Nothing

```
#' Create a weighted co-occurrence matrix (wecoma)
#'
#' @param x A matrix with categories
#' @param w A matrix with weights
#' @param neighbourhood The number of directions in which cell adjacencies are considered as neighbours:
#' 4 (rook's case) or 8 (queen's case). The default is 4.
#' @param classes A vector or a list with the values of selected classes from the `x` object.
#' It is used to calculate wecoma only for selected classes.
#' @param fun Function to calculate values from adjacent cells to contribute to output matrix, `"mean"` - calculate average values from adjacent cells of weight matrix, `"geometric_mean"` - calculate geometric mean values from adjacent cells of weight matrix, or `"focal"` assign value from the focal cell.
#' @param na_action Decides on how to behave in the presence of missing values in `w`. Possible options are `"replace"`, `"omit"`, `"keep"`. The default, `"replace"`, replaces missing values with 0, `"omit"` does not use cells with missing values, and `"keep"` keeps missing values.
#'
#' @return A weighted co-occurrence matrix
#' @export
#'
#' @examples
#' library(comat)
#' data(raster_x, package = "comat")
#' data(raster_w, package = "comat")
#'
#' wom = get_wecoma(raster_x, raster_w)
#' wom
#'
#' get_wecoma(raster_x, raster_w, classes = list(c(1, 3)))
get_wecoma = function(x, w, neighbourhood = 4, classes = NULL, fun = "mean", na_action = "replace"){
if (is.null(classes)){
classes = get_unique_values(x, TRUE)
}
if (inherits(classes, c("integer", "numeric"))){
classes = list(classes)
}
directions = as.matrix(neighbourhood)
rcpp_get_wecoma_internal(x, w, directions, classes[[1]], fun, na_action)
}
```

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