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| 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 |
| as.Surv | Convert object to Surv vector |
| as.Surv.outcome | Convert outcome vector to Surv vector |
| as.Surv.Surv | Trivial function |
| batch.model | Perform modeling |
| detune | Tune parameters of modeling procedures |
| 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 |
| error.rate | 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.crossval | Visualize resampling scheme |
| image.resample | Visualize resampling scheme |
| impute | Regular imputation |
| impute.knn | Regular imputation |
| impute.median | Regular imputation |
| index.fit | Convert a fold to row indexes of fittdng or test set |
| index.test | 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 |
| is.tunable | Tune parameters of modeling procedures |
| is.tuned | Tune parameters of modeling procedures |
| length.outcome | Length of an outcome vector |
| modeling.procedure | Setup a modeling procedure |
| mse | Performance estimation functions |
| na.fill | Replace values with something else |
| neg.auc | Performance estimation functions |
| neg.gmpa | Negative geometric mean of class specific predictive accuracy |
| neg.harrell.C | Performance estimation functions |
| 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.center | Data preprocessing |
| pre.impute.knn | kNN imputation |
| pre.impute.median | Data preprocessing |
| pre.pamr | PAMR adapted dataset pre-processing |
| pre.process | Data preprocessing |
| pre.scale | Data preprocessing |
| pre.split | Data preprocessing |
| predict.modeling.procedure | Predict the response of unknown observations |
| print.outcome | Print outcome vector |
| resample | Resampling schemes |
| resample.crossval | Resampling schemes |
| resample.holdout | Resampling schemes |
| resample.mapply | Compare true response to resampled predictions |
| reset.warn.once | Print a warning message if not printed earlier |
| rmse | Performance estimation functions |
| 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 |
| [.outcome | Extract |