diff --git a/.Rbuildignore b/.Rbuildignore
index f083d4911f2c0f376b2d9b6e18d2e6f5d51787ea..a4a756064a454fcc3ad6642ce96ed8651785a8ef 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -15,3 +15,4 @@ jurapred
^README\.Rmd$
^.*\.vscode$
^revdep$
+^\./vignettes/SimulatingMicrostructures\.Rmd$
diff --git a/DESCRIPTION b/DESCRIPTION
index c6be49bf6d188ea71692194fa9b22770f4944a3e..56e030c288498f3ccca4c81f440b5c03f73e34db 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: gmGeostats
Version: 0.11.3
-Date: 2023-04-13
+Date: 2023-04-17
Title: Geostatistics for Compositional Analysis
Authors@R: c(person(given = "Raimon",
family = "Tolosana-Delgado",
@@ -43,8 +43,8 @@ Imports:
utils,
RColorBrewer
Description: Support for geostatistical analysis of multivariate data,
- in particular data with restrictions, e.g. positive amounts data,
- compositional data, distributional data, microstructural data, etc.
+ in particular data with restrictions, e.g. positive amounts,
+ compositions, distributional data, microstructural data, etc.
It includes descriptive analysis and modelling for such data, both
from a two-point Gaussian perspective and multipoint perspective.
The methods mainly follow Tolosana-Delgado, Mueller and van den
diff --git a/NAMESPACE b/NAMESPACE
index 7dc1d8801fc2ebc80f6f8771af27554beffae6b8..70940acc5804c4ddcf99c1b68547db92505d2cca 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -41,11 +41,15 @@ S3method(as.LMCAnisCompo,gmCgram)
S3method(as.LMCAnisCompo,gstat)
S3method(as.LMCAnisCompo,variogramModelList)
S3method(as.array,DataFrameStack)
+S3method(as.directorVector,azimuth)
+S3method(as.directorVector,azimuthInterval)
+S3method(as.directorVector,default)
S3method(as.function,gmCgram)
S3method(as.gmCgram,LMCAnisCompo)
S3method(as.gmCgram,default)
S3method(as.gmCgram,variogramModel)
S3method(as.gmCgram,variogramModelList)
+S3method(as.gmEVario,default)
S3method(as.gmEVario,gstatVariogram)
S3method(as.gmEVario,logratioVariogram)
S3method(as.gmEVario,logratioVariogramAnisotropy)
@@ -184,6 +188,7 @@ export(as.AnisotropyScaling)
export(as.CompLinModCoReg)
export(as.DataFrameStack)
export(as.LMCAnisCompo)
+export(as.directorVector)
export(as.gmCgram)
export(as.gmEVario)
export(as.gmSpatialModel)
diff --git a/NEWS.md b/NEWS.md
index cf2cff7a3b0ef8552b237231d5501048d0ea1ccd..f0a7ed08c4696194b13fe062837af59682671bbf 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,6 @@
# gmGeostats 0.11.3
+* (2023-04-17) functions producing multiple plots now restore the graphical parameters to initial state if terminated; minor corrections to help files; stub vignette "Simulating microstructures with gmGeostats" is not packaged any more with gmGeostats to CRAN, but can be found here (until it is finished)
* (2023-04-13) batch of hidden methods exported to the namespace (mostly for `as.*()` functions converting between different representations of empirical variograms and of geostatistical models)
* (2023-04-04) C routines called as symbols and not name strings; non-ASCII characters removed
diff --git a/R/Anamorphosis.R b/R/Anamorphosis.R
index e6a81df0929ef2a2d46fd24d173a634e10debe80..43b984e91f257f9c9dac7b617dd5518ad56b4468 100644
--- a/R/Anamorphosis.R
+++ b/R/Anamorphosis.R
@@ -268,12 +268,13 @@ anaBackward <- function(x,Y,sigma0,sigma1=1+sigma0,steps=30,plt=FALSE,sphere=TRU
#' library(compositions)
#' data("jura", package="gstat")
#' Y = acomp(jura.pred[,c(10,12,13)])
-#' par(mfrow=c(1,1))
+#' oldpar = par(mfrow = c(1,1))
#' plot(Y)
#' sph = sphTrans(Y)
#' class(sph)
