Description Usage Arguments Value References See Also Examples
Approximation of the squared wasserstein distance. Calculation based on the mean squared difference between the equidistant empirical quantiles of the two input vectors a and b. As an approximation of the quantile function, 1000 quantiles are computed for each vector.
Vector representing an empirical distribution under condition A
Vector representing an empirical distribution under condition B
The approximated squared wasserstein distance between x and y
Schefzik and Goncalves 2019
[wasserstein_metric()], [squared_wass_decomp()] for different implementations of the wasserstein distance
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# input: one dimensional data in two conditions x <- rnorm(100, 42, 2) y <- c(rnorm(61, 20, 1), rnorm(41, 40,2)) # output: The squared Wasserstein distance approximated as described in # Schefzik and Goncalves 2019 d.wass.approx <- squared_wass_approx(x,y)
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