```
## Janitza's p-values with corrected Gini importance
n <- 50
p <- 400
dat <- data.frame(y = factor(rbinom(n, 1, .5)), replicate(p, runif(n)))
rf.sim <- ranger(y ~ ., dat, importance = "impurity_corrected")
importance_pvalues(rf.sim, method = "janitza")
## Permutation p-values
if (FALSE) {
rf.iris <- ranger(Species ~ ., data = iris, importance = 'permutation')
importance_pvalues(rf.iris, method = "altmann", formula = Species ~ ., data = iris)
}
```

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