Random Forest Prediction Decomposition and Feature Importance Measure R package tree.interpreter version 0.1.3 An R re-implementation of the 'treeinterpreter' package on PyPI <https://pypi.org/project/treeinterpreter/>. Each prediction can be decomposed as 'prediction = bias + feature_1_contribution + ... + feature_n_contribution'. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) <doi:10.48550/arXiv.1906.10845>. Software Imports: Rcpp (>= 1.0.2) LinkingTo: Rcpp, RcppArmadillo Suggests: MASS, randomForest, ranger, testthat (>= 2.1.0), knitr, rmarkdown, covr Qingyao Sun <sunqingyao19970825@gmail.com> Comprehensive R Archive Network (CRAN) Qingyao Sun [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=tree.interpreter/LICENSE) 2025-09-18 application/tgz https://CRAN.R-project.org/package=tree.interpreter doi:10.32614/CRAN.package.tree.interpreter