# Goodness of fit tests Anderson-Darling goodness of fit tests for Regional Frequency Analysis: Monte-Carlo method. ```r gofNORMtest (x) gofEXPtest (x, Nsim=1000) gofGUMBELtest (x, Nsim=1000) gofGENLOGIStest (x, Nsim=1000) gofGENPARtest (x, Nsim=1000) gofGEVtest (x, Nsim=1000) gofLOGNORMtest (x, Nsim=1000) gofP3test (x, Nsim=1000) ``` ## Arguments - `x`: data sample - `Nsim`: number of simulated samples from the hypothetical parent distribution ## Details An introduction, analogous to the following one, on the Anderson-Darling test is available on [https://en.wikipedia.org/wiki/Anderson-Darling_test](https://en.wikipedia.org/wiki/Anderson-Darling_test). Given a sample $xi (i=1,...,m)$ of data extracted from a distribution $FR(x)$, the test is used to check the null hypothesis $H0 : FR(x) = F(x,\theta)$, where $F(x,\theta)$ is the hypothetical distribution and $\theta$ is an array of parameters estimated from the sample $xi$. The Anderson-Darling goodness of fit test measures the departure between the hypothetical distribution $F(x,\theta)$ and the cumulative frequency function $Fm(x)$ defined as: $$ F_m(x) = 0 \ , \ x < x_{(1)}Fm(x)=0, x

GOFmontecarlo function