Local Interpretable Model-Agnostic Explanations R package lime version 0.5.4 CRAN Task View: MachineLearning (https://CRAN.R-project.org/view=MachineLearning) When building complex models, it is often difficult to explain why the model should be trusted. While global measures such as accuracy are useful, they cannot be used for explaining why a model made a specific prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for explaining the outcome of black box models by fitting a local model around the point in question an perturbations of this point. The approach is described in more detail in the article by Ribeiro et al. (2016) <doi:10.48550/arXiv.1602.04938>. Software Depends: R (>= 4.1) Imports: assertthat, ggplot2, glmnet, glue, gower, grDevices, lifecycle, Matrix, methods, Rcpp, rlang, stats, stringi, tools LinkingTo: Rcpp, RcppEigen Suggests: covr, h2o, htmlwidgets, keras, knitr, magick, MASS, mlr, ranger, rmarkdown, sessioninfo, shiny, shinythemes, testthat (>= 3.0.0), text2vec, xgboost Emil Hvitfeldt <emil.hvitfeldt@posit.co> Comprehensive R Archive Network (CRAN) Emil Hvitfeldt [aut, cre] (ORCID: <https://orcid.org/0000-0002-0679-1945>), Thomas Lin Pedersen [aut] (ORCID: <https://orcid.org/0000-0002-5147-4711>), Michaël Benesty [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=lime/LICENSE) 2025-12-11 application/tgz https://CRAN.R-project.org/package=lime doi:10.32614/CRAN.package.lime