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