diff --git a/R/accuracy.R b/R/accuracy.R
index 8a91cbcde192760ac6e0e9d74e4db88701bc37f6..62cf5cdaac414449ef9bef433fbca1009a75dd39 100644
--- a/R/accuracy.R
+++ b/R/accuracy.R
@@ -58,7 +58,7 @@
 #' method "mahalanobis".
 #' 
 #' @export
-#' @family {accuracy functions} 
+#' @family accuracy functions
 accuracy <- function(x,...) UseMethod("accuracy", x)
 
 
@@ -207,7 +207,7 @@ accuracy.DataFrameStack <- function(x, observed,
 #' @return The value of the mean accuracy after Deutsch (1997); details
 #' can be found in \code{\link[=precision.accuracy]{precision()}}.
 #' @export
-#' @family {accuracy functions} 
+#' @family accuracy functions
 mean.accuracy = function(x, ...){
   aux = x$accuracy - x$p
   mean(ifelse(aux>0,1,0),...)
@@ -245,7 +245,7 @@ precision <- function(x,...) UseMethod("precision",x)
 #' To consider the whole curve, goodness can be used
 #' \deqn{G = 1-\int_{0}^{1} (\pi_i-p_i)\cdot (3\cdot I\{(\pi_i-p_i)>0\}-2) dp.}
 #' 
-#' @family {accuracy functions} 
+#' @family accuracy functions
 #' @method precision accuracy
 #' @export
 precision.accuracy <- function(x, ...){
@@ -283,7 +283,7 @@ precision.accuracy <- function(x, ...){
 #' @export
 #' @method plot accuracy
 #' 
-#' @family {accuracy functions} 
+#' @family accuracy functions
 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], ...){
@@ -332,7 +332,7 @@ xvErrorMeasures <- function(x,...) UseMethod("xvErrorMeasures", x)
 #' 
 #' @export
 #' @method xvErrorMeasures data.frame
-#' @family {accuracy functions} 
+#' @family accuracy functions
 xvErrorMeasures.data.frame = function(x, observed=x$observed, output="MSDR1",
                            univariate=length(dim(observed))==0, ...){
   if(length(output)>1)
diff --git a/R/compositionsCompatibility.R b/R/compositionsCompatibility.R
index 162c8bbc2b68218cce5ca84f46daedbd26580fbd..73db8ccc66b4f89496922bbf61f9ae9f7b418a26 100644
--- a/R/compositionsCompatibility.R
+++ b/R/compositionsCompatibility.R
@@ -1043,7 +1043,7 @@ gsi.produceV = function(V=NULL, D=nrow(V),
 #' of info. If you want to "freeze" your plot, embed your call in another
 #' call to \code{\link{par}}, e.g. \code{par(variogramModelPlot(...))}.
 #' @export
-#' @family {variogramModelPlot}
+#' @family variogramModelPlot
 #' @method variogramModelPlot logratioVariogram
 variogramModelPlot.logratioVariogram <- function(vg, model = NULL,   # gstat  or variogramModelList object containing a variogram model fitted to vg
                                         col = rev(rainbow(ndirections(vg))),
diff --git a/R/genDiag.R b/R/genDiag.R
index 8e6ca3c1f46db1032f8a80fa01a462a74fdfa303..03cacf97a0de174b9441b3bf0868a327c3cf95e1 100644
--- a/R/genDiag.R
+++ b/R/genDiag.R
@@ -113,7 +113,7 @@ Maf = function(x,...) UseMethod("Maf",x)
 #' @method Maf data.frame
 #' @aliases genDiag
 #' 
-#' @family {genDiag}
+#' @family generalised Diagonalisations
 #'
 #' @examples
 #' require("magrittr")
@@ -406,7 +406,7 @@ RJD.rcomp <- RJD.acomp
 #' @return A data set or compositional object of the nature of the original data
 #' used for creating the genDiag object.
