DESCRIPTION

 Package: gmGeostats
-Version: 0.11.1
-Date: 2022-05-03
+Version: 0.11.3
+Date: 2023-04-13
 Title: Geostatistics for Compositional Analysis
 Authors@R: c(person(given = "Raimon", 
         family = "Tolosana-Delgado", 
@@ -50,7 +50,7 @@ Description: Support for geostatistical analysis of multivariate data,
          The methods mainly follow Tolosana-Delgado, Mueller and van den
          Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.
 License: CC BY-SA 4.0 | GPL (>=2)
-URL: https://gitlab.hzdr.de/geomet/gmGeostats
+URL: https://codebase.helmholtz.cloud/geomet/gmGeostats
 Copyright: (C) 2020 by Helmholtz-Zentrum Dresden-Rossendorf and Edith Cowan University;
          gsi.DS code by Hassan Talebi
 RoxygenNote: 7.1.1

NAMESPACE

@@ -325,3 +325,4 @@ useDynLib(gmGeostats,CMVTurningBands)
 useDynLib(gmGeostats,CcalcCgram)
 useDynLib(gmGeostats,anaBackwardC)
 useDynLib(gmGeostats,anaForwardC)
+useDynLib(gmGeostats,getUnitvecR)

NEWS.md

+# gmGeostats 0.11.3
+
+* (2023-04-04) C routines called as symbols and not name strings; non-ASCII characters removed
+
+# gmGeostats 0.11.2
+
+* (2022-11-22) flag '-lstdc++' removed from makevars.
+* (2022-11-15) minor bugs in accuracy calculations corrected. References improved.
+* (2022-08-15) bug in turning bands for spherical variogram corrected.
+
+
 # gmGeostats 0.11.1
 
 * (2022-05-03) minor changes to adapt to more stringent standards for class determination, for variable definition in C code, and for calls to FORTRAN routines from C code involving string arguments.

R/Anamorphosis.R

@@ -145,7 +145,7 @@ anaForward <- function(x,Y,sigma0,sigma1=1+sigma0,steps=30,plt=FALSE,sphere=TRUE
   #h=1/steps
   #if( plt )
   #    plot(x[,1:2],pch=".")
-  erg<-.C("anaForwardC",
+  erg<-.C(anaForwardC,
      dimX=checkInt(dim(x),2),
      x=checkDouble(x),
      dimY=checkInt(dim(Y),2),
@@ -228,7 +228,7 @@ anaBackward <- function(x,Y,sigma0,sigma1=1+sigma0,steps=30,plt=FALSE,sphere=TRU
   }
   Y<-t(st(Y))
   x <- t(x)
-  erg<-.C("anaBackwardC",
+  erg<-.C(anaBackwardC,
      dimX=checkInt(dim(x),2),
      x=checkDouble(x),
      dimY=checkInt(dim(Y),2),

