KVote: Function for calculating the majority vote
In ArdernHB/KnnDist: Kernal Nearest Neighbour classification with distance inputs

This function carried out k-NN analyses on the provided data.

1	KVote(X, K, Weighting = FALSE, TieBreaker = c("Random", "Remove", "Report"))

`X`	a vector or matrix of the nearest neighbours. If `Weighting=TRUE` then a matrix is expected with the first column being the membership names of nearest neighbours and the second column should be the distances to each neighbour; if `Weighting=FALSE` then the just a vector of the membership names of nearest neighbours is required.
`K`	is the number of nearest neighbours that the method will use for assigning group classification.
`Weighting`	is a logical TRUE or FALSE determining whether classification will be based on a weighted K. In this method a simple weighting system is used where weights are 1/distance, where distances is the distance of the considered neighbour from the unknown.
`TieBreaker`	is the method used to break ties if there is no majority resulting from K. Three methods are available('Random', 'Remove' and 'Report'): Random randomly returns one of tied classifications; Remove returns 'UnIDed' for the classification; Report returns a the multiple classifications as a single character string with tied classifications separated by '_'. NOTE: for correct cross-validation proceedures the results of both Report will be considered an incorrect identification even if one of the multiple reported classifications is correct.