e2m(X, sm.method = c("svd", "eigen"))

# test data
## Not run: 
X <- matrix(rnorm(500), ncol = 5)
# Normal way to compute the Mahalanobis distance
md1 <- sqrt(mahalanobis(X, center = colMeans(X), cov = cov(X)))
# Projection approach for computing the Mahalanobis distance
# 1. Projecting from the Euclidean to the Mahalanobis space
Xm <- e2m(X, sm.method = "svd")
# 2. Use the normal Euclidean distance on the Mahalanobis space
md2 <- sqrt(rowSums((sweep(Xm, 2, colMeans(Xm), "-"))^2))
# Plot the results of both methods
plot(md1, md2)
# Test on a real dataset
# Mahalanobis in the spectral space
data(NIRsoil)
X <- NIRsoil$spc
Xm <- e2m(X, sm.method = "svd")
md2 <- sqrt(rowSums((sweep(Xm, 2, colMeans(Xm), "-"))^2))

md1 <- sqrt(mahalanobis(X, center = colMeans(X), cov = cov(X))) # does not work
# Mahalanobis in the PC space
pc <- 20
pca <- prcomp(X, center = TRUE, scale = TRUE)
X <- pca$x[, 1:pc]
X2 <- sweep(pca$x[, 1:pc, drop = FALSE], 2, pca$sdev[1:pc], "/")
md4 <- sqrt(rowSums((sweep(Xm, 2, colMeans(Xm), "-"))^2))
md5 <- sqrt(rowSums((sweep(X2, 2, colMeans(X2), "-"))^2))
md3 <- sqrt(mahalanobis(X, center = colMeans(X), cov = cov(X))) # does work

## End(Not run)