• using R Under development (unstable) (2025-09-11 r88810)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc (GCC) 15.1.1 20250521 (Red Hat 15.1.1-2)     GNU Fortran (GCC) 15.1.1 20250521 (Red Hat 15.1.1-2)
• running under: Fedora Linux 42 (Workstation Edition)
• using session charset: UTF-8
• using option ‘--no-stop-on-test-error’
• checking for file ‘fairmodels/DESCRIPTION’ ... OK
• checking extension type ... Package
• this is package ‘fairmodels’ version ‘1.2.1’
• package encoding: UTF-8
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• checking whether package ‘fairmodels’ can be installed ... [13s/18s] OK See the install log for details.
• checking package directory ... OK
• checking ‘build’ directory ... OK
• checking DESCRIPTION meta-information ... OK
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• checking code files for non-ASCII characters ... OK
• checking R files for syntax errors ... OK
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• checking whether the package can be unloaded cleanly ... OK
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• checking whether the namespace can be unloaded cleanly ... OK
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• checking dependencies in R code ... OK
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• checking R code for possible problems ... [23s/30s] OK
• checking Rd files ... NOTE checkRd: (-1) choose_metric.Rd:35: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) choose_metric.Rd:36: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) choose_metric.Rd:37: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) confusion_matrx.Rd:20: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) confusion_matrx.Rd:21: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) confusion_matrx.Rd:22: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) confusion_matrx.Rd:23: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) disparate_impact_remover.Rd:28: Lost braces     28 | pigeonholing. The number of pigeonholes is fixed and equal to min{101, unique(a)}, where a is vector with values for subgroup. So if some subgroup is not numerous and        | ^ checkRd: (-1) fairness_check.Rd:47: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:48: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:49: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:50: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:51: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:52: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:53: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:54: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:55: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:56: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:57: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:58: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:61: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:62: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:63: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:64: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:65: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:66: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_heatmap.Rd:12: Lost braces     12 | \item{scale}{logical, if code{TRUE} metrics will be scaled to mean 0 and sd 1. Default \code{FALSE}}        | ^ checkRd: (-1) fairness_heatmap.Rd:19: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_heatmap.Rd:20: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_heatmap.Rd:21: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_heatmap.Rd:22: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_pca.Rd:18: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_pca.Rd:19: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_pca.Rd:20: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_pca.Rd:21: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_pca.Rd:22: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_radar.Rd:18: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_radar.Rd:19: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_matrices.Rd:25: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_matrices.Rd:26: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_matrices.Rd:27: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_matrices.Rd:28: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_metric.Rd:30: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_metric.Rd:31: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_metric.Rd:32: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_metric.Rd:33: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) metric_scores.Rd:18: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) metric_scores.Rd:19: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) performance_and_fairness.Rd:20: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) performance_and_fairness.Rd:21: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) performance_and_fairness.Rd:22: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) performance_and_fairness.Rd:23: Lost braces in \itemize; \value handles \item{}{} directly
• checking Rd metadata ... OK
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• checking for unstated dependencies in examples ... OK
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• checking installed files from ‘inst/doc’ ... OK
• checking files in ‘vignettes’ ... OK
