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DESCRIPTION
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DESCRIPTION
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Package: fmeffects
Title: Model-Agnostic Interpretations with Forward Marginal Effects
Version: 0.1.3
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) <doi:10.48550/arXiv.2201.08837>.
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