bootstrap               This method computes predicted outcome for each
                        observation in the data frame using the tree
                        model supplied as an input argument.
compute.acc             Predictive accuracy estimates across trees for
                        logistic regression model
compute.mse             Predictive accuracy estimates (MSE) across
                        trees for linear or poisson regression model.
compute.r2              Predictive accuracy estimates across trees for
                        linear or poisson regression
get.mf.object.glm       Fit a general linear model to a mobForest model
get.mf.object.lm        Fit a linear model to a mobForest model
get.pred.values         Get predictions summarized across trees for
                        out-of-bag cases or all cases for cases from
                        new test data
get.varimp              Variable importance scores computed through
                        random forest analysis
logistic.acc            Contingency table: Predicted vs. Observed
                        Outcomes
mob.rf.tree             Model based recursive partitioning - randomized
                        subset of partition variables considered during
                        each split.
mob_fit_checksplit      Utility Function. Taken from party package to
                        remove ":::" warning
mob_fit_childweights    Utility Function. Taken from party package to
                        remove ":::" warning
mob_fit_fluctests       Utility Function. Taken from party package to
                        remove ":::" warning
mob_fit_getlevels       Utility Function. Taken from party package to
                        remove ":::" warning
mob_fit_getobjfun       Utility Function. Taken from party package to
                        remove ":::" warning
mob_fit_setupnode       Utility Function. Taken from party package to
                        remove ":::" warning
mob_fit_splitnode       Utility Function. Taken from party package to
                        remove ":::" warning
mobforest.analysis      Model-based random forest analysis
mobforest.control       Control parameters for random forest
mobforest.control-class
                        Class '"mobforest.control"' of mobForest model
mobforest.output        Model-based random forest object
mobforest.output-class
                        Class '"mobforest.output"' of mobforest model
prediction.output       Predictions and predictive accuracy estimates
prediction.output-class
                        Class '"prediction.output"' of mobForest model
predictive.acc          Predictive performance across all trees
print.estimates         Predictive Accuracy Report
residual.plot           Produces two plots: a) histogram of residuals,
                        b) predicted Vs residuals. This feature is
                        applicable only when linear regression is
                        considered as the node model.
string.formula          Model in the formula object converted to a
                        character
tree.predictions        Predictions from tree model
varimp.output           Variable importance matrix containing the
                        decrease in predictive accuracy after permuting
                        the variables across all trees
varimp.output-class     Class '"varimp.output"' of mobforest model
varimplot               A plot with variable importance score on X-axis
                        and variable name on Y-axis.