#' z = sph(Y)
#' plot(z)
+#' par(oldpar)
#' cor(cbind(z, ilr(Y)))
#' colMeans(cbind(z, ilr(Y)))
sphTrans <- function(Y,...) UseMethod("sphTrans",Y)
diff --git a/R/accuracy.R b/R/accuracy.R
index eb13e61423d34af16676571d530b100d4a31f06b..a00c17149ae8a79929bbd7ed64546a3771a5f441 100644
--- a/R/accuracy.R
+++ b/R/accuracy.R
@@ -311,6 +311,10 @@ precision.accuracy <- function(x, ...){
plot.accuracy <- function(x, xlim=c(0,1), ylim=c(0,1), xaxs="i", yaxs="i", type="o", col="red", asp=1,
xlab="confidence", ylab="coverage", pty="s", main="accuracy plot",
colref=col[1], ...){
+
+ oldpar <- par(no.readonly = TRUE)
+ on.exit(par(oldpar))
+
par(pty=pty)
plot(x$p, x$accuracy, xaxs=xaxs, yaxs=yaxs, xlim=xlim, ylim=ylim, type=type, col=col, asp=asp,
xlab=xlab, ylab=ylab, main=main, ...)
@@ -338,7 +342,18 @@ xvErrorMeasures <- function(x,...) UseMethod("xvErrorMeasures", x)
#' @param output which output do you want? a vector of one or several of c("ME","MSE","MSDR","Mahalanobis")
#' @param ... extra arguments for generic functionality
#'
+#' @return If just some of c("ME","MSE","MSDR") are requested, the output is a named
+#' vector with the desired quantities. If only "Mahalanobis" is requested, the output is a vector
+#' of Mahalanobis square errors. If you mix up things and ask for "Mahalanobis" and some of
+#' the quantities mentioned above, the result will be a named list with the requested quantities.
#'
+#' @details "ME" stands for *mean error* (average of the differences between true values and predicted values),
+#' "MSE" stands for *mean square error* (average of the square differences between true values and predicted values),
+#' and "MSDR" for *mean squared deviation ratio* (average of the square between true values and predicted values
+#' each normalized by its kriging variance). These quantities are classically used in evaluating
+#' output results of validation exercises of one single variable.
+#' For multivariate cases, see [xvErrorMeasures.data.frame()].
+#'
#' @export
#' @method xvErrorMeasures default
#' @family accuracy functions
@@ -378,7 +393,7 @@ xvErrorMeasures.default <- function(x, krigVar, observed, output="MSDR1", ...){
#' "MSE" stands for *mean square error* (average of the square differences between true values and predicted values),
#' and "MSDR" for *mean squared deviation ratio* (average of the square between true values and predicted values
#' each normalized by its kriging variance). These quantities are classically used in evaluating
-#' output results of validation excercices of one single variable.
+#' output results of validation exercises of one single variable.
#' For multivariate cases, "ME" (a vector) and "MSE" (a scalar) work as well,
#' while two different definitions of a multivariate
#' mean squared deviation ratio can be given:
diff --git a/R/compositionsCompatibility.R b/R/compositionsCompatibility.R
index 7af1848e417047d175e0690b97d3ef0a4c4262c0..ada9ee0bc19e39571cb55c707905543a03deb621 100644
--- a/R/compositionsCompatibility.R
+++ b/R/compositionsCompatibility.R
@@ -306,7 +306,9 @@ logratioVariogram1 = function(comp, loc, maxdist=max(dist(loc))/2,
#' @param ... additional arguments for generic functionality (currently ignored)
#'
#' @return This function is called for its effect of producing a figure.
-#' Additionally, the graphical parameters active *prior to calling this function* are returned invisibly.
+#' Additionally, the graphical parameters active *prior to calling this function* are
+#' returned invisibly.