R/accuracy.R

@@ -5,7 +5,7 @@
 #' Computes goodness-of-fit measures (accuracy, precision and joint goodness) adapted or extended from the 
 #' definition of Deutsch (1997).
 #'
-#' @param x data container for the predictions (plus cokriging error variances/covariance) or simulations (and eventually for the true values in univariate problems)
+#' @param object data container for the predictions (plus cokriging error variances/covariance) or simulations (and eventually for the true values in univariate problems)
 #' @param ... generic functionality, currently ignored
 #'
 #' @return If `outMahalanobis=TRUE` (the primary use), this function returns a two-column dataset of class
@@ -13,18 +13,18 @@
 #' the actual coverage of the intervals of each of these probabilities. If `outMahalanobis=FALSE`, the output
 #' is a vector (for prediction) or matrix (for simulation) of Mahalanobis error norms.
 #' 
-#' @details For method "kriging", `x` must contain columns with names including the string "pred" for predictions
+#' @details For method "kriging", `object` must contain columns with names including the string "pred" for predictions
 #' and "var" for the kriging variance; the observed values can also be included as an extra column with name "observed",
-#' or else additionally provided in argument `observed`. For method "cokriging", the columns of `x` must contain
+#' or else additionally provided in argument `observed`. For method "cokriging", the columns of `object` must contain
 #' predictions, cokriging variances and cokriging covariances in columns including the strings "pred", "var" resp. "cov",
 #' and observed values can only be provided via `observed` argument. Note that these are the natural formats when
 #' using [gstat::predict.gstat()] and other (co)kriging functions of that package.
 #' 
 #' For univariate and multivariate cokriging results (methods "kriging" and "cokriging"), the coverage values are computed based on the
 #' Mahalanobis square error, the (square) distance between prediction and true value, using as the positive definite bilinear form 
-#' of the distance the variance-covariance cokriging matrix. The rationale is that, under the  assumption
-#' that the random field is Gaussian, the distribution of this Mahalanobis square error is known
-#' to follow a \eqn{\chi^2(\nu)} with degrees of freedom \eqn{\nu} equal to the number of variables. Having this
+#' of the distance the variance-covariance cokriging matrix. The rationale is that, under the assumption
+#' that the random field is Gaussian, the distribution of this Mahalanobis square error should
+#' follow a \eqn{\chi^2(\nu)} with degrees of freedom \eqn{\nu} equal to the number of variables. Having this
 #' reference distribution allows us to compute confidence intervals for that Mahalanobis square error, and then 
 #' count how many of the actually observed errors are included on each one of the intervals (the *coverage*).
 #' For a perfect adjustment to the distribution, the plot of coverage vs. nominal confidence (see [plot.accuracy])
@@ -33,9 +33,9 @@
 #' from the standard normal distribution appearing by normalizing the residual with the kriging variance; the result is the
 #' same. 
 #' 
-#' For method "simulation" and object `x` is a data.frame, the variable names containing the realisations must 
-#' contain the string "sim", and `observed` must be a vector with as many elements as rows has `x`. If 
-#' `x` is a [DataFrameStack()], then it is assumed that the stacking dimension is running through the realisations;
+#' For method "simulation" and object `object` is a data.frame, the variable names containing the realisations must 
+#' contain the string "sim", and `observed` must be a vector with as many elements as rows has `object`. If 
+#' `object` is a [DataFrameStack()], then it is assumed that the stacking dimension is running through the realisations;
 #' the true values must still be given in `observed`.
 #' In both cases, the method is based on ranks:
 #' with them we can calculate which is the frequency of simulations being more extreme than the observed value.
@@ -43,7 +43,7 @@
 #' for each location separately. 
 #' 
 #' Method "mahalanobis" ("Mahalanobis" also works) is the analogous for multivariate simulations. It
-#' only works for `x` of class [DataFrameStack()], and requires the stacking dimension to run through
+#' only works for `object` of class [DataFrameStack()], and requires the stacking dimension to run through
 #' the realisations and the other two dimensions to coincide with the dimensions of `observed`, i.e.
 #' giving locations by rows and variables by columns. In this case, a covariance matrix will be computed
 #' and this will be used to compute the Mahalanobis square error defined above in method "cokriging": 
@@ -59,7 +59,12 @@
 #' 
 #' @export
 #' @family accuracy functions
-accuracy <- function(x,...) UseMethod("accuracy", x)
+#' @references Mueller, Selia and Tolosana-Delgado (2023) Multivariate cross-validation
+#' and measures of accuracy and precision. 
+#' Mathematical Geosciences (under review).
+#' 
+#' 
+accuracy <- function(object,...) UseMethod("accuracy", object)
 
 
 
