Nothing

```
#' @export thin.algorithm
#' @title Implements random spatial thinning algorithm
#'
#' @description \code{thin.algorithm} implements a randomization approach to
#' spatially thinning species occurence data. This function is the algorithm underlying
#' the \code{\link{thin}} function.
#'
#' @param rec.df.orig A data frame of long/lat points for each presence record. The
#' data.frame should be a two-column data frame, one column of long and one of
#' lat
#' @param thin.par Thinning parameter - the distance (in kilometers) that you want
#' records to be separated by.
#' @param reps The number of times to repete the thinning process. Given the random
#' process of removing nearest-neighbors there should be 'rep' number of different
#' sets of coordinates.
#' @return reduced.rec.dfs: A list object of length 'rep'. Each list element is a different
#' data.frame of spatially thinned presence records.
#'
thin.algorithm <- function(rec.df.orig, thin.par, reps) {
## Create an empty list object of length `reps` to store thinned occs datasets
reduced.rec.dfs <- vector("list", reps)
## Calculate square distance matrix AND identify which elements
## are less than the thin parameter
## ***
## Distances calculated using fields::rdist.earthThis function
## which returns distances, correcting for change in distance
## of degree of longitude based on distance from equator
DistMat.save <- rdist.earth(x1=rec.df.orig, miles=FALSE) < thin.par
## Set the diagonal of the dist matrix to FALSE values
diag(DistMat.save) <- FALSE
## Set any NA values in the dist matrix to FALSE
DistMat.save[is.na(DistMat.save)] <- FALSE
## Calculate the row sums of the DistMat.save object
## This returns the number of elements that are less than
## the thin.par for each row
SumVec.save <- rowSums(DistMat.save)
## Make a vector of TRUE values of length equal to the number
## of rows in the DistMat
df.keep.save <- rep(TRUE, length(SumVec.save))
for (Rep in seq_len(reps)) {
## For each iteration in reps, reset the DistMat and
## other indicator variables to original values
DistMat <- DistMat.save
SumVec <- SumVec.save
df.keep <- df.keep.save
## Perform while loop based on two criteria
## 1. The minimum distance between two occurences is less than the
## thinning parameter
## 2. The number of rows in the resulting data set is greater than 1
while (any(DistMat) && sum(df.keep) > 1) {
## Identify the row(s) (occurence) that is within the thin.par distance
## to the greatest number of other occurrences.
## If there is more than one row, choose one at random to remove
RemoveRec <- which(SumVec == max(SumVec))
if (length(RemoveRec) > 1) {
RemoveRec <- sample(RemoveRec, 1)
}
## Assuming the row chosen above is removed, decrease the
## SumVec object by how many other rows are influenced by its removal
SumVec <- SumVec - DistMat[, RemoveRec]
## Set the SumVec value for the row to be removed equal to 0
SumVec[RemoveRec] <- 0L
## Set the occ to be ignored in the next iteration of the while loop
DistMat[RemoveRec, ] <- FALSE
DistMat[, RemoveRec] <- FALSE
## Note the occurence for removal from the thinned data set
df.keep[RemoveRec] <- FALSE
}
## Make the new, thinned, data set
rec.df <- rec.df.orig[df.keep, , drop=FALSE]
colnames(rec.df) <- c("Longitude", "Latitude")
reduced.rec.dfs[[Rep]] <- rec.df
}
## Order the list object of thinned records by most records
## to least
reduced.rec.order <- unlist(lapply(reduced.rec.dfs, nrow))
reduced.rec.order <- order(reduced.rec.order, decreasing = TRUE)
reduced.rec.dfs <- reduced.rec.dfs[reduced.rec.order]
return(reduced.rec.dfs)
}
```

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