• checking examples ... [19s/24s] ERROR Running examples in ‘fairmodels-Ex.R’ failed The error most likely occurred in: > ### Name: fairness_heatmap > ### Title: Fairness heatmap > ### Aliases: fairness_heatmap > > ### ** Examples > > > data("german") > > y_numeric <- as.numeric(german$Risk) - 1 > > lm_model <- glm(Risk ~ ., + data = german, + family = binomial(link = "logit") + ) > > rf_model <- ranger::ranger(Risk ~ ., + data = german, + probability = TRUE, + num.trees = 200, + num.threads = 1 + ) > > explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated   -> model label : lm ( [33m default [39m )   -> data : 1000 rows 9 cols   -> target variable : 1000 values   -> predict function : yhat.glm will be used ( [33m default [39m )   -> predicted values : No value for predict function target column. ( [33m default [39m )   -> model_info : package stats , ver. 4.6.0 , task classification ( [33m default [39m )   -> predicted values : numerical, min = 0.1369187 , mean = 0.7 , max = 0.9832426   -> residual function : difference between y and yhat ( [33m default [39m )   -> residuals : numerical, min = -0.9572803 , mean = 1.280546e-17 , max = 0.8283475   [32m A new explainer has been created! [39m > explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated   -> model label : ranger ( [33m default [39m )   -> data : 1000 rows 9 cols   -> target variable : 1000 values   -> predict function : yhat.ranger will be used ( [33m default [39m )   -> predicted values : No value for predict function target column. ( [33m default [39m )   -> model_info : package ranger , ver. 0.17.0 , task classification ( [33m default [39m )   -> predicted values : numerical, min = 0.07287302 , mean = 0.6989152 , max = 0.9974848   -> residual function : difference between y and yhat ( [33m default [39m )   -> residuals : numerical, min = -0.7219256 , mean = 0.001084826 , max = 0.6142332   [32m A new explainer has been created! [39m > > fobject <- fairness_check(explainer_lm, explainer_rf, + protected = german$Sex, + privileged = "male" + ) Creating fairness classification object -> Privileged subgroup : character ( [32m Ok [39m ) -> Protected variable : factor ( [32m Ok [39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( [32m compatible [39m ) -> Metric calculation : 10/13 metrics calculated for all models ( [33m3 NA created [39m )  [32m Fairness object created succesfully [39m > > # same explainers with different cutoffs for female > fobject <- fairness_check(explainer_lm, explainer_rf, fobject, + protected = german$Sex, + privileged = "male", + cutoff = list(female = 0.4), + label = c("lm_2", "rf_2") + ) Creating fairness classification object -> Privileged subgroup : character ( [32m Ok [39m ) -> Protected variable : factor ( [32m Ok [39m ) -> Cutoff values for explainers : female: 0.4, male: 0.5 -> Fairness objects : 1 object ( [32m compatible [39m ) -> Checking explainers : 4 in total ( [32m compatible [39m ) -> Metric calculation : 10/13 metrics calculated for all models ( [33m3 NA created [39m )  [32m Fairness object created succesfully [39m > > > fh <- fairness_heatmap(fobject) > > plot(fh) Error in rep(yes, length.out = len) :   attempt to replicate an object of type 'object' Calls: plot -> plot.fairness_heatmap -> ifelse Execution halted
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... [57s/72s] ERROR   Running ‘testthat.R’ [56s/71s] Running the tests in ‘tests/testthat.R’ failed. Complete output:   > library(testthat)   > library(fairmodels)   >   >   > test_check("fairmodels")   Welcome to DALEX (version: 2.5.2).   Find examples and detailed introduction at: http://ema.drwhy.ai/      Loaded gbm 2.2.2   This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3   Preparation of a new explainer is initiated     -> model label : ranger ( [33m default [39m )     -> data : 6172 rows 7 cols     -> target variable : 6172 values     -> predict function : yhat.ranger will be used ( [33m default [39m )     -> predicted values : No value for predict function target column. ( [33m default [39m )     -> model_info : package ranger , ver. 0.17.0 , task classification ( [33m default [39m )     -> predicted values : numerical, min = 0.1611047 , mean = 0.5454043 , max = 0.8667267     -> residual function : difference between y and yhat ( [33m default [39m )     -> residuals : numerical, min = -0.849369 , mean = -0.0005242329 , max = 0.7752409     [32m A new explainer has been created! [39m   Preparation of a new explainer is initiated     -> model label : lm ( [33m default [39m )     -> data : 6172 rows 7 cols     -> target variable : 6172 values     -> predict function : yhat.glm will be used ( [33m default [39m )     -> predicted values : No value for predict function target column. ( [33m default [39m )     -> model_info : package stats , ver. 4.6.0 , task classification ( [33m default [39m )     -> predicted values : numerical, min = 0.004522979 , mean = 0.5448801 , max = 0.8855426     -> residual function : difference between y and yhat ( [33m default [39m )     -> residuals : numerical, min = -0.8822826 , mean = -5.053611e-13 , max = 0.9767658     [32m A new explainer has been created! [39m   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 2 in total ( [32m compatible [39m )   -> Metric calculation : 13/13 metrics calculated for all models    [32m Fairness object created succesfully [39m   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 2 in total ( [32m compatible [39m )   -> Metric calculation : 13/13 metrics calculated for all models    Fairness object created succesfully   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [33m changed from numeric [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 2 in total ( [32m compatible [39m )   -> Metric calculation : 13/13 metrics calculated for all models    Fairness object created succesfully   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : Creating fairness classification object   -> Privileged subgroup : character ( [33m from first fairness object [39m )   -> Protected variable : factor ( [33m from first fairness object [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 2 objects ( [32m compatible [39m )   -> Checking explainers : 4 in total ( [32m compatible [39m )   Creating fairness classification object   -> Privileged subgroup : character ( [33m from first fairness object [39m )   -> Protected variable : factor ( [33m from first fairness object [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 1 object ( [32m compatible [39m )   -> Checking explainers : 3 in total ( [31m model type not supported [39m )   Creating fairness classification object   -> Privileged subgroup : character ( [33m from first fairness object [39m )   -> Protected variable : factor ( [33m from first fairness object [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 2 objects ( [31m not compatible [39m )   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [33m from first fairness object [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 2 objects ( [31m not compatible [39m )   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 2 objects ( [31m not compatible [39m )   Preparation of a new explainer is initiated     -> model label : lm ( [33m default [39m )     -> data : 1000 rows 3 cols     -> target variable : 1000 values     -> predict function : yhat.lm will be used ( [33m default [39m )     -> predicted values : No value for predict function target column. ( [33m default [39m )     -> model_info : package stats , ver. 4.6.0 , task regression ( [33m default [39m )     -> predicted values : numerical, min = -164.9767 , mean = 749.8455 , max = 1520.65     -> residual function : difference between y and yhat ( [33m default [39m )     -> residuals : numerical, min = -344.4796 , mean = 1.359789e-12 , max = 291.3585     [32m A new explainer has been created! [39m   Creating fairness regression object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [33m changed from character [39m )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [32m compatible [39m )   -> Metric calculation : 3/3 metrics calculated for all models    [32m Fairness regression object created succesfully [39m      Creating fairness regression object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [33m changed from character [39m )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [32m compatible [39m )   -> Metric calculation : 3/3 metrics calculated for all models    Fairness regression object created succesfully      Creating fairness regression object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [33m changed from character [39m )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [32m compatible [39m )   -> Metric calculation : 3/3 metrics calculated for all models    [32m Fairness regression object created succesfully [39m      Creating fairness regression object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [33m changed from character [39m )   -> Fairness objects : 1 object ( [32m compatible [39m )   -> Checking explainers : 2 in total ( [32m compatible [39m )   Creating fairness regression object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [33m changed from numeric [39m )   Creating fairness regression object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [33m changed from character [39m )   Creating fairness regression object   -> Privileged subgroup : character ( [33m from first fairness object [39m )   -> Protected variable : factor ( [33m from first fairness object [39m )   Creating fairness regression object   -> Privileged subgroup : character ( [33m from first fairness object [39m )   -> Protected variable : factor ( [33m from first fairness object [39m )   Creating fairness regression object   -> Privileged subgroup : character ( [33m from first fairness object [39m )   -> Protected variable : factor ( [33m changed from character [39m )   -> Fairness objects : 1 object ( [32m compatible [39m )   -> Checking explainers : 2 in total ( [31m model type not supported [39m )   Preparation of a new explainer is initiated     -> model label : lm ( [33m default [39m )     -> data : 1000 rows 3 cols     -> target variable : 1000 values     -> predict function : yhat.lm will be used ( [33m default [39m )     -> predicted values : No value for predict function target column. ( [33m default [39m )     -> model_info : package stats , ver. 4.6.0 , task regression ( [33m default [39m )     -> predicted values : numerical, min = -164.9767 , mean = 749.8455 , max = 1520.65     -> residual function : difference between y and yhat ( [33m default [39m )     -> residuals : numerical, min = -344.4796 , mean = 1.359789e-12 , max = 291.3585     [32m A new explainer has been created! [39m   Creating fairness regression object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [33m changed from character [39m )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [32m compatible [39m )   -> Metric calculation : 3/3 metrics calculated for all models    [32m Fairness regression object created succesfully [39m      Creating fairness regression object   -> Privileged subgroup : character ( [33m from first fairness object [39m )   -> Protected variable : factor ( [33m from first fairness object [39m )   -> Fairness objects : 1 object ( [32m compatible [39m )   -> Checking explainers : 2 in total ( [32m compatible [39m )   -> Metric calculation : 3/3 metrics calculated for all models    [32m Fairness regression object created succesfully [39m      Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [32m compatible [39m )   -> Metric calculation : 13/13 metrics calculated for all models    [32m Fairness object created succesfully [39m   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [31m not compatible [39m )   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [32m compatible [39m )   -> Metric calculation : 13/13 metrics calculated for all models    [32m Fairness object created succesfully [39m   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 1 object ( [32m compatible [39m )   -> Checking explainers : 2 in total ( [32m compatible [39m )   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [32m compatible [39m )   -> Metric calculation : 13/13 metrics calculated for all models    [32m Fairness object created succesfully [39m   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 2 objects ( [31m not compatible [39m )   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 2 in total ( [31m y not equal [39m )   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [32m compatible [39m )   -> Metric calculation : 13/13 metrics calculated for all models    [32m Fairness object created succesfully [39m   Creating fairness classification object   -> Privileged subgroup : character ( [33m from first fairness object [39m )   -> Protected variable : factor ( [33m from first fairness object [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 2 objects ( [32m compatible [39m )   -> Checking explainers : 2 in total ( [32m compatible [39m )   -> Metric calculation : 13/13 metrics calculated for all models    [32m Fairness object created succesfully [39m   Performace metric not given, setting deafult ( accuracy )   Performace metric not given, setting deafult ( accuracy )   Performace metric not given, setting deafult ( accuracy )   Fairness Metric not given, setting deafult ( TPR )   Fairness Metric not given, setting deafult ( TPR )   Fairness Metric not given, setting deafult ( TPR )   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 2 in total ( [32m compatible [39m )   -> Metric calculation : 8/13 metrics calculated for all models ( [33m5 NA created [39m )    [32m Fairness object created succesfully [39m   Fairness Metric not given, setting deafult ( TPR )   Performace metric not given, setting deafult ( accuracy )      Creating object with:   Fairness metric: TPR   Performance metric: accuracy         Creating object with:   Fairness metric: FPR   Performance metric: f1      Fairness data top rows for FPR                group score model   1 African_American 0.35204756 lm   2 Asian 0.04347826 lm   3 Caucasian 0.16393443 lm   4 Hispanic 0.11562500 lm   5 Native_American 0.16666667 lm   6 Other 0.07762557 lm      Performance data for f1 :   1 lm 0.6039853   2 ranger 0.6343798      Fairness Metric is NULL, setting deafult parity loss metric ( TPR )   Performace metric is NULL, setting deafult ( accuracy )      Creating object with:   Fairness metric: TPR   Performance metric: accuracy   Performace metric is NULL, setting deafult ( accuracy )      Creating object with:   Fairness metric: non_existing   Performance metric: accuracy   Fairness Metric is NULL, setting deafult parity loss metric ( TPR )      Creating object with:   Fairness metric: TPR   Performance metric: non_existing   Fairness Metric is NULL, setting deafult parity loss metric ( TPR )      Creating object with:   Fairness metric: TPR   Performance metric: auc   Fairness Metric is NULL, setting deafult parity loss metric ( TPR )      Creating object with:   Fairness metric: TPR   Performance metric: accuracy   Fairness Metric is NULL, setting deafult parity loss metric ( TPR )      Creating object with:   Fairness metric: TPR   Performance metric: precision   Fairness Metric is NULL, setting deafult parity loss metric ( TPR )      Creating object with:   Fairness metric: TPR   Performance metric: recall   Preparation of a new explainer is initiated     -> model label : lm ( [33m default [39m )     -> data : 6172 rows 7 cols     -> target variable : 6172 values     -> predict function : yhat.glm will be used ( [33m default [39m )     -> predicted values : No value for predict function target column. ( [33m default [39m )     -> model_info : package stats , ver. 4.6.0 , task classification ( [33m default [39m )     -> predicted values : numerical, min = 0.1144574 , mean = 0.4551199 , max = 0.995477     -> residual function : difference between y and yhat ( [33m default [39m )     -> residuals : numerical, min = -0.9767658 , mean = 5.053909e-13 , max = 0.8822826     [32m A new explainer has been created! [39m   Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [32m compatible [39m )   -> Metric calculation : 8/13 metrics calculated for all models ( [33m5 NA created [39m )    [32m Fairness object created succesfully [39m   Preparation of a new explainer is initiated     -> model label : lm ( [33m default [39m )     -> data : 1000 rows 3 cols     -> target variable : 1000 values     -> predict function : yhat.lm will be used ( [33m default [39m )     -> predicted values : No value for predict function target column. ( [33m default [39m )     -> model_info : package stats , ver. 4.6.0 , task regression ( [33m default [39m )     -> predicted values : numerical, min = -119.546 , mean = 756.4906 , max = 1594.562     -> residual function : difference between y and yhat ( [33m default [39m )     -> residuals : numerical, min = -302.6659 , mean = 3.478115e-13 , max = 332.7938     [32m A new explainer has been created! [39m   Creating fairness regression object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [33m changed from character [39m )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [32m compatible [39m )   -> Metric calculation : 3/3 metrics calculated for all models    [32m Fairness regression object created succesfully [39m      Preparation of a new explainer is initiated     -> model label : ranger ( [33m default [39m )     -> data : 1000 rows 3 cols     -> target variable : 1000 values     -> predict function : yhat.ranger will be used ( [33m default [39m )     -> predicted values : No value for predict function target column. ( [33m default [39m )     -> model_info : package ranger , ver. 0.17.0 , task regression ( [33m default [39m )     -> predicted values : numerical, min = 361.6527 , mean = 756.1869 , max = 1136.792     -> residual function : difference between y and yhat ( [33m default [39m )     -> residuals : numerical, min = -669.0748 , mean = 0.3037205 , max = 630.6428     [32m A new explainer has been created! [39m   Creating fairness regression object   -> Privileged subgroup : character ( [33m from first fairness object [39m )   -> Protected variable : factor ( [33m from first fairness object [39m )   -> Fairness objects : 1 object ( [32m compatible [39m )   -> Checking explainers : 2 in total ( [32m compatible [39m )   -> Metric calculation : 3/3 metrics calculated for all models    [32m Fairness regression object created succesfully [39m      Creating fairness classification object   -> Privileged subgroup : character ( [32m Ok [39m )   -> Protected variable : factor ( [32m Ok [39m )   -> Cutoff values for explainers : 0.5 ( for all subgroups )   -> Fairness objects : 0 objects   -> Checking explainers : 1 in total ( [32m compatible [39m )   -> Metric calculation : 13/13 metrics calculated for all models    [32m Fairness object created succesfully [39m      changing protected to factor   Preparation of a new explainer is initiated     -> model label : lm ( [33m default [39m )     -> data : 15 rows 2 cols     -> target variable : 15 values     -> predict function : yhat.glm will be used ( [33m default [39m )     -> predicted values : No value for predict function target column. ( [33m default [39m )     -> model_info : package stats , ver. 4.6.0 , task classification ( [33m default [39m )     -> predicted values : numerical, min = 7.884924e-12 , mean = 0.4666667 , max = 1     -> residual function : difference between y and yhat ( [33m default [39m )     -> residuals : numerical, min = -7.884924e-12 , mean = -5.256659e-13 , max = 7.884915e-12     [32m A new explainer has been created! [39m   [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ]      ══ Failed tests ════════════════════════════════════════════════════════════════   ── Error ('test_heatmap.R:2:3'): Test heatmap ──────────────────────────────────   Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object'   Backtrace:       ▆    1. ├─base::plot(fairness_heatmap(fobject)) at test_heatmap.R:2:3    2. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(fobject))    3. └─base::ifelse(...)   ── Failure ('test_plot_density.R:14:3'): Test plot_density ─────────────────────   plt$labels$x not equal to "probability".   target is NULL, current is character   ── Error ('test_plot_fairmodels.R:8:3'): Test plot_fairmodels ──────────────────   Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object'   Backtrace:        ▆     1. ├─base::suppressWarnings(...) at test_plot_fairmodels.R:8:3     2. │ └─base::withCallingHandlers(...)     3. ├─fairmodels:::expect_s3_class(...)     4. │ ├─testthat::expect(...) at tests/testthat/helper_objects.R:70:20     5. │ └─base::class(object) %in% class     6. ├─fairmodels::plot_fairmodels(fc, type = "fairness_heatmap")     7. └─fairmodels:::plot_fairmodels.fairness_object(fc, type = "fairness_heatmap")     8. └─fairmodels:::plot_fairmodels.default(x, type, ...)     9. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(x, ...))    10. └─base::ifelse(...)      [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ]   Error: Test failures   Execution halted
• checking for unstated dependencies in vignettes ... OK
• checking package vignettes ... OK
• checking re-building of vignette outputs ... [130s/163s] ERROR Error(s) in re-building vignettes: --- re-building ‘Advanced_tutorial.Rmd’ using rmarkdown [WARNING] Deprecated: --no-highlight. Use --syntax-highlighting=none instead. --- finished re-building ‘Advanced_tutorial.Rmd’ --- re-building ‘Basic_tutorial.Rmd’ using rmarkdown Quitting from Basic_tutorial.Rmd:254-257 [unnamed-chunk-19] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <error/rlang_error> Error in `rep()`: ! attempt to replicate an object of type 'object' --- Backtrace:     ▆  1. ├─base::plot(fheatmap, text_size = 3)  2. └─fairmodels:::plot.fairness_heatmap(fheatmap, text_size = 3)  3. └─base::ifelse(...) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'Basic_tutorial.Rmd' failed with diagnostics: attempt to replicate an object of type 'object' --- failed re-building ‘Basic_tutorial.Rmd’ SUMMARY: processing the following file failed:   ‘Basic_tutorial.Rmd’ Error: Vignette re-building failed. Execution halted
• checking PDF version of manual ... [9s/13s] OK
• checking HTML version of manual ... OK
• checking for non-standard things in the check directory ... OK
• checking for detritus in the temp directory ... OK
• DONE

Status: 3 ERRORs, 1 NOTE