@@ -68,19 +73,20 @@ accuracy <- function(x,...) UseMethod("accuracy", x)
 #' @param observed either a vector- or matrix-like object of the true values
 #' @param prob sequence of cutoff probabilities to use for the calculations
 #' @param method which method was used for generating the predictions/simulations? 
-#' one of c("kriging", "cokriging", "simulation") for `x` of class "data.frame", or of
-#' c("simulation", "mahalanobis", "flow") for `x` of class [DataFrameStack()].
+#' one of c("kriging", "cokriging", "simulation") for `object` of class "data.frame", or of
+#' c("simulation", "mahalanobis", "flow") for `object` of class [DataFrameStack()].
 #' @param outMahalanobis if TRUE, do not do the final accuracy calculations and return the Mahalanobis
 #' norms of the residuals; if FALSE do the accuracy calculations
 #' @param ivar if `method`="kriging" or "cokriging" you can also specify here one single variable name 
-#' to consider for univariate accuracy; this variable name must exist both in `x` 
+#' to consider for univariate accuracy; this variable name must exist both in `object` 
 #' (including "pred" and "var" prefixes or suffixes in the column names) and in `observed`;
 #' this might require renaming the columns of these files!
 #' @export
-accuracy.data.frame <- function(x, observed=x$observed, 
+accuracy.data.frame <- function(object, observed=object$observed, 
                                 prob = seq(from=0, to=1, by=0.05),
                                 method="kriging", outMahalanobis=FALSE, 
                                 ivar, ...){
+  x = object # after v 0.11.9003, 'accuracy' first argument is renamed to 'object' for compatibility with tidymodels 
   methods = c("kriging", "cokriging", "simulation")
   mm = methods[pmatch(method, methods)]
   if(!missing(ivar)){
@@ -88,7 +94,7 @@ accuracy.data.frame <- function(x, observed=x$observed,
     iPred = intersect(grep(ivar, colnames(x)), grep("pred", colnames(x)))
     iVar = intersect(grep(ivar, colnames(x)), grep("var", colnames(x)))
     if(any(sapply(list(iTrue, iPred, iVar), length)!=1))
-      stop("accuracy: univariate case by specifying an `ivar` requires the named variable to occur ONCE in `observed` and once in `x`, here prefixed or suffixed with `pred` and `var` to identify kriging predictions and variances")
+      stop("accuracy: univariate case by specifying an `ivar` requires the named variable to occur ONCE in `observed` and once in `object`, here prefixed or suffixed with `pred` and `var` to identify kriging predictions and variances")
     mm = "kriging"
     observed = observed[,iTrue]
     x = x[, c(iPred, iVar)]
@@ -125,7 +131,7 @@ accuracy.data.frame <- function(x, observed=x$observed,
   if(outMahalanobis)
     return(mynorms)
   # cases for kriging and cokriging
-  qqq = stats::qchisq(prob,df=D)
+  qqq = stats::qchisq(prob,df=D)   # TODO: this could be Hotelling's T^2 distributed? 
   aa = outer(mynorms,qqq,"<=")
   a = colMeans(aa)
   erg = data.frame(p=prob, accuracy=a)
@@ -139,19 +145,20 @@ accuracy.data.frame <- function(x, observed=x$observed,
 #' @param ivars in multivariate cases, a vector of names of the variables to analyse (or one single variable name)
 #' @method accuracy DataFrameStack
 #' @export
-accuracy.DataFrameStack <- function(x, observed, 
-                                    ivars = intersect(colnames(observed), dimnames(x)[[noStackDim(x)]]),
+accuracy.DataFrameStack <- function(object, observed, 
+                                    ivars = intersect(colnames(observed), dimnames(object)[[noStackDim(object)]]),
                                     prob = seq(from=0, to=1, by=0.05),
                                     method = ifelse(length(ivars)==1, "simulation", "Mahalanobis"),
                                     outMahalanobis=FALSE, ...){
+  x = object  # after v 0.11.9003, 'accuracy' first argument is renamed to 'object' for compatibility with tidymodels 
   methods = c("simulation", "Mahalanobis","mahalanobis", "flow")
   mm = methods[pmatch(method, methods)]
   oneAcc.sim = function(sims, true){
-    rks = rank(c(true,sims))
+    rks = rank(c(true,as.matrix(sims)))
     2*abs(rks[1]/(1+length(sims))-0.5)
   }
   oneAcc.assim = function(sims, true){
-    rks = rank(c(true,sims))
+    rks = rank(c(true,as.matrix(sims)))
     rks[1]/(1+length(sims))
   }
   
@@ -160,7 +167,7 @@ accuracy.DataFrameStack <- function(x, observed,
       warning("accuracy: outMahalanobis=TRUE not valid with method='simulation'")
     if(nrow(x)!=length(observed))
       if(nrow(x)!=nrow(observed))
-        stop("accuracy: dimensions of x and observed do not match")
+        stop("accuracy: dimensions of `object` and `observed` do not match")
     sims = as.matrix(gmApply(x, FUN=function(xx)xx[,ivars, drop=FALSE]))
     res = sapply(1:nrow(sims), function(i) oneAcc.sim(sims[i,], observed[i, ivars, drop=FALSE]))
     aa = outer(res, prob, "<=")
@@ -406,7 +413,7 @@ xvErrorMeasures.data.frame = function(x, observed=x$observed, output="MSDR1",
     preds = sapply(unclass(xreord), cbind)
     obs = as.matrix(observed)
     resids = preds-obs
-    if(output=="ME") return(mean(resids, na.rm=TRUE))
+    if(output=="ME") return(colMeans(resids, na.rm=TRUE))
     if(output=="MSE") return(mean(rowSums(resids^2, na.rm=T), na.rm=TRUE))
     if(output=="MSDR2"){
       myvar = function(j) covs[,j,j]

