Support function for identifying missing values
Fit quadratic discriminant.
PAMR adapted dataset pre-processing
Load a package and offer to install if missing
List all available methods
Wrapper for several methods to test if a variable is empty
Perform modeling
Nearest neighbors imputation
Fit linear discriminant
Dichotomize time-to-event data
Fit random forest.
Regular imputation
Fit conditional inference forest
Convert a fold to row indexes of fittdng or test set
Plots an axis the way an axis should be plotted.
Extract prediction performance
Negative geometric mean of class specific predictive accuracy
Fit a linear model fitted with ordinary least squares
Fit GLM with LASSO, Ridge or elastic net regularization.
Basic imputation
Predict using a caret method
Get color palettes
Print a timestamped and indented log message
Prediction using nearest shrunken centroids.
Fit nearest shrunken centroids model.
Increase indentation
Replace values with something else
Plots a box around a plot
Feature importance of nearest shrunken centroids.
Extending the emil framework with user-defined methods
Predict with conditional inference forest
Prediction using random forest.
Introduction to the emil package
Plot Surv vector
Prediction using already trained prediction model
Extract parameter tuning statistics
Feature (variable) importance of a fitted model
Learning curve analysis
Fit a model
Extract predictions from modeling results
Predict the response of unknown observations
Prediction using linear model
Data preprocessing
Plot results from learning curve analysis
Detect if modeling results contains multiple procedures
Performance estimation functions
Extract p-value from a Cox proportional hazards model
Print a warning message if not printed earlier
Visualize resampling scheme
Fit a model using the caret package
Get names for modeling procedures
Extract p-value from a cumulative incidence estimation
Add vertical or horizontal lines to a plot
Generate resampling subschemes
Resampling schemes
Calculate ROC curves
Feature importance of random forest.
emil and dplyr integration
Extracts p-value from a logrank test
Extracts p-value from a competing risk model
Prediction using already trained classifier.
Predict using generalized linear model with elastic net regularization
Setup a modeling procedure
Tune parameters of modeling procedures
Extract a subset of a tree of nested lists
Extraction of p-value from a statistical test
Weighted error rate