 #' @export
-#' @family {genDiag}
+#' @family generalised Diagonalisations
 #' @examples
 #' data("jura", package="gstat")
 #' juracomp = compositions::acomp(jura.pred[, -(1:6)]) 
@@ -447,7 +447,7 @@ predict.genDiag = function (object, newdata=NULL, ...) {
 #'
 #' @return nothing. Function is called exclusively to produce the plot
 #' @export
-#' @family {genDiag}
+#' @family generalised Diagonalisations
 #' @importFrom compositions coloredBiplot
 #' @method coloredBiplot genDiag
 #'
diff --git a/R/gmSpatialModel.R b/R/gmSpatialModel.R
index 437d4e97c83defd27b4455239f927f1db96964d7..9546f38924e76ae9a2922a6afdc5b8a4839b2f7c 100644
--- a/R/gmSpatialModel.R
+++ b/R/gmSpatialModel.R
@@ -14,7 +14,7 @@
 #' @slot proj4string see [sp::SpatialPointsDataFrame()]
 #' @slot model gmUnconditionalSpatialModel. Some unconditional geospatial model. It can be NULL. 
 #' @slot parameters gmSpatialMethodParameters. Some method parameters. It can be NULL
-#' @family {gmSpatialModel} 
+#' @family gmSpatialModel
 #'
 #' @return You will seldom create the spatial model directly. Use instead the creators `make.gm*` linked below
 #' @export
@@ -62,7 +62,7 @@ setClass("gmSpatialModel", contains="SpatialPointsDataFrame",
 #'
 #' @return A "gmSpatialModel" object with all information provided appropriately structured. See [gmSpatialModel-class].
 #' @export
-#' @family {gmSpatialModel} 
+#' @family gmSpatialModel 
 #' @seealso [SequentialSimulation()], [TurningBands()] or [CholeskyDecomposition()] for specifying the exact 
 #' simulation method and its parameters, [predict.gmSpatialModel()] for running predictions or simulations
 #'
@@ -118,7 +118,7 @@ make.gmMultivariateGaussianSpatialModel <- function(
 #'
 #' @return A "gmSpatialModel" object with all information provided appropriately structured. See [gmSpatialModel-class].
 #' @export
-#' @family {gmSpatialModel} 
+#' @family gmSpatialModel
 #' @seealso [SequentialSimulation()], [TurningBands()] or [CholeskyDecomposition()] for specifying the exact 
 #' simulation method and its parameters, [predict.gmSpatialModel()] for running predictions or simulations
 #'
@@ -179,7 +179,7 @@ make.gmCompositionalGaussianSpatialModel <- function(
 #'
 #' @return  A "gmSpatialModel" object with all information provided appropriately structured. See [gmSpatialModel-class].
 #' @export
-#' @family {gmSpatialModel} 
+#' @family gmSpatialModel 
 #' @seealso [DirectSamplingParameters()] for specifying a direct simulation method parameters,
 #' [predict.gmSpatialModel()] for running the simulation
 make.gmCompositionalMPSSpatialModel = function(
@@ -328,7 +328,7 @@ as.gstat.gmSpatialModel <- function(object, ...){
 #'
 #' @return The same spatial object re-structured as a "gmSpatialModel", see [gmSpatialModel-class]
 #' @export
-#' @family {gmSpatialModel}
+#' @family gmSpatialModel
 as.gmSpatialModel <- function(object, ...) UseMethod("as.gmSpatialModel", object)
 
 #' @describeIn as.gmSpatialModel Recast spatial object to gmSpatialModel format
@@ -385,7 +385,7 @@ as.gmSpatialModel.gstat = function(object, V=NULL, ...){
 #' direct sampling is available: it can be obtained by providing some parameter object created with a call to
 #' [DirectSamplingParameters()]. Currently it is also necessary that `newdata` is a gridded set of locations.  
 #' @export
-#' @family {gmSpatialModel} 
+#' @family gmSpatialModel 
 predict.gmSpatialModel <-  function(object, newdata=NULL, pars=object@parameters, ...){
   # deal with (co)kriging
   if(is(pars, "gmNeighbourhoodSpecification")){
diff --git a/R/gmValidationStrategy.R b/R/gmValidationStrategy.R
index 262a9d7df2cca38ca8cfca260a69e6779b422280..6cfcdda424ef54308c7b8fe9f35d94732bacf08b 100644
--- a/R/gmValidationStrategy.R
+++ b/R/gmValidationStrategy.R
@@ -17,7 +17,7 @@
 #' @return An object, a list with an appropriate class, controlling the strategy specified. 
 #' This can be of class "NfoldCrossValidation" or of class  c("LeaveOneOut", "NfoldCrossValidation").