R/compositionsCompatibility.R

@@ -36,7 +36,7 @@ logratioVariogram <- function(data, ...) UseMethod("logratioVariogram", data)
 #' @param comp an alias for `data`, provided for back-compatibility with compositions::logratioVariogram 
 logratioVariogram.default <- function(data, loc, ..., comp=data){
   res = try(compositions::logratioVariogram(comp=acomp(comp), loc=loc, ...), silent=TRUE)
-  if(class(res)!="try-error") return(res)
+  if(!inherits(res, what="try-error", which=FALSE)) return(res)
   res = compositions::logratioVariogram(data=acomp(comp), loc=loc, ...)
 }
   

R/data.R

@@ -13,8 +13,8 @@
 #' * The samples were complemented with information about their belonging to one of the  
 #' major crustal blocks (MCB) of Australia.
 #' * Easting and Northing coordinates were computed using the Lambert conformal conic projection of 
-#' Australia (earth ellipsoid GRS80; standard parallels: 18°S and 36ºS latitude; central meridian: 
-#' 134ºE longitude).  
+#' Australia (earth ellipsoid GRS80; standard parallels: 18S and 36S latitude; central meridian: 
+#' 134E longitude).  
 #'
 #'
 #'#' @format A tibble, a data set of compound class c("tbl_df", "tbl", "data.frame") with 5259 
@@ -32,7 +32,7 @@
 #'   \item{REGION}{One of the three geo-regions of the data set: "EAST", "WEST" or "EUCLA"}
 #'   \item{DUPLICATE CODE}{Marker for duplicate samples, for quality control}
 #'   \item{SAMPLEID}{ID of the sample}
-#'   \item{GRAIN SIZE}{Particle size of the soil sample, one of "<2 mm" (coarse) or "<75 µm" (fine)}
+#'   \item{GRAIN SIZE}{Particle size of the soil sample, one of "<2 mm" (coarse) or "<75 um" (fine)}
 #'   \item{DEPTH}{Sampling depth, one of: "TOS" (top soil) or "BOS" (bottom soil)}
 #'   \item{CODE}{A combination of `Grain Size` and `DEPTH`, one of "Tc" "Tf" "Bc" "Bf", standing for TOS coarse, TOS fine, BOS coarse and BOS fine, respectively}
 #'   \item{`Ag ICP-MS mg/kg 0.03`}{concemtration of Silver in that sample, analysed with inductive coupled plasma mass spectrometry (ICP-MS), with a detection limit of 0.03ppm}

R/genDiag.R

@@ -131,7 +131,7 @@ Maf = function(x,...) UseMethod("Maf",x)
 #' spatial imaging, Stanford University, Palo Alto, USA, 14pp.
 #' 
 #' Tichavsky, P. and Yeredor, A., 2009. Fast approximate joint diagonalization 
-#' incorporating weight matrices. IEEE Transactions on Signal Processing 57, 878 – 891.
+#' incorporating weight matrices. IEEE Transactions on Signal Processing 57, 878 ??? 891.
 #' 
 #' @family generalised Diagonalisations
 #'
@@ -192,7 +192,7 @@ Maf.data.frame <- function(x, vg, i=2,...){
   res = list(sdev=sdevs, loadings = W, center=mn, scale=rep(1, ncol(x)),
              n.obs=nrow(x), scores = facscores, C1=C1, C2=C2, eigenvalues = eigenvalues[ord], 
              invLoadings=Winv)
-  if(class(x)!="data.frame"){
+  if(!is(x,"data.frame")){
     res$Center = cdtInv(mn)
     res$InvLoadings = cdtInv(Winv, orig=x)
     res$InvDownLoadings = cdtInv(-Winv, orig=x)
@@ -447,7 +447,7 @@ predict.genDiag = function (object, newdata=NULL, ...) {
   requireNamespace("compositions", quietly=TRUE)
   if(is.null(newdata))
     newdata = object$scores
-  if("data.frame" %in% class(newdata))
+  if(is(newdata, "data.frame"))
     newdata = as.matrix(newdata)
   Z = newdata %*% unclass(object$invLoadings)
   if("Center" %in% names(object)){
@@ -478,9 +478,9 @@ predict.genDiag = function (object, newdata=NULL, ...) {
 #' @importFrom compositions coloredBiplot
 #' @method coloredBiplot genDiag
 #' 
-#' @references Mueller, Tolosana-Delgado, Grunsky and McKinley (2021) Biplots for 
-#' Compositional Data Derived from Generalised Joint Diagonalization Methods. 
-#' Applied Computational Geosciences (under review)
+#' @references Mueller, Tolosana-Delgado, Grunsky and McKinley (2020) Biplots for 
+#' compositional data derived from generalised joint diagonalization methods. 
+#' Applied Computational Geosciences 8:100044 \doi{10.1016/j.acags.2020.100044}
 #'
 #' @examples
 #' data("jura", package="gstat")

