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