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
plot.Surv               Plot Surv vector
plot.learning_curve     Plot results from learning curve analysis
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
predict.model           Predict the response of unknown observations
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 regularization
predict_lda             Prediction using already trained prediction
                        model
predict_lm              Prediction using linear model
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
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