+#'
#' @export
#'
#' @examples
@@ -323,7 +325,9 @@ image.logratioVariogramAnisotropy = function(x, jointColor=FALSE, breaks=NULL,
probs = seq(0,1,0.1), col = spectralcolors,
...){
lrvg = x
- o = par()
+ opar = par(no.readonly = TRUE)
+ on.exit(opar)
+
# construct the polar grid
r = attr(lrvg, "lags")
rlim = range(r)
@@ -368,7 +372,7 @@ image.logratioVariogramAnisotropy = function(x, jointColor=FALSE, breaks=NULL,
}
}
}}
- invisible(o)
+ invisible(opar)
}
#################################################
@@ -480,9 +484,7 @@ plot.logratioVariogramAnisotropy = function(x, azimuths=colnames(x),
}
# set the matrix of figures
- opar = par()
- opar = par_remove_readonly(opar)
-
+ opar = par(no.readonly = TRUE)
if(closeplot) on.exit(par(opar))
diff --git a/R/data.R b/R/data.R
index 707db276150505ad1d24b5549026f786b7f5c8fc..7bb51789ab8643dec4d256ad1e03826a202684d3 100644
--- a/R/data.R
+++ b/R/data.R
@@ -260,8 +260,8 @@ getTellus <- function(wd=".", destfile = "TellusASoil.RData" , TI = FALSE, clean
success = FALSE
wd0 = getwd()
- setwd(wd)
on.exit(setwd(wd0))
+ setwd(wd)
if(is.logical(TI)){
TIfile = "Tellus_TI.RData"
diff --git a/R/exploratools.R b/R/exploratools.R
index fb33facfe7e204222874add18ae9d25b6fcff2a0..5c48d9e3653f95cca27941d60d7fe8cdecf3c554 100644
--- a/R/exploratools.R
+++ b/R/exploratools.R
@@ -89,9 +89,7 @@ pwlrmap = function(loc, # XY coordinates (matrix or data frame)
){
# set of maps where the symbols are chosen according to each possible pwlr, in
# a scale given by the user
- opar = par()
- opar = par_remove_readonly(opar)
-
+ opar = par(no.readonly = TRUE)
if(closeplot) on.exit(par(opar))
# dimensions
D = ncol(comp)
@@ -226,8 +224,7 @@ pairsmap.default <- function(data, # data to represent
closeplot=TRUE,
...
){
- opar = par()
- opar = par_remove_readonly(opar)
+ opar = par(no.readonly = TRUE)
if(closeplot) on.exit(par(opar))
# dimensions
D = ncol(data)
@@ -427,9 +424,7 @@ swath.default <- function(data, # data (matrix, rmult, aplus, rplus or data.fra
if(is(data, "Spatial")) return(swath_SpatialPointsDataFrame(data=data, loc=loc, pch=pch, xlab=xlab, mfrow=mfrow,
withLoess=withLoess, commonScale=commonScale, ...))