 #' @export
-#' @family {validation functions}
+#' @family validation functions
 #'
 #' @examples
 #' NfoldCrossValidation(nfolds=5, doAll=FALSE)
@@ -34,7 +34,7 @@ NfoldCrossValidation = function(nfolds=2, doAll=TRUE, ...){
 #' 
 #' @return an object of class c("LeaveOneOut", "NfoldCrossValidation") to be used
 #' in a call to [validate()]
-#' @family {validation functions}
+#' @family validation functions
 #' 
 #' @export
 #' @examples 
@@ -59,8 +59,8 @@ LeaveOneOut = function(){
 #' @return A data frame of predictions (possibly with kriging variances and covariances, or equivalent 
 #' uncertainty measures) for each element of the validation set
 #' @export
-#' @family {validation functions} 
-#' @family {accuracy functions}
+#' @family validation functions 
+#' @family accuracy functions
 #'
 #' @examples
 #' data("Windarling")
diff --git a/R/gstatCompatibility.R b/R/gstatCompatibility.R
index 4c572d197a20ce6838bd82e5a3b416d2f5f3d0cd..e76069f4ce69ddb5dca6eca899fdae5b7b650f87 100644
--- a/R/gstatCompatibility.R
+++ b/R/gstatCompatibility.R
@@ -159,7 +159,7 @@ getGstatData = function(gg # gstat object
 #' @importFrom gstat vgm
 #' @seealso `gstat::plot.gstatVariogram()`
 #' @method variogramModelPlot gstatVariogram
-#' @family {variogramModelPlot}
+#' @family variogramModelPlot
 #' @examples
 #' data("jura", package="gstat")
 #' X = jura.pred[,1:2]
diff --git a/R/mask.R b/R/mask.R
index 6ac6d068f6814da7572ed7383459ca0665d7b4d3..51685164feb6c531a7ebc5d27f8844c6200c390d 100644
--- a/R/mask.R
+++ b/R/mask.R
@@ -18,7 +18,7 @@
 #' analysis. Finally, method 'point2poly' created the mask by taking the points internal
 #' to a "SpatialPolygon" object (given in \code{x}).   
 #' @export
-#' @family {masking}
+#' @family masking functions
 #'
 #' @examples
 #' ## with data.frame
@@ -188,7 +188,7 @@ gsi.masking.polygon = function(grid, poly){
 #' @param x a masked object 
 #' @return The retrieved mask information from `x`, an object of class "mask" 
 #' @export
-#' @family {masking}
+#' @family masking functions
 getMask = function(x) UseMethod(generic = "getMask", x)
 
 #' @describeIn getMask Get the mask info out of a spatial data object
@@ -230,7 +230,7 @@ getMask.SpatialPointsDataFrame = function(x) attr(x@data, "mask")
 #'
 #' @return the summary of number of nodes inside/outside the mask
 #' @export
-#' @family {masking}
+#' @family masking functions
 print.mask <- function(x,...){
   print("mask active")
   print(summary(x))
@@ -247,7 +247,7 @@ print.mask <- function(x,...){
 #'
 #' @return The object `x` appropriately masked (for the setter methods). 
 #' @export
-#' @family {masking}
+#' @family masking functions
 setMask <- function(x,...) UseMethod("setMask", x)
 
 
@@ -367,7 +367,7 @@ unmask <- function(x,...) UseMethod("unmask", x)
 #' in `x` and in `fullgrid` be cross-checked to ensure that they are given in 
 #' compatible orders? See [sortDataInGrid()] and [setGridOrder()] for controlling
 #' the ordering of vectors and grids.
-#' @family {masking}
+#' @family masking functions
 #' @method unmask data.frame
 #'
 #' @return The original grid data and extend potential
diff --git a/R/variograms.R b/R/variograms.R
index db7322743ae9368a82d5aab54db6d93ea82e30c1..a7a4c66bc073ed67f21e1eaa6e7d7398e0c3bc4a 100644
--- a/R/variograms.R
+++ b/R/variograms.R
@@ -26,7 +26,7 @@
 #' 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 
 #' vg.Gauss vg.Sph vg.sph vg.Spherical gsi.validModels
@@ -74,7 +74,7 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
 #' is obtained by summing (+) two `gmCgram` objects.
 #' @export
 #' @method [[ gmCgram
-#' @family {gmCgram}
+#' @family gmCgram functions
 #' @examples
 #' utils::data("variogramModels")
 #' v1 = setCgram(type=vg.Gau, sill=diag(2), anisRanges = 3*diag(c(3,1)))
@@ -177,7 +177,7 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
 #' variogram matrices, use the `[`-notation.
