# Dist: Distance matrix In Rfast: A Collection of Efficient and Extremely Fast R Functions

Distance matrix.

## Usage

 ```1 2``` ```Dist(x, method = "euclidean", square = FALSE, p = 0,vector = FALSE) vecdist(x) ```

## Arguments

 `x` A matrix with data. The distances will be calculated between pairs of rows. In the case of vecdist this is a vector. `method` This is either "euclidean", "manhattan", "canberra1", "canberra2", "minimum", "maximum", "minkowski", "bhattacharyya", "hellinger", "kullback_leibler" or "jensen_shannon". The last two options are basically the same. `square` If you choose "euclidean" or "hellinger" as the method, then you can have the option to return the squared Euclidean distances by setting this argument to TRUE. `p` This is for the the Minkowski, the power of the metric. `vector` For return a vector instead a matrix.

## Details

The distance matrix is computer with an extra argument for the Euclidean distances. The "kullback_leibler" refers to the symmetric Kullback-Leibler divergence.

## Value

A square matrix with the pairwise distances.

## References

Mardia K. V., Kent J. T. and Bibby J. M. (1979). Multivariate Analysis. Academic Press.

```dista, colMedians ```

## Examples

 ```1 2 3 4 5``` ```x <- matrix(rnorm(50 * 10), ncol = 10) a1 <- Dist(x) a2 <- as.matrix( dist(x) ) x<-a1<-a2<-NULL ```

### Example output

```Loading required package: Rcpp