[.outcome               Extract
as.Surv                 Convert object to Surv vector
as.Surv.Surv            Trivial function
as.Surv.outcome         Convert outcome vector to Surv vector
as.character.outcome    Convert outcome vector to character vector
as.data.frame.outcome   Convert outcome vector to data frame
as.matrix.outcome       Convert outcome vector to matrix
as.outcome              Convert object to outcome vector
as.outcome.Surv         Convert Surv vector to outcome vector
batch.model             Perform modeling
dim.outcome             Dimension of an outcome vector
emil                    Introduction to the emil package
emil.extensions         Extending the emil framework with user-defined
                        methods
emil.fit.caret          Fit a model using the caret package
emil.fit.cforest        Fit conditional inference forest
emil.fit.glmnet         Fit GLM with LASSO, Ridge or elastic net
                        regularization.
emil.fit.lda            Fit linear discriminant
emil.fit.lm             Fit a linear model fitted with ordinary least
                        squares
emil.fit.pamr           Fit nearest shrunken centroids model.
emil.fit.qda            Fit quadratic discriminant.
emil.fit.randomForest   Fit random forest.
emil.predict.caret      Predict using a caret method
emil.predict.cforest    Predict with conditional inference forest
emil.predict.glmnet     Predict using generalized linear model with
                        elastic net regularization
emil.predict.lda        Prediction using already trained prediction
                        model
emil.predict.lm         Prediction using linear model
emil.predict.pamr       Prediction using nearest shrunken centroids.
emil.predict.qda        Prediction using already trained classifier.
emil.predict.randomForest
                        Prediction using random forest.
emil.vimp.pamr          Variable importance of nearest shrunken
                        centroids.
emil.vimp.randomForest
                        Variable importance of random forest.
error.fun               Performance estimation functions
evaluate.modeling       Performance estimation of modeling procedures
factor.events           Get events on factor form
fill                    Replace values with something else
fit                     Fit a model
image.resample          Visualize resampling scheme
impute                  Regular imputation
index.fit               Convert a fold to row indexes of fittdng or
                        test set
integer.events          Return events in integer form
is.blank                Wrapper for several methods to test if a
                        variable is empty
is.na.outcome           Check for missing values
is.outcome              Test if object is of class outcome
length.outcome          Length of an outcome vector
modeling.procedure      Setup a modeling procedure
neg.gmpa                Negative geometric mean of class specific
                        predictive accuracy
nice.require            Load a package and offer to install if missing
outcome                 Create a vector of outcomes
p.value                 Extraction of p-value from a statistical test
p.value.coxph           Extract p-value from a Cox proportional hazards
                        model
p.value.crr             Extracts p-value from a competing risk model
p.value.cuminc          Extract p-value from a cumulative incidence
                        estimation
p.value.survdiff        Extracts p-value from a logrank test
plot.outcome            Plot outcome vector
pre.impute.knn          kNN imputation
pre.pamr                PAMR adapted dataset pre-processing
pre.process             Data preprocessing
predict.modeling.procedure
                        Predict the response of unknown observations
print.outcome           Print outcome vector
resample                Resampling schemes
resample.mapply         Compare true response to resampled predictions
subframe                Extract and organize predictions according to a
                        resampling scheme
subresample             Generate resampling subschemes
subtree                 Extract a subset of a tree of nested lists
trace.msg               Print a timestamped and indented log message
tune                    Tune parameters of modeling procedures
vimp                    Variable importance of a fitted model
warn.once               Print a warning message if not printed earlier
weighted.error.rate     Weighted error rate
