-} 
+}
@@ -1155,14 +1157,14 @@ print.directionClass = function(x, complete=TRUE, ...){
  if(complete){
    print(unclass(x), ...)
  }
-} 
+}
 as.directorVector <- function(x){ UseMethod("as.directorVector",x) }
-#' @method as.directorVector default 
+#' @method as.directorVector default
 as.directorVector.default = function(x,...) x
 #' @method as.directorVector azimuth
@@ -1185,7 +1187,7 @@ as.directorVector.azimuthInterval = function(x, D=2){
 gsi.lagdists = function(x){
  class(x) = c("lagdist","lagClass")
  return(x)
-} 
+}
 gsi.lagClass = function(x){
  if(length(dim(x))!=2) stop("provided lag classes are not matrix-like!")
@@ -1209,7 +1211,7 @@ gsi.midValues.default = function(x) x
 #' @method gsi.midValues lagClass
 gsi.midValues.lagClass <- function(x){ (x[[1]]+x[[2]])/2 }
 #' @method gsi.midValues azimuthIntervals
 gsi.midValues.azimuthInterval <- function(x){ (x[[1]]+x[[2]])/2 }
@@ -1218,49 +1220,49 @@ gsi.midValues.azimuthInterval <- function(x){ (x[[1]]+x[[2]])/2 }
 ## theoretical structural functions
-# S3 -> S4 classes  
+# S3 -> S4 classes
 # cat("creating variogram model classes\n")
 # abstract classes
 #' @title Structural function model specification
-#' @description Abstract class, containing any specification of a variogram (or covariance) model. 
+#' @description Abstract class, containing any specification of a variogram (or covariance) model.
 #' Members must implement a coercion method to
-#' class "gmCgram" (see [setCgram()] for an example), and (possibly) coercion to 
+#' class "gmCgram" (see [setCgram()] for an example), and (possibly) coercion to
 #' class "variogramModel" or "variogramModelList" (see [gstat::vgm()])
 #' @export
 #' @include compositionsCompatibility.R
 #' @include gstatCompatibility.R
 #' @include preparations.R
-setClassUnion(name="ModelStructuralFunctionSpecification", 
+setClassUnion(name="ModelStructuralFunctionSpecification",
              members=c("NULL","gmCgram", "LMCAnisCompo", "variogramModelList", "variogramModel"))
-# 
+#
 # #### container class --------------
 # # An S4 class to represent a Gaussian random field specification
 # #
-# # @slot structure ModelStructuralFunctionSpecification. Variogram or 
+# # @slot structure ModelStructuralFunctionSpecification. Variogram or
 # # (generalised) covariance function specification, typically an object
 # # obtained from a call to functions such as \code{\link{setCgram}},
 # # \code{\link{LMCAnisCompo}} or \code{gstat::vgm}.
 # # @slot formula formula specifying the structure
-# # of dependence of the mean of the random field w.r.to spatial coordinates 
+# # of dependence of the mean of the random field w.r.to spatial coordinates
-# # and/or covariables; typically it will have no left-hand-side term; 
+# # and/or covariables; typically it will have no left-hand-side term;
 # # @slot beta numeric, a vector with as many coefficients as terms the formula
 # # above requires for a full specification of the trend; if unknown, these can
-# # be NAs, as many as needed. 
+# # be NAs, as many as needed.
 # #
 # # @return A object with the slots populated as given
 # # @export
 # # @seealso [gmSpatialModel-class], and the `make.gm*` functions referenced there
-# setClass("gmGaussianModel", 
+# setClass("gmGaussianModel",
 #          slots = list(structure = "ModelStructuralFunctionSpecification",
 #                       formula="formula",
 #                       beta = "structure")
 # )
-# 
+#
 # #setMethod("initialize", signature="gmGaussianModel",
 # #          def=function(.Object, structure, formula, beta){
 # #            .Object@formula = formula
@@ -1274,21 +1276,21 @@ setClassUnion(name="ModelStructuralFunctionSpecification",
 #### container class --------------
 # An S4 class to represent a Gaussian random field specification
 #
-# @slot structure ModelStructuralFunctionSpecification. Variogram or 
+# @slot structure ModelStructuralFunctionSpecification. Variogram or
 # (generalised) covariance function specification, typically an object
 # obtained from a call to functions such as \code{\link{setCgram}},
 # \code{\link{LMCAnisCompo}} or \code{gstat::vgm}.
 # @slot formula formula specifying the structure
-# of dependence of the mean of the random field w.r.to spatial coordinates 
+# of dependence of the mean of the random field w.r.to spatial coordinates
-# and/or covariables; typically it will have no left-hand-side term; 
+# and/or covariables; typically it will have no left-hand-side term;
 # @slot beta numeric, a vector with as many coefficients as terms the formula
 # above requires for a full specification of the trend; if unknown, these can
-# be NAs, as many as needed. 
+# be NAs, as many as needed.
 #
 # @return A object with the slots populated as given
 # @export
 # @seealso [gmSpatialModel-class], and the `make.gm*` functions referenced there
-setClass("gmGaussianModel", 
+setClass("gmGaussianModel",
         slots = list(structure = "ModelStructuralFunctionSpecification",
                      formula="formula",
                      beta = "numeric")
@@ -1302,9 +1304,9 @@ setClass("gmGaussianModel",
 # abstract classes
 #' @title Empirical structural function specification
-#' @description Abstract class, containing any specification of an empirical variogram 
+#' @description Abstract class, containing any specification of an empirical variogram
 #' (or covariance function, or variations). Members must implement a coercion method to
-#' class "gmEVario" (see [gsi.EVario2D()] for an example), and (possibly) coercion to 
+#' class "gmEVario" (see [gsi.EVario2D()] for an example), and (possibly) coercion to
 #' class "gstatVariogram" (see [gstat::variogram()])
 #' @export
 #' @include compositionsCompatibility.R