# preparations
- opar = par()
- opar = par_remove_readonly(opar)
-
+ opar = par(no.readonly = TRUE)
on.exit(par(opar))
col0 = spectralcolors(10)[10]
@@ -498,9 +493,7 @@ swath.acomp <- function(data, # composition (rcomp, acomp, ccomp)
# set of swath plots for each possible pwlr, eventually with a loess line
# preparations
- opar = par()
- opar = par_remove_readonly(opar)
-
+ opar = par(no.readonly = TRUE)
on.exit(par(opar))
col0 = spectralcolors(10)[10]
comp = data
diff --git a/R/gmSimulation.R b/R/gmSimulation.R
index 67625138a049c3cc0c2a026c03af7917e1358da0..63d12bb5e88155a04b44d198baa70dc975b47dcb 100644
--- a/R/gmSimulation.R
+++ b/R/gmSimulation.R
@@ -96,7 +96,7 @@ gsi.DS4CoDa <- function(n, f, t, n_realiz, nx_TI, ny_TI, nx_SimGrid, ny_SimGrid,
#pb = list()
#myfun = function(ii){
for(ii in 1:n_realiz){
- cat(paste("\n Realization number #",ii, "\n"))
+ message(paste("\n Realization number #",ii))
# Defining a fully random path for simulation
list_sim <- which(maskArray[,,1] & is.na(SimGrid_ilr_list[[ii]][,,1]), arr.ind = TRUE)
@@ -330,7 +330,7 @@ gsi.DS <- function(n, f, t, n_realiz,
#pb = list()
#myfun = function(ii){
for(ii in 1:n_realiz){
- cat(paste("\n Realization number #",ii, "\n"))
+ message(paste("\n Realization number #",ii))
# Defining a fully random path for simulation
list_sim <- which(mask_array[,,,drop=T] & is.na(SimGrid_list[[ii]][,,,1]), arr.ind = TRUE)
diff --git a/R/gmSpatialModel.R b/R/gmSpatialModel.R
index c5a1d38e23d5710df8f3c1fe82533e079b9d0efe..bf385815e566b64ca143804146ae3086e3d016c4 100644
--- a/R/gmSpatialModel.R
+++ b/R/gmSpatialModel.R
@@ -455,7 +455,7 @@ setMethod("Predict",signature(object="gmSpatialModel", newdata="ANY"),
#' @export
setMethod("Predict",signature(object="gmSpatialModel", newdata="ANY", pars="gmNeighbourhoodSpecification"),
function(object, newdata, pars, ...){
- cat("starting cokriging \n")
+ message("starting cokriging")
object@parameters = pars
out = predict(as.gstat(object), newdata=newdata, ...)
return(out)
@@ -469,7 +469,7 @@ setMethod("Predict",signature(object="gmSpatialModel", newdata="ANY", pars="gmNe
setMethod("Predict",signature(object="gmSpatialModel", newdata="ANY", pars="gmTurningBands"),
function(object, newdata, pars, ...){
stop("Turning Bands method not yet interfaced here; use")
- cat("starting turning bands \n")
+ message("starting turning bands")
}
)
@@ -479,7 +479,7 @@ setMethod("Predict",signature(object="gmSpatialModel", newdata="ANY", pars="gmTu
setMethod("Predict",signature(object="gmSpatialModel", newdata="ANY", pars="gmCholeskyDecomposition"),
function(object, newdata, pars, ...){
stop("Choleski decomposition method not yet implemented")
- cat("starting Choleski decomposition \n")
+ message("starting Choleski decomposition")
}
)
@@ -490,7 +490,7 @@ setMethod("Predict",signature(object="gmSpatialModel", newdata="ANY", pars="gmCh
#' @export
setMethod("Predict",signature(object="gmSpatialModel", newdata="ANY", pars="gmSequentialSimulation"),
function(object, newdata, pars, ...){
- cat("starting SGs \n")
+ message("starting SGs")
object@parameters = pars$ng
erg = predict(as.gstat(object), newdata=newdata, nsim=pars$nsim, debug.level=pars$debug.level, ...)
Dg = ncol(object@coords)
@@ -512,7 +512,7 @@ setMethod("Predict",signature(object="gmSpatialModel", newdata="ANY", pars="gmSe
setMethod("Predict",signature(object="gmSpatialModel", newdata="ANY", pars="gmDirectSamplingParameters"),
function(object, newdata, pars, ...){
- cat("starting direct sampling \n")
+ message("starting direct sampling")
# extract training image
gt.ti = sp::getGridTopology(object@model)
dt.ti = as(object@model,"SpatialGridDataFrame")@data
diff --git a/R/grids.R b/R/grids.R
index c5aa3c38b50fb54f7a8ba2d7f81ee3b710f72efe..14c00b8089c4e8bd96d091624522ae0b3ff09c38 100644
--- a/R/grids.R
+++ b/R/grids.R
@@ -467,6 +467,9 @@ image_cokriged.default <- function(x, ivar=3,
if(legendPos) legendPos = "top"
}
if(!is.logical(legendPos)){
+ oldpar <- par(no.readonly = TRUE)
+ on.exit(par(oldpar))
+
# make sure where the color legend goes
par(oma=c(1,1,1,1))
if(legendPos %in% c("top","left")){
diff --git a/R/gstatCompatibility.R b/R/gstatCompatibility.R
index 7d0e6ebb04b111e9905f9a1b3ddc64bc28012b08..59a9b5b4489d1bcec1f3da82001a52c3a2035617 100644
--- a/R/gstatCompatibility.R
+++ b/R/gstatCompatibility.R
@@ -264,10 +264,9 @@ variogramModelPlot.gstatVariogram =
}
}
d = length(vrnames)
- # plot empirical vario!
