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Issue #38 (#39)
* plot scale fix and regression * description and fixes * test and fixes * update tests * Bump version and update NEWS * fix plot density * Update links and descriptions * add NEWS entry * update README * add seed
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‎DESCRIPTION

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Package: fairmodels
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Type: Package
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Title: Flexible Tool for Bias Detection, Visualization, and Mitigation
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Version: 1.0.1
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Authors@R:
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c(person("Jakub", "Wiśniewski", role = c("aut", "cre"),
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email = "[email protected]"),
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person("Przemysław", "Biecek", role = c("aut"),
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comment = c(ORCID = "0000-0001-8423-1823")))
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Description: Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Find more details in (Wiśniewski, Biecek (2021)) <arXiv:2104.00507>.
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License: GPL-3
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Encoding: UTF-8
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LazyData: true
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Depends: R (>= 3.5)
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Imports:
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DALEX,
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ggplot2,
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patchwork,
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ggdendro,
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ggrepel,
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scales
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Suggests:
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ranger,
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gbm,
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knitr,
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rmarkdown,
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covr,
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testthat,
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spelling
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RoxygenNote: 7.1.1.9000
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VignetteBuilder: knitr
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URL: http://fairmodels.drwhy.ai/
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BugReports: https://github.com/ModelOriented/fairmodels/issues
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Language: en-US
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Package: fairmodels
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Type: Package
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Title: Flexible Tool for Bias Detection, Visualization, and Mitigation
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Version: 1.1.0
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Authors@R:
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c(person("Jakub", "Wiśniewski", role = c("aut", "cre"),
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email = "[email protected]"),
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person("Przemysław", "Biecek", role = c("aut"),
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comment = c(ORCID = "0000-0001-8423-1823")))
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Description: Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021)) <arXiv:2104.00507>.
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License: GPL-3
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Encoding: UTF-8
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LazyData: true
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Depends: R (>= 3.5)
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Imports:
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DALEX,
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ggplot2,
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patchwork,
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ggdendro,
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ggrepel,
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scales
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Suggests:
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ranger,
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gbm,
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knitr,
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rmarkdown,
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covr,
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testthat,
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spelling
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RoxygenNote: 7.1.1.9001
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VignetteBuilder: knitr
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URL: https://fairmodels.drwhy.ai/
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BugReports: https://github.com/ModelOriented/fairmodels/issues
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Language: en-US

