Compute the Mahalanobis distance of all pairwise rows in `.means`

. The
result is a symmetric matrix containing the distances that may be used for
hierarchical clustering.

## Arguments

- .means
A matrix of data with, say, p columns.

- covar
The covariance matrix.

- inverted
Logical argument. If

`TRUE`

,`covar`

is supposed to contain the inverse of the covariance matrix.

## Author

Tiago Olivoto tiagoolivoto@gmail.com

## Examples

```
# \donttest{
library(metan)
library(dplyr)
# Compute the mean for genotypes
means <- means_by(data_ge, GEN) %>%
column_to_rownames("GEN")
# Compute the covariance matrix
covmat <- cov(means)
# Compute the distance
dist <- mahala(means, covmat)
# Dendrogram
dend <- dist %>%
as.dist() %>%
hclust() %>%
as.dendrogram()
plot(dend)
# }
```