- opar = par()
- opar = par_remove_readonly(opar)
+ # plot empirical vario!
+ opar = par(no.readonly = TRUE)
if(closeplot) on.exit(par(opar))
if(newfig) par(mfrow=c(d,d), mar=c(1,1,1,1)+0.5, oma=c(0,3,3,0))
diff --git a/R/mask.R b/R/mask.R
index c0744b096309a0043ce61079ceb8d25146359d37..59de4e27d269d9b5cddb94445e3011ff55dad23e 100644
--- a/R/mask.R
+++ b/R/mask.R
@@ -28,7 +28,7 @@
#' xyz.df = data.frame(xy, z = rnorm(29*23)*ifelse(abs(xy$x-xy$y)<3, 1, NA)+(xy$x+xy$y)/2)
#' mask.df = constructMask(grid = xy, method = "maxdist", maxval = 3, x=xyz.df)
#' image(mask.df)
-#' par(mfrow=c(1,1))
+#' oldpar = par(mfrow = c(1,1))
#' mask.df
#' xyz.df.masked = setMask(xyz.df, mask.df)
#' dim(xyz.df.masked)
@@ -46,17 +46,20 @@
#' aux = sp::SpatialPixelsDataFrame(grid = xy.gt, data=xyz.df, points = xy.sp)
#' xyz.sgdf = as(aux, "SpatialGridDataFrame")
#' image_cokriged(xyz.sgdf, ivar="z")
+#' ## reorder the data in the grid and plot again
#' par(mfrow=c(1,1))
#' ms = function(x) sortDataInGrid(x, grid=xy.gt)
#' mask.gt = constructMask(grid = xy.gt, method = "maxdist", maxval = 3, x=xyz.sgdf)
#' image(x,y,matrix(ms(xyz.sgdf@data$z), nrow=23, ncol=29))
#' image(x,y,matrix(ms(mask.gt), nrow=23, ncol=29))
#' image(mask.gt)
+#' ## work with the mask and plot again
#' par(mfrow=c(1,1))
#' xyz.sgdf.masked = setMask(x = xyz.sgdf, mask = mask.gt)
#' getMask(xyz.sgdf.masked)
#' image(x,y,matrix(ms(xyz.sgdf@data$z), nrow=23, ncol=29))
#' points(xyz.sgdf.masked@coords)
+#' par(oldpar)
constructMask = function(grid, method="maxdist", maxval=NULL, x=NULL){
methods = c("maxdist", "sillprop", "point2poly")
m = methods[pmatch(method, methods)]
diff --git a/R/variograms.R b/R/variograms.R
index f1c3fbdc46864c4fb02f04c02921d962f4971e15..b3a0d8b4f441ba55b796304688888c0e4a2cdc4a 100644
--- a/R/variograms.R
+++ b/R/variograms.R
@@ -389,9 +389,7 @@ plot.gmCgram = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo= N
xseq.lo = seq(from=xlim.lo[1], to=xlim.lo[2], length.out=xlength)
}else{ xseq.lo=NULL}
- opar = par()
- opar = par_remove_readonly(opar)
-
+ opar = par(no.readonly = TRUE)
if(closeplot) on.exit(par(opar))
getVdens = function(vdir, xseq){
@@ -936,9 +934,7 @@ plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo=
}
}
- opar = par()
- opar = par_remove_readonly(opar)
-
+ opar = par(no.readonly = TRUE)
if(closeplot) on.exit(par(opar))
myplot = function(...) matplot(type=type, ylab="", xlab="",xaxt="n", ...)