‎NAMESPACE

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# Generated by roxygen2: do not edit by hand
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S3method(plot,all_cutoffs)
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S3method(plot,ceteris_paribus_cutoff)
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S3method(plot,chosen_metric)
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S3method(plot,fairness_heatmap)
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S3method(plot,fairness_object)
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S3method(plot,fairness_pca)
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S3method(plot,fairness_radar)
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S3method(plot,group_metric)
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S3method(plot,metric_scores)
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S3method(plot,performance_and_fairness)
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S3method(plot,stacked_metrics)
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S3method(plot_fairmodels,default)
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S3method(plot_fairmodels,explainer)
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S3method(plot_fairmodels,fairness_object)
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S3method(print,all_cutoffs)
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S3method(print,ceteris_paribus_cutoff)
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S3method(print,chosen_metric)
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S3method(print,fairness_heatmap)
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S3method(print,fairness_object)
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S3method(print,fairness_pca)
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S3method(print,fairness_radar)
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S3method(print,group_metric)
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S3method(print,metric_scores)
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S3method(print,performance_and_fairness)
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S3method(print,stacked_metrics)
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export(all_cutoffs)
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export(calculate_group_fairness_metrics)
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export(ceteris_paribus_cutoff)
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export(choose_metric)
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export(confusion_matrix)
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export(disparate_impact_remover)
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export(expand_fairness_object)
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export(fairness_check)
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export(fairness_heatmap)
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export(fairness_pca)
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export(fairness_radar)
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export(group_matrices)
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export(group_metric)
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export(group_model_performance)
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export(metric_scores)
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export(performance_and_fairness)
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export(plot_density)
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export(plot_fairmodels)
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export(pre_process_data)
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export(resample)
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export(reweight)
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export(roc_pivot)
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export(stack_metrics)
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import(ggplot2)
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import(patchwork)
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importFrom(DALEX,model_performance)
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importFrom(DALEX,theme_drwhy)
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importFrom(DALEX,theme_drwhy_vertical)
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importFrom(ggdendro,dendro_data)
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importFrom(ggdendro,segment)
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importFrom(ggrepel,geom_text_repel)
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importFrom(stats,dist)
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importFrom(stats,ecdf)
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importFrom(stats,hclust)
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importFrom(stats,median)
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importFrom(stats,na.omit)
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importFrom(stats,quantile)
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importFrom(utils,head)
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# Generated by roxygen2: do not edit by hand
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S3method(plot,all_cutoffs)
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S3method(plot,ceteris_paribus_cutoff)
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S3method(plot,chosen_metric)
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S3method(plot,fairness_heatmap)
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S3method(plot,fairness_object)
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S3method(plot,fairness_pca)
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S3method(plot,fairness_radar)
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S3method(plot,fairness_regression_object)
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S3method(plot,group_metric)
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S3method(plot,metric_scores)
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S3method(plot,performance_and_fairness)
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S3method(plot,stacked_metrics)
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S3method(plot_fairmodels,default)
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S3method(plot_fairmodels,explainer)
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S3method(plot_fairmodels,fairness_object)
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S3method(print,all_cutoffs)
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S3method(print,ceteris_paribus_cutoff)
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S3method(print,chosen_metric)
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S3method(print,fairness_heatmap)
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S3method(print,fairness_object)
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S3method(print,fairness_pca)
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S3method(print,fairness_radar)
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S3method(print,fairness_regression_object)
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S3method(print,group_metric)
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S3method(print,metric_scores)
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S3method(print,performance_and_fairness)
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S3method(print,stacked_metrics)
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export(all_cutoffs)
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export(calculate_group_fairness_metrics)
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export(ceteris_paribus_cutoff)
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export(choose_metric)
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export(confusion_matrix)
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export(disparate_impact_remover)
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export(expand_fairness_object)
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export(fairness_check)
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export(fairness_check_regression)
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export(fairness_heatmap)
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export(fairness_pca)
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export(fairness_radar)
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export(group_matrices)
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export(group_metric)
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export(group_model_performance)
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export(metric_scores)
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export(performance_and_fairness)
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export(plot_density)
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export(plot_fairmodels)
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export(pre_process_data)
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export(regression_metrics)
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export(resample)
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export(reweight)
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export(roc_pivot)
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export(stack_metrics)
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import(ggplot2)
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import(patchwork)
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importFrom(DALEX,model_performance)
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importFrom(DALEX,theme_drwhy)
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importFrom(DALEX,theme_drwhy_vertical)
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importFrom(ggdendro,dendro_data)
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importFrom(ggdendro,segment)
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importFrom(ggrepel,geom_text_repel)
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importFrom(stats,binomial)
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importFrom(stats,dist)
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importFrom(stats,ecdf)
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importFrom(stats,glm)
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importFrom(stats,hclust)
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importFrom(stats,median)
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importFrom(stats,na.omit)
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importFrom(stats,quantile)
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importFrom(utils,head)

‎NEWS.md

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# fairmodels 1.1.0
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* Added function `fairness_check_regression()` that supports regression models along with 2 plot types [(#38)](https://github.com/ModelOriented/fairmodels/issues/38).
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* Added additional tests.
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* Modularized `fairness_check()` code.
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* Changed x-axis ticks generation in `fairness_check()`.
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* Fixed issues with `plot_density
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* Updated links in README and DESCRIPTION.
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# fairmodels 1.0.1
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* Changed examples - added parameter `num.threads = 1` to `ranger` and added *donttest{}* to examples with long computation time.
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* Fixed links in DESCRIPTION and README.
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# fairmodels 0.2.6
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* Fixed bug which appeared when two fairness objects were passed to `fairness_check` without an explainer. (#36)
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* Fixed bug which appeared when two fairness objects were passed to `fairness_check` without an explainer. [(#36)](https://github.com/ModelOriented/fairmodels/issues/36)
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# fairmodels 0.2.5
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* Extended documentation for `epsilon` parameter in `fairness_check()` function.
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# fairmodels 0.2.4
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* Deleted on-load information message about four-fifths rule.
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* Fixed bug with `NA` warning in metrics that are not chosen. (#32)
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* Fixed bug with `NA` warning in metrics that are not chosen. [(#32)](https://github.com/ModelOriented/fairmodels/issues/32)
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# fairmodels 0.2.3
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* Fixed the way the `parity_loss` is calculated in `all_cutoffs` and `ceteris_paribus_cutoff`. (#24)
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* Fixed the way the `parity_loss` is calculated in `all_cutoffs` and `ceteris_paribus_cutoff`. [(#24)](https://github.com/ModelOriented/fairmodels/issues/24)
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* Updated vignettes
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* changed documentation of functions to explicitly state metrics instead of `fairness_check_metrics()`. (#29)
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* Fixed typos (#27 and #28)
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* changed documentation of functions to explicitly state metrics instead of `fairness_check_metrics()`. [(#29)](https://github.com/ModelOriented/fairmodels/issues/29)
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* Fixed typos ([#27](https://github.com/ModelOriented/fairmodels/issues/27) and [#28](https://github.com/ModelOriented/fairmodels/issues/28))
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* Changed conclusion drawn from density plot in `Basic Tutorial` (#26)
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# fairmodels 0.2.2

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