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README.Rmd
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README.Rmd
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---
output: github_document
---
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# EasyMMD
Gretton et al. (2007) introduced Maximum Mean Discrepancy (MMD). EasyMMD in an R package which provides a simple (hopefully!) and computationally efficient way to compute the MMD between two datasets.
## Installation
You can install EasyMMD from github with:
```{r gh-installation, eval = FALSE}
devtools::install_github("AnthonyEbert/EasyMMD")
```
## Example
This is a basic example which shows you how to solve a common problem:
```{r example}
library(EasyMMD)
n <- 1e3
x <- rnorm(n)
y <- rnorm(n, 5)
MMD(y,x)
# For faster computation, precompute part of the MMD for the observed data y
y_kmmd <- kmmd(y)
MMD(y,x, y_kmmd)
microbenchmark::microbenchmark(MMD(y,x), MMD(y,x, y_kmmd), times = 10)
```
## References
Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., & Smola, A. J. (2007). A kernel method for the two-sample-problem. In Advances in neural information processing systems (pp. 513-520).