@@ -1071,13 +1067,13 @@ variogramModelPlot.gmEVario <- function(vg, model = NULL, # gstat or variogra
if(is.null(model))
return(invisible(opar))
# OTHERWISE: add the curves for the model
- aux = as_directorVector(attr(vg, "directions"))
+ aux = as.directorVector(attr(vg, "directions"))
if(!is.null(vdir.lo)) vdir.lo = aux[vdir.lo,]
if(!is.null(vdir.up)) vdir.up = aux[vdir.up,]
opar = plot(as.gmCgram(model), vdir.up= vdir.up, vdir.lo= vdir.lo, add=TRUE, cov =FALSE, ...)
#f = as.function(as.gmCgram(gg))
#Dv = dim(vg$gamma)[2]
- #dirs = as_directorVector(attr(vg, "directions"))
+ #dirs = as.directorVector(attr(vg, "directions"))
#Dg = ncol(dirs)
#for(i in 1:Dv){
# for(j in 1:Dv){
@@ -1177,7 +1173,7 @@ gsi.directorVector = function(x){
}
print.directionClass = function(x, complete=TRUE, ...){
- cat(paste("with",nrow(as_directorVector(x)),"directions\n"))
+ cat(paste("with",nrow(as.directorVector(x)),"directions\n"))
if(complete){
print(unclass(x), ...)
}
@@ -1185,14 +1181,30 @@ print.directionClass = function(x, complete=TRUE, ...){
-# @export
-as_directorVector <- function(x, ...){ UseMethod("as_directorVector",x) }
+#' Express a direction as a director vector
+#'
+#' Internal methods to express a direction (in 2D or 3D) as director
+#' vector(s). These functions are not intended for direct use.
+#'
+#' @param x value of the direction in a certain representation
+#' @param ... extra parameters for generic functionality
+#'
+#' @return A 2- or 3- column matrix which rows represents the unit
+#' director vector of each direction specified.
+#' @export
+as.directorVector <- function(x, ...) UseMethod("as.directorVector",x)
+
-#' @method as_directorVector default
-as_directorVector.default = function(x) x
+#' @describeIn as.directorVector default method
+#' @method as.directorVector default
+#' @export
+as.directorVector.default = function(x, ...) x
-#' @method as_directorVector azimuth
-as_directorVector.azimuth = function(x, D=2){
+#' @describeIn as.directorVector method for azimuths
+#' @method as.directorVector azimuth
+#' @param D dimension currently used (D=2 default; otherwise D=3; other values are not accepted)
+#' @export
+as.directorVector.azimuth = function(x, D=2, ...){
res = cbind(cos(pi/2-x), sin(pi/2-x))
if(D>2){
res = cbind(res, matrix(0, ncol=D-2, nrow=nrow(res)))
@@ -1201,10 +1213,12 @@ as_directorVector.azimuth = function(x, D=2){
return(gsi.directorVector(res))
}
-#' @method as_directorVector azimuthInterval
-as_directorVector.azimuthInterval = function(x, D=2, ...){
+#' @describeIn as.directorVector method for azimuthIntervals
+#' @method as.directorVector azimuthInterval
+#' @export
+as.directorVector.azimuthInterval = function(x, D=2, ...){
res = (x[[1]]+x[[2]])/2
- return(as_directorVector.azimuth(res))
+ return(as.directorVector.azimuth(res))
}
diff --git a/cran-comments.md b/cran-comments.md
index 8c004ceddbae6827cd2a138c81b9025372078b66..033351d3ee07ceeb83e6b6e35f2c5440e5b0ab60 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -18,67 +18,134 @@
## This is a resubmission, after archiving on 2023-04-05
-### Changes occurring after submission on 2023-04-13 15:57
+### Comments by Victoria Wimmer on 2023-04-14
+
+The following issues have been corrected or commented here inline:
```
-Dear maintainer,
-
-package gmGeostats_0.11.3.tar.gz does not pass the incoming checks automatically, please see the following pre-tests:
-Windows: <https://win-builder.r-project.org/incoming_pretest/gmGeostats_0.11.3_20230413_155730/Windows/00check.log>
-Status: 1 NOTE
-Debian: <https://win-builder.r-project.org/incoming_pretest/gmGeostats_0.11.3_20230413_155730/Debian/00check.log>
-Status: 2 NOTEs
+Thanks,
-Please fix all problems and resubmit a fixed version via the webform.
