Evaluation of Modeling without Information Leakage

as.modeling_procedure | Coerce to modeling procedure |

dichotomize | Dichotomize time-to-event data |

emil | Introduction to the emil package |

error_fun | Performance estimation functions |

evaluate | Evaluate a modeling procedure |

extension | Extending the emil framework with user-defined methods |

factor_to_logical | Convert factors to logicals |

fill | Replace values with something else |

fit | Fit a model |

fit_caret | Fit a model using the 'caret' package |

fit_cforest | Fit conditional inference forest |

fit_coxph | Fit Cox proportional hazards model |

fit_glmnet | Fit elastic net, LASSO or ridge regression model |

fit_lda | Fit linear discriminant |

fit_lm | Fit a linear model fitted with ordinary least squares |

fit_naive_bayes | Fit a naive Bayes classifier |

fit_pamr | Fit nearest shrunken centroids model. |

fit_qda | Fit quadratic discriminant. |

fit_randomForest | Fit random forest. |

fit_rpart | Fit a decision tree |

fit_svm | Fit a support vector machine |

get_color | Get color palettes |

get_importance | Feature (variable) importance of a fitted model |

get_performance | Extract prediction performance |

get_prediction | Extract predictions from modeling results |

get_response | Extract the response from a data set |

get_tuning | Extract parameter tuning statistics |

image.resample | Visualize resampling scheme |

importance_glmnet | Feature importance extractor for elastic net models |

importance_pamr | Feature importance of nearest shrunken centroids. |

importance_randomForest | Feature importance of random forest. |

impute | Regular imputation |

indent | Increase indentation |

index_fit | Convert a fold to row indexes of fittdng or test set |

is_blank | Wrapper for several methods to test if a variable is empty |

is_constant | Check if an object contains more than one unique value |

is_multi_procedure | Detect if modeling results contains multiple procedures |

learning_curve | Learning curve analysis |

list_method | List all available methods |

log_message | Print a timestamped and indented log message |

mode | Get the most common value |

modeling_procedure | Setup a modeling procedure |

na_index | Support function for identifying missing values |

name_procedure | Get names for modeling procedures |

neg_gmpa | Negative geometric mean of class specific predictive accuracy |

nice_axis | Plots an axis the way an axis should be plotted. |

nice_box | Plots a box around a plot |

nice_require | Load a package and offer to install if missing |

notify_once | Print a warning message if not printed earlier |

pipe | Pipe operator |

plot.learning_curve | Plot results from learning curve analysis |

plot_Surv | Plot Surv vector [DEPRECATED] |

predict_caret | Predict using a 'caret' method |

predict_cforest | Predict with conditional inference forest |

predict_coxph | Predict using Cox proportional hazards model |

predict_glmnet | Predict using generalized linear model with elastic net... |

predict_lda | Prediction using already trained prediction model |

predict_lm | Prediction using linear model |

predict.model | Predict the response of unknown observations |

predict_naive_bayes | Predict using naive Bayes model |

predict_pamr | Prediction using nearest shrunken centroids. |

predict_qda | Prediction using already trained classifier. |

predict_randomForest | Prediction using random forest. |

predict_rpart | Predict using a fitted decision tree |

predict_svm | Predict using support vector machine |

pre_factor_to_logical | Convert factors to logical columns |

pre_impute | Basic imputation |

pre_impute_df | Impute a data frame |

pre_impute_knn | Nearest neighbors imputation |

pre_log_message | Print log message during pre-processing |

pre_pamr | PAMR adapted dataset pre-processing |

pre_process | Data preprocessing |

print.preprocessed_data | Print method for pre-processed data |

pvalue | Extraction of p-value from a statistical test |

pvalue.coxph | Extract p-value from a Cox proportional hazards model |

pvalue.crr | Extracts p-value from a competing risk model |

pvalue.cuminc | Extract p-value from a cumulative incidence estimation |

pvalue.survdiff | Extracts p-value from a logrank test |

resample | Resampling schemes |

roc_curve | Calculate ROC curves |

select | 'emil' and 'dplyr' integration |

subresample | Generate resampling subschemes |

subtree | Extract a subset of a tree of nested lists |

trivial_error_rate | Calculate the trivial error rate |

tune | Tune parameters of modeling procedures |

validate_data | Validate a pre-processed data set |

vlines | Add vertical or horizontal lines to a plot |

weighted_error_rate | Weighted error rate |

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