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