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Interaction forests\n(IFs) are ensembles of decision trees that model quantitative\nand qualitative interaction effects using bivariable splitting.\nIFs come with the Effect Importance Measure (EIM), which can be\nused to identify variable pairs that feature quantitative and\nqualitative interaction effects with high predictive relevance.\nIFs and EIM focus on well interpretable forms of interactions.\nThe package also offers plot functions for visualising the\nestimated forms of interaction effects. Categorical, metric,\nand survival outcomes are supported. This is a fork of the R\npackage 'ranger' (main author: Marvin N. Wright) that\nimplements random forests using an efficient C++\nimplementation. References: [1] Hornung, R. & Boulesteix, A.-L.\n(2022) Interaction Forests: Identifying and exploiting\ninterpretable quantitative and qualitative interaction effects.\nComputational Statistics & Data Analysis 171:107460,\n. [2] Hornung, R. 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