Package: iml
Type: Package
Title: Interpretable Machine Learning
Version: 0.2.1
Date: 2018-03-04
Authors@R: c(person(given = "Christoph", family = "Molnar", 
                    role = c("aut", "cre"), email = "christoph.molnar@gmail.com"))
Maintainer: Christoph Molnar <christoph.molnar@gmail.com>
Description: Interpretability methods to analyze the behavior and predictions of
 any machine learning model.
 Implemented methods are:
 Feature importance described by Fisher et al. (2018) <arXiv:1801.01489>,
 partial dependence plots described by Friedman (2001) <http://www.jstor.org/stable/2699986>,
 individual conditional expectation ('ice') plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>,
 local models (variant of 'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>,
 the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x> and
 tree surrogate models.
Imports: R6, checkmate, dplyr, tidyr, ggplot2, partykit, glmnet,
        Metrics, data.table
Suggests: randomForest, gower, testthat, rpart, MASS, caret, e1071,
        lime, mlr
License: MIT + file LICENSE
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-03-07 13:47:11 UTC; chris
Author: Christoph Molnar [aut, cre]
Repository: CRAN
Date/Publication: 2018-03-13 17:14:38 UTC