R/geostats.R

@@ -50,7 +50,7 @@ gsi.calcCgram <- function(X,Y,vgram,ijEqual=FALSE) {
   nY<-nrow(Y)
   k <- length(vgram$type)
   dimC = c(d,nX,d,nY)
-  erg<- .C("CcalcCgram",
+  erg<- .C(CcalcCgram,
            dimX=checkInt(dim(X),2),
            ldX=checkInt(dim(X)[1],1),
            X=checkDouble(X),
@@ -70,8 +70,9 @@ gsi.calcCgram <- function(X,Y,vgram,ijEqual=FALSE) {
   structure(erg$C,dim=c(d*nX,d*nY))
 }
 
+#' @useDynLib gmGeostats getUnitvecR
 gsiGetUnitVec <- function(dimX,ip) {
-    erg = .C("getUnitvecR",dimX=checkInt(dimX,1),ip=checkInt(ip,1),unitvec=numeric(dimX))
+    erg = .C(getUnitvecR,dimX=checkInt(dimX,1),ip=checkInt(ip,1),unitvec=numeric(dimX))
     unitvec = erg$unitvec
     unitvec
     }
@@ -119,7 +120,7 @@ gsi.TurningBands <- function(X,vgram,nBands,nsim=NULL) {
   }
   # run!
   if( is.null(nsim) ) {
-    erg<-.C("CMVTurningBands",
+    erg<-.C(CMVTurningBands,
             dimX=checkInt(dim(X),2),
             X=checkDouble(X,prod(dim(X))),
             dimZ=checkInt(c(d,nrow(X),1),3),
@@ -137,7 +138,7 @@ gsi.TurningBands <- function(X,vgram,nBands,nsim=NULL) {
   } else {
     nsim <- checkInt(nsim,1)
     stopifnot(nsim>0)
-    erg<-.C("CMVTurningBands",
+    erg<-.C(CMVTurningBands,
             dimX=checkInt(dim(X),2),
             X=checkDouble(X,prod(dim(X))),
             dimZ=checkInt(c(d,nrow(X),nsim),3),
@@ -209,7 +210,7 @@ gsi.CondTurningBands <- function(Xout, Xin, Zin, vgram,
     vgram$M = M
     m = 3
   }
-  erg <- .C("CCondSim",
+  erg <- .C(CCondSim,
             dimZin=checkInt(dimZin,2),
             Zin   =checkDouble(Zin),
             Cinv  =checkDouble(Cinv,length(Zin)^2),

R/gmValidationStrategy.R

@@ -81,7 +81,7 @@ validate <- function(object, strategy, ...) UseMethod("validate", strategy)
 #' @method validate LeaveOneOut
 #' @export
 validate.LeaveOneOut = function(object, strategy, ...){
-  if("gstat" %in% class(object)){
+  if(inherits(object,what="gstat",which=FALSE)){
     n = nrow(object$data[[1]]$data)
   }else if(is(object, "gmSpatialModel")){
     n = nrow(object@data)
@@ -99,7 +99,7 @@ validate.LeaveOneOut = function(object, strategy, ...){
 #' @export
 validate.NfoldCrossValidation = function(object, strategy, ...){
   # manage "gstat" case
-  if("gstat" %in% class(object)){
+  if(is(object,"gstat")){
     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))
   }

