DESCRIPTION

Package: triplot
Title: Explaining Correlated Features in Machine Learning Models 
Version: 1.3.1
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