bestBoostingIter |
Get the best number of boosting iterations |
calcAUC |
Fast computation of the AUC w.r.t. to the ROC |
calcBrier |
Calculate the Brier score |
calcDev |
Calculate the deviance |
calcMis |
Calculate the misclassification rate |
calcMSE |
Calculate the MSE |
calcNCE |
Calculate the normalized cross entropy |
calcNRMSE |
Calculate the NRMSE |
cooling.schedule |
Define the cooling schedule for simulated annealing |
cv.prune |
Optimal pruning via cross-validation |
fancy.plot |
Plot a logic decision tree |
fit4plModel |
Fitting 4pL models |
fitLinearBoostingModel |
Linear models based on boosted models |
fitLinearLogicModel |
Linear models based on logic terms |
fitLinearModel |
Fitting linear models |
get.ideal.penalty |
Tuning the LASSO regularization parameter |
getDesignMatrix |
Design matrix for the set of conjunctions |
gxe.test |
Gene-environment interaction test |
gxe.test.boosting |
Gene-environment (GxE) interaction test based on boosted linear models |
importance.test.boosting |
Term importance test based on boosted linear models |
logicDT |
Fitting logic decision trees |
logicDT.bagging |
Fitting bagged logicDT models |
logicDT.bagging.default |
Fitting bagged logicDT models |
logicDT.bagging.formula |
Fitting bagged logicDT models |
logicDT.boosting |
Fitting boosted logicDT models |
logicDT.boosting.default |
Fitting boosted logicDT models |
logicDT.boosting.formula |
Fitting boosted logicDT models |
logicDT.default |
Fitting logic decision trees |
logicDT.formula |
Fitting logic decision trees |
partial.predict |
Partial prediction for boosted models |
plot.logicDT |
Plot a logic decision tree |
plot.vim |
Plot calculated VIMs |
predict.4pl |
Prediction for 4pL models |
predict.genetic.logicDT |
Prediction for logicDT models |
predict.linear |
Prediction for linear models |
predict.linear.logic |
Prediction for 'linear.logic' models |
predict.logic.bagged |
Prediction for logicDT models |
predict.logic.boosted |
Prediction for logicDT models |
predict.logicDT |
Prediction for logicDT models |
prune |
Post-pruning using a fixed complexity penalty |
prune.path |
Pruning path of a logic decision tree |
refitTrees |
Refit the logic decision trees |
splitSNPs |
Split biallelic SNPs into binary variables |
tree.control |
Control parameters for fitting decision trees |
vim |
Variable Importance Measures (VIMs) |