+Please proof-read your description text.
+Currently it reads: "... positive amounts data ..."
+Probably it should rather be: "... positive amounts of data ..."
+```
-(...)
+ANSWER: Description proofread and slightly changed. The specific sentence was correct as it was: it is about data constituted by positive numbers, i.e. amounts. Hopefully the new description is clearer.
-Flavor: r-devel-linux-x86_64-debian-gcc
```
+Please add \value to .Rd files regarding exported methods and explain the functions results in the documentation. Please write about the structure of the output (class) and also what the output means. (If a function does not return a value, please document that too, e.g. \value{No return value, called for side effects} or similar)
+Missing Rd-tags:
+ xvErrorMeasures.default.Rd: \value
+```
+
+ANSWER: Done. Also a section Details has been added.
-Following changes done:
+```
+\dontrun{} should only be used if the example really cannot be executed (e.g. because of missing additional software, missing API keys, ...) by the user. That's why wrapping examples in \dontrun{} adds the comment ("# Not run:") as a warning for the user.
+Does not seem necessary.
+Please unwrap the examples if they are executable in < 5 sec, or replace \dontrun{} with \donttest{}.
+```
-* Check: S3 generic/method consistency, Result: NOTE
-Mismatches for apparent methods not registered:
-as.directorVector:
- function(x, ...)
-as.directorVector.default:
- function(x)
-as.directorVector:
- function(x, ...)
-as.directorVector.azimuth:
- function(x, D)
+ANSWER: The following instances of \dontrun{} were found. All of them had to be preserved for the specific reasons mentioned below. Please advice specifically to other strategies if this is not acceptable.
-as.directorVector renamed to as_directorVector, as it is only an internal function
+noSpatCorr.test() is a very slow function, even with the small reproducible example given in the examples section;.
+getTellus() downloads a data set from an internet location; \dontrun{} is necessary as to avoid writing in the user's filespace.
-* Apparent methods for exported generics not registered:
+validate.test() is also a very slow function.
-as.gmEVario.default --> added to NAMESPACE
+sortDataInGrid() and image_cokriged() examples both depend on getTellus() in order to run, and running needs to be avoided for the same reason.
+write.GSLib() also generates a foreign file in the user's file space, giving problems if run (as getTellus() above).
-Apologies for the repeated errors. We cannot reproduce them in our Ubuntu systems, and hence cannot check completely that they disappeared.
-
-
-Additionally, the following words are all correct:
-
-Possibly misspelled words in DESCRIPTION:
- Boogaart (36:10)
- Geostatistics (4:8)
- Tolosana (35:36)
- geostatistical (30:26)
- microstructural (32:51)
- multipoint (34:52)
-
+gsi.DS() is also a very slow function.
+
+The overarching problem is that, dealing with spatial data, examples quickly become quite complex (and computations several minutes long) if they have to be relevant and intuitive for users to understand.
+
+
+```
+You write information messages to the console that cannot be easily suppressed. It is more R like to generate objects that can be used to extract the information a user is interested in, and then print() that object.
+Instead of print()/cat() rather use message()/warning() or if(verbose)cat(..) (or maybe stop()) if you really have to write text to the console.
+(except for print, summary, interactive functions)
+```
+
+ANSWER: No print() usage was found outside print() or summary() methods.
+Some cat() were found in print() or summary() methods, and left as they were. All other cat() messages were converted to message() as requested.
+
+```
+Please ensure that your functions do not write by default or in your examples/vignettes/tests in the user's home filespace (including the package directory and getwd()). This is not allowed by CRAN policies.
+Please omit any default path in writing functions. In your examples/vignettes/tests you can write to tempdir().