R/gstatCompatibility.R

@@ -246,13 +246,13 @@ variogramModelPlot.gstatVariogram =
       model = lapply(noms, function(i) gstat::vgm(model="Sph", psill=0, range=1, nugget=0))
       names(model)=noms
     }else{
-      if("variogramModelList" %in% class(model)){
+      if(is(model, "variogramModelList")){
         vrnames = names(model)
         vrnames = vrnames[-grep(".", vrnames, fixed=TRUE)]
-      }else if("gstat" %in% class(model)){
+      }else if(is(model, "gstat")){
         vrnames = names(model$data)
         model = model$model
-      }else if("variogramModel" %in% class(model) ){
+      }else if(is(model,"variogramModel") ){
         vrnames = levels(model$id)[1]
       }else{
         ggtr = tryCatch(as.variogramModel(model))

R/mask.R

@@ -377,7 +377,7 @@ unmask <- function(x,...) UseMethod("unmask", x)
 #' a "unmask.DataFrameStack" produces a "unmask.DataFrameStack"; 
 #' a "SpatialPoints" produces a "SpatialPoints"; and finally
 #' a "SpatialPixels" produces either a "SpatialPixels" or a "SpatialGrid" (if it is full).
-#' Note that only in the case that `class(x)=="SpatialPixels"` is `mask` required,
+#' Note that only in the case that `is(x,"SpatialPixels")=TRUE` is `mask` required,
 #' for the other methods all arguments have reasonable defaults.
 #' @export
 unmask.data.frame <- function(x, mask=attr(x,"mask"), fullgrid = attr(mask, "fullgrid"), 

R/variograms.R

@@ -132,7 +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(is(y,"gmCgram"),
             dim(x$sill)[-1]==dim(y$sill)[-1],
             dim(x$M)[-1]==dim(y$M)[-1])
   myfun = function(A,B){

cran-comments.md

@@ -2,31 +2,54 @@
   
 * Linux: Ubuntu 18.04.4LTS  (local)
   - R 3.6.3 (production)
-  - R devel 2022-04-23 r82240 (test)
+  - R 4.2.3 patched (2023-03-29 r84146) (test)
+  - R devel (2023-04-02 r84146) (test)
+* Windows 7 Enterprise
+  - R version 4.2.2 (2022-10-31 ucrt) (test)
 
   
 ## R CMD check results
   
-  0 errors | 0 warnings | 0 notes
+  0 ERRORS
+  0 WARNINGS
+  1 NOTES (new resubmission after archiving)
   
