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CRAN: Package midr

midr: Learning from Black-Box Models by Maximum Interpretation Decomposition

The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. The package implements 'Maximum Interpretation Decomposition' (MID), a functional decomposition technique that finds an optimal additive approximation of the original model. This approximation is achieved by minimizing the squared error between the predictions of the black-box model and the surrogate model. The theoretical foundations of MID are described in Iwasawa & Matsumori (2025) [Forthcoming], and the package itself is detailed in Asashiba et al. (2025) <doi:10.48550/arXiv.2506.08338>.

Version: 0.5.0 Imports: graphics, grDevices, RcppEigen, rlang, stats, utils Suggests: datasets, ggplot2, khroma, knitr, RColorBrewer, rmarkdown, scales, shapviz, testthat, viridisLite Published: 2025-06-23 DOI: 10.32614/CRAN.package.midr Author: Ryoichi Asasihba [aut, cre], Hirokazu Iwasawa [aut], Reiji Kozuma [ctb] Maintainer: Ryoichi Asasihba <ryoichi.asashiba at gmail.com> BugReports: https://github.com/ryo-asashi/midr/issues License: MIT + file LICENSE URL: https://github.com/ryo-asashi/midr, https://ryo-asashi.github.io/midr/ NeedsCompilation: no Citation: midr citation info Materials: README NEWS CRAN checks: midr results Documentation: Downloads: Linking:

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