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