xspliner: Assisted Model Building, using Surrogate Black-Box Models to Train Interpretable Spline Based Additive Models

Builds generalized linear model with automatic data transformation. The 'xspliner' helps to build simple, interpretable models that inherits informations provided by more complicated ones. The resulting model may be treated as explanation of provided black box, that was supplied prior to the algorithm.

Version: 0.0.4
Depends: R (≥ 3.0)
Imports: stats, pdp, dplyr, ggplot2, mgcv, magrittr, purrr, tidyr, pROC (≥ 1.15.3)
Suggests: ALEPlot, factorMerger, testthat, knitr, rmarkdown, ResourceSelection, randomForest, e1071, caret, breakDown, DALEX, xgboost, gridExtra, grid, ISLR
Published: 2019-09-25
Author: Krystian Igras [aut, cre], Przemyslaw Biecek [aut, ths]
Maintainer: Krystian Igras <krystian8207 at gmail.com>
BugReports: https://github.com/ModelOriented/xspliner/issues
License: GPL-2 | GPL-3 [expanded from: GPL]
URL: https://ModelOriented.github.io/xspliner/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: xspliner results


Reference manual: xspliner.pdf
Vignettes: Automate your work
Use cases
Classification and discrete predictors
Extra information about the package
Methods and xspliner environment
Basic theory and usage


Package source: xspliner_0.0.4.tar.gz
Windows binaries: r-devel: xspliner_0.0.4.zip, r-release: xspliner_0.0.4.zip, r-oldrel: xspliner_0.0.4.zip
macOS binaries: r-release (arm64): xspliner_0.0.4.tgz, r-release (x86_64): xspliner_0.0.4.tgz, r-oldrel: xspliner_0.0.4.tgz
Old sources: xspliner archive


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