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  "Title": "Multivariate Outlier Detection and Replacement",
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  "Description": "Provides a random forest based implementation of the\nmethod described in Chapter 7.1.2 (Regression model based\nanomaly detection) of Chandola et al. (2009)\n<doi:10.1145/1541880.1541882>. It works as follows: Each\nnumeric variable is regressed onto all other variables by a\nrandom forest. If the scaled absolute difference between\nobserved value and out-of-bag prediction of the corresponding\nrandom forest is suspiciously large, then a value is considered\nan outlier. The package offers different options to replace\nsuch outliers, e.g. by realistic values found via predictive\nmean matching. Once the method is trained on a reference data,\nit can be applied to new data.",
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  "Encoding": "UTF-8",
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  "Date/Publication": "2025-04-06 09:47:38 UTC",
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    "message": "Merge pull request #16 from mayer79/update-code-cov-version\n\nUpdate code coverage version",
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      "title": "Multivariate Outlier Detection and Replacement",
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