 #' @export
 #' @method [ gmCgram
-#' @family {gmCgram}
+#' @family gmCgram functions
 #' @examples
 #' utils::data("variogramModels")
 #' v1 = setCgram(type=vg.Gau, sill=diag(2), anisRanges = 3*diag(c(3,1)))
@@ -209,7 +209,7 @@ setCgram = function(type, nugget=sill*0, sill, anisRanges, extraPar=0){
 #' be also valid for the sill).
 #' @export
 #' @aliases ncol.gmCgram nrow.gmCgram
-#' @family {gmCgram}
+#' @family gmCgram functions
 #'
 #' @method length gmCgram
 #' @examples
@@ -245,7 +245,7 @@ nrow.gmCgram = function(x) nrow(x$nugget)
 #' than the geographic dimension of the variogram (i.e. \code{dim(x$M)[3]}).
 #' @export
 #' @method as.function gmCgram
-#' @family {gmCgram}
+#' @family gmCgram functions
 #' @examples
 #' utils::data("variogramModels")
 #' v1 = setCgram(type=vg.Gau, sill=diag(2)+0.5, anisRanges = 2*diag(c(3,0.5)))
@@ -292,7 +292,7 @@ predict.gmCgram = function(object, newdata, ...){
 #' \code{LMCAnisCompo} (for compositional data) and \code{variogramModelList}
 #' (as provided by package \code{gstat}).  
 #' @export
-#' @family {gmCgram}
+#' @family gmCgram functions
 as.gmCgram <- function(m, ...)  UseMethod("as.gmCgram",m) 
 
 #' @describeIn as.gmCgram Convert theoretical structural functions to gmCgram format 
@@ -324,7 +324,7 @@ as.gmCgram.default <- function(m,...) m
 #' If you want to further add stuff to it, better just call \code{plot(x,...)}. The difference
 #' is only relevant when working with the screen graphical device. 
 #' @export 
-#' @family {gmCgram}
+#' @family gmCgram functions
 #' @examples
 #' utils::data("variogramModels")
 #' v1 = setCgram(type=vg.Gau, sill=diag(3)-0.5, anisRanges = 2*diag(c(3,0.5)))
@@ -571,7 +571,7 @@ setGeneric("variogram", function(object,...) standardGeneric("variogram"))
 #' @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}
+#' @family gmEVario functions
 #'
 #' @examples
 #' library(gstat)
@@ -708,7 +708,7 @@ gsi.EVario2D = function(X,Z,Ff=rep(1, nrow(X)),
 #' computed along certain distances, recorded in its attributes and retrievable with command
 #' \code{\link{ndirections}}.   
 #' @export
-#' @family {gmEVario}
+#' @family gmEVario functions
 #'
 #' @examples
 #' library(gstat)
@@ -812,7 +812,7 @@ plot.gmEVario = function(x, xlim.up=NULL, xlim.lo=NULL, vdir.up= NULL, vdir.lo=
 #' @aliases as.gmEVario.gstatVariogram as.gmEVario.logratioVariogram
 #'  as.gmEVario.logratioVariogramAnisotropy
 #'
-#' @family {gmEVario}
+#' @family gmEVario functions
 as.gmEVario  <- function(vgemp,...){ UseMethod("as.gmEVario",vgemp)}
 as.gmEVario.default  <- function(vgemp,...) vgemp
 
@@ -839,7 +839,9 @@ variogramModelPlot <- function(vg, ...)  UseMethod("variogramModelPlot", vg)
 #' of info. If you want to "freeze" your plot, embed your call in another
 #' call to \code{\link{par}}, e.g. \code{par(variogramModelPlot(...))}.
 #' @export
-#' @family {variogramModelPlot} {gmEVario} {gmCgram} 
+#' @family variogramModelPlot 
+#' @family gmEVario functions 
+#' @family gmCgram functions 
 #' @seealso [logratioVariogram()]
 #' @method variogramModelPlot gmEVario
 #'
@@ -912,7 +914,8 @@ variogramModelPlot.gmEVario <- function(vg, model = NULL,   # gstat  or variogra
 #' @return Generic function. It provides the 
 #' number of directions at which an empirical variogram was computed
 #' @export
-#' @family {gmEVario} {gmCgram} 
+#' @family gmEVario functions 
+#' @family gmCgram functions 
 #' @seealso [logratioVariogram()], [gstat::variogram()]
 ndirections <- function(x){ UseMethod("ndirections", x) }
 ndirections.default = function(x) nrow(x)