   
-## This is a resubmission
-  
+## This is a resubmission, after archiving on 2023-04-05
+
+Request from 2023-03-19. All calls to .C() are now explicitly pointing
+to symbols instead of function namestrings. Package "gmGeostats" could 
+not be uploaded before the archiving deadline because the main dependence 
+"compositions" was showing problems as well. 
+
 ```
 Dear maintainer,
 
 Please see the problems shown on
-<https://www.stats.ox.ac.uk/pub/bdr/BLAS/gmGeostats.out>
+<https://cran.r-project.org/web/checks/check_results_gmGeostats.html>.
 
-For reproduction details see
-<https://www.stats.ox.ac.uk/pub/bdr/BLAS/README.txt> .
+The "DLL requires the use of native symbols" errors in the
+r-devel-linux-x86_64-debian-gcc check results are from
 
-Please correct before 2022-05-05 to safely retain your package on CRAN.
+r83985 | luke | 2023-03-16 02:31:57 +0100 (Thu, 16 Mar 2023) | 3 lines
+Enforce R_forceSymbols for namespaces, but for now only if an
+environment variable is set.
 
-The CRAN Team
-```
-  
-... related to the way classes were assessed (now using `inherits(...)`) and the calls of FORTRAN routines from C code that involve string arguments (now following the instructions given in <https://www.stats.ox.ac.uk/pub/bdr/BLAS/README.txt> ).
-  
+which makes R_forceSymbols work correctly, for now provided that env var
+R_REALLY_FORCE_SYMBOLS is set.
+
+You can conveniently extract the non-symbols in your package's foreign
+function calls via tools:::package_native_routine_registration_db(),
+e.g.,
+
+R> tools:::package_native_routine_registration_db("/data/rsync/PKGS/tibble")
+  cname                  s n
+1 .Call tibble_need_coerce 1
+
+but please note that there may be several such calls in your code.
+
+Please correct before 2023-04-02 to safely retain your package on CRAN.
 
-best regards
\ No newline at end of file
+Best,
+-k
+```
\ No newline at end of file

src/Makevars

 PKG_CFLAGS = $(SHLIB_OPENMP_CFLAGS)
-PKG_LIBS =  $(FLIBS) -lstdc++ $(SHLIB_OPENMP_CFLAGS)
+PKG_LIBS =  $(FLIBS) $(SHLIB_OPENMP_CFLAGS)
 
 C_OBJS = gmGeostats.o
 

src/Makevars.win

 PKG_CFLAGS = $(SHLIB_OPENMP_CFLAGS)
-PKG_LIBS =  $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) -lstdc++ $(SHLIB_OPENMP_CFLAGS)
+PKG_LIBS =  $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) $(SHLIB_OPENMP_CFLAGS)
 
 C_OBJS = gmGeostats.o
 

src/gmGeostats.c

@@ -218,8 +218,14 @@ const double *extra /* extra parameter (unused), for consistency with other cova
    * where projs were calculated. Evaluate a wave of random phase at the
    * projs points. Return that wave */	
   int i;
-  double phase,amp,omega,d1;   
-  omega = norm_rand()  * M_SQRT2 / range * M_SQRT_3; /* sqrt(3) needed to produce effective range*/
+  double phase,amp,omega,d1,o1,o2,o3;   
+  /* omega = norm_rand()  * M_SQRT2 / range * M_SQRT_3; /* sqrt(3) needed to produce effective range*/
+  // chi(3) distributed radial frequency, with random sign
+  o1 = norm_rand();
+  o2 = norm_rand();
+  o3 = norm_rand();
+  omega = sqrt(o1*o1 + o2*o2 + o3*o3) * sign(o1);
+  omega = omega * M_SQRT2 / range * M_SQRT_3; /* sqrt(3) needed to produce effective range*/
   phase = unif_rand() * M_2PI;
   amp  = M_SQRT2; /* Lantuejoul (2002), page 191 */
   for(i=0;i<n;i++) {

vignettes/register_new_layer_datatype.Rmd

@@ -236,13 +236,13 @@ theta.vg %>% recompute_complex_cov %>% plot
 ```
 This is again a quick and dirty solution, as:
 
-1. by using the plotting capacities of "gstat" for an incompplete object we waste half the space of the plot;
+1. by using the plotting capacities of "gstat" for an incomplete object we waste half the space of the plot;
 2. actually the current function can only deal with symmetric covariances (because of the way that gstat::variogram estimates the covariance), and
 3. hence the imaginary part is identically zero.
 
 ### Non-symmetric covariance
 
-Hence, we need to improve the computations slightly. First we need to consider estimates for the whole 360$^o$, so that we can obtain $C_{VU}(h)=C_{UV}(-h)$, 
+We need to improve the computations slightly. First we need to consider estimates for the whole 360$^o$, so that we can obtain $C_{VU}(h)=C_{UV}(-h)$, 
 ```{r}
 # compute variogram for the whole circle, i.e. until 360 deg
 theta.cvg = gmGeostats::variogram(
@@ -342,10 +342,10 @@ Boogaart, K. G. v. d.; Tolosana-Delgado, R. (2013) _Analysing compositional data
 Boogaart, K.G. v.d.; Tolosana-Delgado, Raimon; Bren, Matevz (2021). compositions: Compositional Data Analysis. R package version 2.0-2. http://www.stat.boogaart.de/compositions/
 
 
-De Iaco, S.; Posa, D.; Palma, M. (2013) Complex-Valued Random Fields for Vectorial Data: Estimating and Modeling Aspects. _Mathematical Geosciences_ 45: 557–573
+De Iaco, S.; Posa, D.; Palma, M. (2013) Complex-Valued Random Fields for Vectorial Data: Estimating and Modeling Aspects. _Mathematical Geosciences_ 45: 557???573
 
 
 Stevens, S. (1946) On the theory of scales of measurement. _Science_ 103: 677-680
 
 
-Wackernagel, H. (2003) _Multivariate geostatistics—an introduction with applications_, 3rd edn. Springer, Berlin 
+Wackernagel, H. (2003) _Multivariate geostatistics???an introduction with applications_, 3rd edn. Springer, Berlin