fixes for CRAN

R/AAAA.R

@@ -1,6 +1,6 @@
 
 setClass("spLearner", slots = c(spModel = "ANY", vgmModel = "list", covariates = "SpatialPixelsDataFrame", spID = "SpatialGridDataFrame", quantregModel = "ANY"), validity = function(object) {
-    if(!class(object@vgmModel$observations)=="SpatialPointsDataFrame")
+    if(!methods::is(object@vgmModel$observations, "SpatialPointsDataFrame"))
       return("Expecting an object of class 'SpatialPointsDataFrame'")
     cn = c("cov.model", "lambda", "practicalRange")
     if(!all(cn %in% names(object@vgmModel$vgm))){

R/buffer.dist.R

@@ -15,7 +15,7 @@
 #' @return object of class \code{SpatialPixelsDataFrame} with distances to points
 #' @export
 #'
-#' @author \href{https://opengeohub.org/people/tom-hengl}{Tom Hengl}
+#' @author \href{https://opengeohub.org/people/tom-hengl/}{Tom Hengl}
 #'
 #' @references
 #' \itemize{

R/getSpatialTiles.R

@@ -13,7 +13,7 @@
 #' @return List of object result of clipping
 #' @export
 #'
-#' @author \href{https://opengeohub.org/people/tom-hengl}{Tom Hengl}
+#' @author \href{https://opengeohub.org/people/tom-hengl/}{Tom Hengl}
 #'
 #' @examples
 #' library(sp)
@@ -77,7 +77,7 @@ setMethod("getSpatialTiles", signature(obj = "Spatial"), function(obj, block.x,
 #' @export
 setMethod("getSpatialTiles", signature(obj = "ANY"), function(obj, block.x, block.y = block.x, overlap.percent = 0, limit.bbox = TRUE, return.SpatialPolygons = FALSE){
 
-  if(!class(obj)=="GDALobj"){
+  if(!methods::is(obj, "GDALobj")){
     stop("Object of class \"GDALobj\" required.")
   }
 

R/landgis.R

@@ -11,7 +11,7 @@
 #' @return Locally downloaded GeoTIFF.
 #' @export
 #'
-#' @author \href{https://opengeohub.org/people/tom-hengl}{Tom Hengl}
+#' @author \href{https://opengeohub.org/people/tom-hengl/}{Tom Hengl}
 #'
 #' @examples
 #' search.landgis(pattern=c("clay", "10..10cm"))
@@ -23,7 +23,7 @@ download.landgis <- function(coverageId, filename, scalefactor=NULL, subset=NULL
   wcs.url <- paste0(service, '&request=GetCoverage&coverageId=', coverageId, subset, scalefactor)
   x <- utils::download.file(wcs.url, filename, quiet = silent, ...)
   try( obj <- rgdal::GDALinfo(filename, silent = silent) )
-  if(!class(obj)=="GDALobj"){
+  if(!methods::is(obj, "GDALobj")){
     ## download from zenodo?
     x <- search.landgis(strsplit(coverageId, ":")[[1]][2])
     warning(paste("WCS request exceed memory limitations. Try downloading from zenodo:\n", x[[2]]))

R/makeTiles.R

@@ -11,7 +11,7 @@
 #' @return A regular tiling system
 #' @export
 #'
-#' @author \href{https://opengeohub.org/people/tom-hengl}{Tom Hengl}
+#' @author \href{https://opengeohub.org/people/tom-hengl/}{Tom Hengl}
 makeTiles <- function(bb, block.x, block.y, overlap.percent, limit.bbox, columns = NULL, rows = NULL){
 
   ## number of tiles:

R/spc.R

@@ -14,7 +14,7 @@
 #'
 #' @note This method assumes that the input covariates are cross-correlated and hence their overlap can be reduced. The input variables are scaled by default and the missing values will be replaced with 0 values to reduce loss of data due to missing pixels.
 #'
-#' @author \href{https://opengeohub.org/people/tom-hengl}{Tom Hengl}
+#' @author \href{https://opengeohub.org/people/tom-hengl/}{Tom Hengl}
 #'
 #' @examples
 #' if(requireNamespace("plotKML", quietly = TRUE)){

R/spfkm.R

@@ -15,7 +15,7 @@
 #' @return A fuzzy kmeans model
 #' @export
 #'
-#' @author \href{https://opengeohub.org/people/tom-hengl}{Tom Hengl}
+#' @author \href{https://opengeohub.org/people/tom-hengl/}{Tom Hengl}
 #'
 #' @examples
 #' if(requireNamespace("plotKML")){
@@ -60,8 +60,8 @@ setMethod("spfkm", signature(formulaString = "formula", observations = "SpatialP
   ## if available, use class centres:
   check_tc <- !is.null(class.c)&!is.null(class.sd)
   if(check_tc){
-    if(!class(class.c)=="matrix"){ stop("Object of type 'matrix' with column names for covariates and row names correspodning to the class names required") }
-    if(!class(class.sd)=="matrix"){ stop("Object of type 'matrix' with column names for covariates and row names correspodning to the class names required") }
+    if(!methods::is(class.c, "matrix")){ stop("Object of type 'matrix' with column names for covariates and row names correspodning to the class names required") }
+    if(!methods::is(class.sd, "matrix")){ stop("Object of type 'matrix' with column names for covariates and row names correspodning to the class names required") }
     mout = list(NULL)
   }
   ## otherwise, estimate class centres using the multinomial logistic regression:

