Package: fmeffects Title: Model-Agnostic Interpretations with Forward Marginal Effects Version: 0.1.4 Authors@R: c( person("Holger", "Löwe", , "hbj.loewe@gmail.com", role = c("cre", "aut")), person("Christian", "Scholbeck", , "christian.scholbeck@stat.uni-muenchen.de", role = "aut"), person("Christian", "Heumann", , "christian.heumann@stat.uni-muenchen.de", role = "rev"), person("Bernd", "Bischl", , "bernd.bischl@stat.uni-muenchen.de", role = "rev"), person("Giuseppe", "Casalicchio", , "giuseppe.casalicchio@stat.uni-muenchen.de", role = "rev") ) Description: Create local, regional, and global explanations for any machine learning model with forward marginal effects. You provide a model and data, and 'fmeffects' computes feature effects. The package is based on the theory in: C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2022) . License: LGPL-3 Encoding: UTF-8 Roxygen: list(markdown = TRUE) RoxygenNote: 7.2.3 Suggests: caret, furrr, future, hexbin, knitr, mlr3verse, parallelly, ranger, rmarkdown, rpart, tidymodels Imports: checkmate, cli, data.table, partykit, ggparty, ggplot2, cowplot, R6, testthat Collate: 'ExtrapolationDetector.R' 'FME.R' 'FMEPlot.R' 'NonLinearityMeasure.R' 'Partitioning.R' 'PartitioningCtree.R' 'PartitioningPlot.R' 'PartitioningRpart.R' 'Predictor.R' 'PredictorCaret.R' 'PredictorLM.R' 'PredictorMLR3.R' 'PredictorParsnip.R' 'Pruner.R' 'S3.R' 'ame.R' 'bikes.R' 'misc.R' 'zzz.R' URL: https://holgstr.github.io/fmeffects/, https://github.com/holgstr/fmeffects BugReports: https://github.com/holgstr/fmeffects/issues VignetteBuilder: knitr Config/pak/sysreqs: cmake make libuv1-dev Repository: https://holgstr.r-universe.dev Date/Publication: 2024-11-05 16:36:51 UTC RemoteUrl: https://github.com/holgstr/fmeffects RemoteRef: HEAD RemoteSha: 12331c401b819f374316f3ee354fa2b1c8e2675c NeedsCompilation: no Packaged: 2026-06-20 07:53:05 UTC; root Author: Holger Löwe [cre, aut], Christian Scholbeck [aut], Christian Heumann [rev], Bernd Bischl [rev], Giuseppe Casalicchio [rev] Maintainer: Holger Löwe Depends: R (>= 3.5.0)