analyze_dataset: Analyze the given dataset.
In bwrc/astrid-r: Automatic STructure IDentification

Find the attribute interactions in the given dataset.

analyze_dataset(dataset, classname = "class", seed_traintest = 42,
  seed = 42, classifier = NULL, alpha = 0.05, R = (1 +
  ceiling(1/alpha)), Rmin = 250, Rmax = 500, z = 2.57,
  prune_singletons = TRUE, parallel = TRUE, early_stopping = FALSE)

`dataset`	The dataset to analyze
`classname`	The name of the class attribute in the dataset. Default is `class`.
`seed_traintest`	Random seed used for splitting the data into training and testing sets, default is 42.
`seed`	Random seed, default is 42.
`classifier`	The classifier to be used, as a string. Default is `NULL`.
`alpha`	Significance level (default is 0.05).
`R`	Number of samples to use for calculating the accuracy. Default is R = 1 + (1 / alpha).
`Rmin`	Ninimum number of replications to use for calculating p-values. Default is 250.
`Rmax`	Maximum number of replications to use for calculating p-values. Default is 500.
`z`	Parameter for confidence band for p-values. Use 1.96 for 95 percent, 2.25 for 97.5 percent and 2.57 for 99 percent. Default is 2.57.
`prune_singletons`	Should singletons be pruned. Default is `TRUE`.
`parallel`	Calculate p-values in parallel (Boolean, default is `TRUE`). If parallel is used the results are not deterministic using the same random seed.
`early_stopping`	Use early stopping. Default is `FALSE` in which case Rmin samples are used to calculate p-values. If `TRUE` at least Rmin and at most Rmax values are used.