+```
+
+ANSWER: We only found two functions writing to the user homespace, write.GSLib() and getTellus(), and both are doing that at the request of the user; write.GSLib() is just a write.*() function, and getTellus() downloads an illustration data set from a server of the Geological Survey of Northern Ireland. This solution had to be adopted for licensing reasons, as the GSNI allows to use the data but not to redistribute it. We are not sure which other function/vignette might be meant here.
+
+```
+Please make sure that you do not change the user's options, par or working directory. If you really have to do so within functions, please ensure with an *immediate* call of on.exit() that the settings are reset when the function is exited. e.g.:
+...
+oldpar <- par(no.readonly = TRUE) # code line i
+on.exit(par(oldpar)) # code line i + 1
+...
+par(mfrow=c(2,2)) # somewhere after
+...
+
+...
+old <- options() # code line i
+on.exit(options(old)) # code line i+1
+...
+options(digits = 3)
+...
+
+...
+oldwd <- getwd() # code line i
+on.exit(setwd(oldwd)) # code line i+1
+...
+setwd(...) # somewhere after
+...
+If you're not familiar with the function, please check ?on.exit. This function makes it possible to restore options before exiting a function even if the function breaks. Therefore it needs to be called immediately after the option change within a function.
+
+Please always make sure to reset to user's options(), working directory or par() after you changed it in examples and vignettes and demos. --> man/constructMask.Rd; man/sphTrans.Rd
+e.g.:
+oldpar <- par(mfrow = c(1,2))
+...
+par(oldpar)
+
+Please fix and resubmit.
+
+Best,
+Victoria Wimmer
+```
+
+ANSWER: The following instances of par() or options() were found
+
+pwlrmap(), pairsmap.default(), plot.gmCgram(), plot.gmEVario(), variogramModelPlot.gstatVariogram(), plot.logratioVariogramAnisotropy() : these functions implement the `on.exit(par(opar))` solution, albeit conditionally on the user not wanting the device to remain open. This is achieved by a logical flag `closeplot` which default value is TRUE, hence giving the requested behavour. We have added the extra argument `no.readonly = TRUE` to `opar = par()` so that now `oldpar<-par(...)` and `on.exit(...)` are consecutive as requested.
+
+swath.default(), swath.acomp(), image.logratioVariogramAnisotropy(), plot.accuracy(): We have added the extra argument `no.readonly = TRUE` to `opar = par()` so that now `oldpar<-par(...)` and `on.exit(...)` are consecutive as requested.
+
+About options(): only a package-wide edit of options occurs. This is introduced in .onLoad() and removed on .onUnload(). Please advice if this is not the correct way to do it.
+
+About working directory manipulations: this only occurs at the request of the user by calling function getTellusData() (see above as well). This function now implements the proposed solution of
+```
+ wd0 = getwd()
+ on.exit(setwd(wd0))
+ setwd(wd) # wd is an argument to getTellusData() provided by the user.
+```
+
+The occurrences in man/constructMask.Rd and man/sphTrans.Rd were also corrected.
+
+```
+Additionally:
+Have the issues why your package was archived been fixed?
+Please explain this in the submission comments.
+```
+
+ANSWER: Yes, these are reported in the next section, "Original request".
best regards
### Original request
-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.
+
+ANSWER: 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 the same problems as well, and it took some time to correct it under macos ARM flavor.
```
Dear maintainer,
diff --git a/vignettes/SimulatingMicrostructures.Rmd b/vignettes/SimulatingMicrostructures.Rmd
index 8c49b67e71bf45753a4473b1ff5675bd32dfcc9d..24fc3de956049613e753a985e74ae7f41fbdad4a 100644
--- a/vignettes/SimulatingMicrostructures.Rmd
+++ b/vignettes/SimulatingMicrostructures.Rmd
@@ -2,7 +2,7 @@
title: "Simulating microstructures with gmGeostats"
output: rmarkdown::html_vignette
vignette: >
- %\VignetteIndexEntry{my-vignette}
+ %\VignetteIndexEntry{Simulating microstructures with gmGeostats}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---