* using log directory 'd:/RCompile/CRANincoming/R-devel/explainer.Rcheck'
* using R Under development (unstable) (2023-12-04 r85659 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 12.3.0
    GNU Fortran (GCC) 12.3.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* checking for file 'explainer/DESCRIPTION' ... OK
* this is package 'explainer' version '1.0.0'
* package encoding: UTF-8
* checking CRAN incoming feasibility ... NOTE
Maintainer: 'Ramtin Zargari Marandi <ramtin.zargari.marandi@regionh.dk>'

New submission

Possibly misspelled words in DESCRIPTION:
  Explainer (2:31)
  Shapley (7:105)

Found the following (possibly) invalid file URIs:
  URI: CONTRIBUTING.md
    From: README.md
  URI: LICENSE.md
    From: README.md
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking serialization versions ... OK
* checking whether package 'explainer' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking for future file timestamps ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
ShapFeaturePlot: no visible binding for global variable 'f_val'
ShapFeaturePlot: no visible binding for global variable 'Phi'
ShapFeaturePlot: no visible binding for global variable
  'correct_prediction'
ShapFeaturePlot: no visible global function definition for 'xlab'
ShapFeaturePlot: no visible global function definition for 'ylab'
eSHAP_plot: no visible binding for global variable 'feature'
eSHAP_plot: no visible binding for global variable 'f_val'
eSHAP_plot: no visible binding for global variable 'sample_num'
eSHAP_plot: no visible binding for global variable 'correct_prediction'
eSHAP_plot_reg: no visible binding for global variable 'feature'
eSHAP_plot_reg: no visible binding for global variable 'Phi'
eSHAP_plot_reg: no visible binding for global variable 'f_val'
eSHAP_plot_reg: no visible binding for global variable 'sample_num'
shapPartialPlot: no visible binding for global variable 'f_val'
shapPartialPlot: no visible binding for global variable 'pred_prob'
shapPartialPlot: no visible binding for global variable
  'correct_prediction'
shapPartialPlot: no visible global function definition for 'xlab'
shapPartialPlot: no visible global function definition for 'ylab'
Undefined global functions or variables:
  Phi correct_prediction f_val feature pred_prob sample_num xlab ylab
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... NOTE
Rd file 'SHAPclust.Rd':
  \examples lines wider than 100 characters:
     maintask <- mlr3::TaskClassif$new(id = "my_classification_task",backend = mydata,target = target_col,positive = positive_class)
     SHAP_output <- eSHAP_plot(task = maintask, trained_model = mylrn, splits = splits, sample.size = 30, seed = seed, subset = 0.8)
     SHAP_plot_clusters <- SHAPclust(task = maintask, trained_model = mylrn, splits = splits, shap_Mean_wide = shap_Mean_wide, shap_Mean_lon ... [TRUNCATED]

Rd file 'ShapFeaturePlot.Rd':
  \examples lines wider than 100 characters:
     maintask <- mlr3::TaskClassif$new(id = "my_classification_task",backend = mydata,target = target_col,positive = positive_class)
     SHAP_output <- eSHAP_plot(task = maintask, trained_model = mylrn, splits = splits, sample.size = 30, seed = seed, subset = 0.8)

Rd file 'eCM_plot.Rd':
  \examples lines wider than 100 characters:
     maintask <- mlr3::TaskClassif$new(id = "my_classification_task",backend = mydata,target = target_col,positive = positive_class)

Rd file 'eDecisionCurve.Rd':
  \examples lines wider than 100 characters:
     maintask <- mlr3::TaskClassif$new(id = "my_classification_task",backend = mydata,target = target_col,positive = positive_class)

Rd file 'eFairness.Rd':
  \examples lines wider than 100 characters:
     maintask <- mlr3::TaskClassif$new(id = "my_classification_task",backend = mydata,target = target_col,positive = positive_class)
     Fairness_results <- eFairness(task = maintask, trained_model = mylrn, splits = splits, target_variable = "sex", var_levels = c("Male",  ... [TRUNCATED]

Rd file 'eROC_plot.Rd':
  \examples lines wider than 100 characters:
     maintask <- mlr3::TaskClassif$new(id = "my_classification_task",backend = mydata,target = target_col,positive = positive_class)

Rd file 'eSHAP_plot.Rd':
  \examples lines wider than 100 characters:
     maintask <- mlr3::TaskClassif$new(id = "my_classification_task",backend = mydata,target = target_col,positive = positive_class)
     SHAP_output <- eSHAP_plot(task = maintask, trained_model = mylrn, splits = splits, sample.size = 30, seed = seed, subset = 0.8)

Rd file 'eSHAP_plot_reg.Rd':
  \examples lines wider than 100 characters:
     SHAP_output <- eSHAP_plot_reg(task = maintask, trained_model = mylrn, splits = splits, sample.size = 30, seed = seed, subset = 0.8)

Rd file 'shapPartialPlot.Rd':
  \examples lines wider than 100 characters:
     maintask <- mlr3::TaskClassif$new(id = "my_classification_task",backend = mydata,target = target_col,positive = positive_class)
     SHAP_output <- eSHAP_plot(task = maintask, trained_model = mylrn, splits = splits, sample.size = 30, seed = seed, subset = 0.8)

These lines will be truncated in the PDF manual.
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... WARNING
'library' or 'require' call not declared from: 'mlr3extralearners'
'data(package=)' call not declared from: 'mlbench'
* checking examples ... OK
* checking PDF version of manual ... [13s] OK
* checking HTML version of manual ... OK
* DONE
Status: 1 WARNING, 3 NOTEs