R/spsample.prob.R

@@ -23,7 +23,7 @@
 #'   \item Royle, J.A., Chandler, R.B., Yackulic, C. and J. D. Nichols. (2012) Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions. Methods in Ecology and Evolution. \doi{10.1111/j.2041-210X.2011.00182.x}
 #' }
 #'
-#' @author \href{https://opengeohub.org/people/tom-hengl}{Tom Hengl}
+#' @author \href{https://opengeohub.org/people/tom-hengl/}{Tom Hengl}
 #'
 #' @examples
 #' if(requireNamespace("plotKML", quietly = TRUE) &

R/tile.R

@@ -25,7 +25,7 @@
 #'
 #' @return Regular tiling system
 #'
-#' @author \href{https://opengeohub.org/people/tom-hengl}{Tom Hengl}
+#' @author \href{https://opengeohub.org/people/tom-hengl/}{Tom Hengl}
 #' @export
 #' @docType methods
 setMethod("tile", signature(x = "RasterLayer"), function(x, y, block.x, tmp.file = TRUE, program, show.output.on.console = FALSE, ...){
@@ -125,11 +125,11 @@ setMethod("tile", signature(x = "RasterLayer"), function(x, y, block.x, tmp.file
     }
     layername <- basename(sub("[.][^.]*$", "", outname, perl=TRUE))
 
-    if(class(x)=="SpatialPolygonsDataFrame"){
+    if(methods::is(x, "SpatialPolygonsDataFrame")){
       try(system(paste(program, '-where \"OGR_GEOMETRY=\'Polygon\'\" -f \"ESRI Shapefile\"', RSAGA::set.file.extension(outname, ".shp"), RSAGA::set.file.extension(tf, ".shp"), '-clipsrc',  y[j,1], y[j,2], y[j,3], y[j,4], '-skipfailures'), show.output.on.console = show.output.on.console))
       try(x.lst[[j]] <- rgdal::readOGR(normalizePath(RSAGA::set.file.extension(outname, ".shp")), layername, verbose = FALSE))
     }
-    if(class(x)=="SpatialLinesDataFrame"){
+    if(methods::is(x, "SpatialLinesDataFrame")){
       try(system(paste(program, '-where \"OGR_GEOMETRY=\'Linestring\'\" -f \"ESRI Shapefile\"', RSAGA::set.file.extension(outname, ".shp"), RSAGA::set.file.extension(tf, ".shp"), '-clipsrc',  y[j,1], y[j,2], y[j,3], y[j,4], '-skipfailures'), show.output.on.console = show.output.on.console))
       try(x.lst[[j]] <- rgdal::readOGR(normalizePath(RSAGA::set.file.extension(outname, ".shp")), layername, verbose = FALSE))
     }

R/train.spLearner.R

@@ -23,7 +23,7 @@
 #' @return Object of class \code{spLearner}
 #' @export
 #'
-#' @author \href{https://opengeohub.org/people/tom-hengl}{Tom Hengl}
+#' @author \href{https://opengeohub.org/people/tom-hengl/}{Tom Hengl}
 train.spLearner.matrix <- function(observations, formulaString, covariates, SL.library, family = stats::gaussian(), method = "stack.cv", predict.type, super.learner, subsets = 5, lambda = 0.5, cov.model = "exponential", subsample = 10000, parallel = "multicore", cell.size, id = NULL, weights = NULL, quantreg = TRUE, ...){
   #if(!.Platform$OS.type=="unix") { parallel <- "seq" }
   tv <- all.vars(formulaString)[1]
@@ -155,7 +155,7 @@ setMethod("train.spLearner", signature(observations = "data.frame", formulaStrin
 #' @return object of class \code{spLearner}, which contains fitted model, variogram model and spatial grid
 #' used for Cross-validation.
 #'
-#' @author \href{https://opengeohub.org/people/tom-hengl}{Tom Hengl}
+#' @author \href{https://opengeohub.org/people/tom-hengl/}{Tom Hengl}
 #'
 #' @note By default uses oblique coordinates (rotated coordinates) as described in Moller et al. (2020; \doi{10.5194/soil-6-269-2020}) to account for geographical distribution of values.
 #' By setting the \code{nearest = TRUE}, distances to nearest observations and values of nearest neighbors will be used (see: Sekulic et al, 2020; \doi{10.3390/rs12101687}). This method closely resembles geostatistical interpolators such as kriging.
@@ -580,14 +580,14 @@ setMethod("predict", signature(object = "spLearner"), predict.spLearner)
   rm.dupl = TRUE
 )
 {
-  if (class(locations) == "SpatialPoints" ||
-      class(locations) == "SpatialPointsDataFrame" ||
-      class(locations) == "SpatialPixelsDataFrame") {
+  if (methods::is(locations, "SpatialPoints") ||
+      methods::is(locations, "SpatialPointsDataFrame") ||
+      methods::is(locations, "SpatialPixelsDataFrame") ) {
     locations <- sp::coordinates(locations)
   } else {
     locations <- locations[, locations.x.y]
   }
-  if (class(observations) == "SpatialPoints" || class(observations) == "SpatialPointsDataFrame") {
+  if (methods::is(observations, "SpatialPoints") || methods::is(observations, "SpatialPointsDataFrame")) {
     variable <- observations[[zcol]]
     observations <- sp::coordinates(observations)
   } else {

R/tune.spLearner.R

@@ -18,7 +18,7 @@
 #'
 #' @return optimized object of type spLearner
 #'
-#' @author \href{https://opengeohub.org/people/tom-hengl}{Tom Hengl}
+#' @author \href{https://opengeohub.org/people/tom-hengl/}{Tom Hengl}
 #'
 #' @note Currently requires that two base learners are \code{regr.ranger} and
 #' \code{regr.xgboost}, and that there are at least 3 base learners in total.