Package: triplot
Title: Explaining Correlated Features in Machine Learning Models
Version: 1.3.0
Authors@R: 
    c(person("Katarzyna", "Pekala", email = "katarzyna.pekala@gmail.com", 
    role = c("aut", "cre")), 
    person("Przemyslaw", "Biecek", role = c("aut"), 
    comment = c(ORCID = "0000-0001-8423-1823")))
Description: Tools for exploring effects of correlated features in predictive 
    models. The predict_triplot() function delivers instance-level explanations 
    that calculate the importance of the groups of explanatory variables. The 
    model_triplot() function delivers data-level explanations. The generic plot 
    function visualises in a concise way importance of hierarchical groups of 
    predictors. All of the the tools are model agnostic, therefore works for any
    predictive machine learning models. Find more details in Biecek (2018) 
    <arXiv:1806.08915>.
Depends: R (>= 3.6)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: ggplot2, DALEX (>= 1.3), glmnet, ggdendro, patchwork
Suggests: testthat, knitr, randomForest, mlbench, ranger, gbm, covr
URL: https://github.com/ModelOriented/triplot
BugReports: https://github.com/ModelOriented/triplot/issues
Language: en-US
NeedsCompilation: no
Packaged: 2020-07-13 15:00:44 UTC; KatarzynaPekala
Author: Katarzyna Pekala [aut, cre],
  Przemyslaw Biecek [aut] (<https://orcid.org/0000-0001-8423-1823>)
Maintainer: Katarzyna Pekala <katarzyna.pekala@gmail.com>
Repository: CRAN
Date/Publication: 2020-07-13 17:00:03 UTC
Built: R 4.2.0; ; 2022-04-13 12:42:19 UTC; unix
