Package: e2tree
Title: Explainable Ensemble Trees
Version: 0.2.0
Authors@R: c(
    person(given = "Massimo",
           family = "Aria",
           role = c("aut", "cre", "cph"),
           email = "aria@unina.it",
           comment = c(ORCID = "0000-0002-8517-9411")),
           person(given = "Agostino",
           family = "Gnasso",
           role = "aut",
           email = "agostino.gnasso@unina.it",
           comment = c(ORCID = "0000-0002-8046-3923"))
           )
Description: The Explainable Ensemble Trees 'e2tree' approach has been proposed by Aria et al. (2024) <doi:10.1007/s00180-022-01312-6>. It aims to explain and interpret decision tree ensemble models using a single tree-like structure. 'e2tree' is a new way of explaining an ensemble tree trained through 'randomForest' or 'xgboost' packages.
License: MIT + file LICENSE
URL: https://github.com/massimoaria/e2tree
BugReports: https://github.com/massimoaria/e2tree/issues
Encoding: UTF-8
RoxygenNote: 7.3.2
Imports: dplyr, doParallel, parallel, foreach, future.apply, ggplot2,
        Matrix, partitions, purrr, tidyr, ranger, randomForest,
        rpart.plot, Rcpp, RSpectra, ape
LinkingTo: Rcpp
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2025-07-16 10:34:25 UTC; massimoaria
Author: Massimo Aria [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0002-8517-9411>),
  Agostino Gnasso [aut] (ORCID: <https://orcid.org/0000-0002-8046-3923>)
Maintainer: Massimo Aria <aria@unina.it>
Repository: CRAN
Date/Publication: 2025-07-16 12:00:02 UTC
Built: R 4.4.3; x86_64-w64-mingw32; 2025-12-19 04:45:05 UTC; windows
Archs: x64
