count_variables         count the number of times each variable is used
                        in a ranger random forest
determine.C             determine.C
determine_cutoff        evaluate a measure that can be used to
                        determining a significance level for the Mean
                        Decrease in Impurity measure returned by a
                        Random Forest model
f.fit                   fit a spline to the histogram of imp
fit.to.data.set         fit.to.data.set
fit.to.data.set.wrapper
                        fit.to.data.set.wrapper
imp20000                20000 importance values
local.fdr               local fdr
my.dsn                  my.dsn
my.test1fun             my.test1fun
my_PIMP                 my_PIMP based on the PIMP function from the
                        vita package. <e2><80><98>PIMP<e2><80><99>
                        implements the test approach of Altmann et al.
                        (2010) for the permutation variable importance
                        measure <e2><80><98>VarImp<e2><80><99> returned
                        by the randomForest package (Liaw and Wiener
                        (2002)) for classification and regression.
my_ranger_PIMP          my_ranger_PIMP based on the PIMP function from
                        the vita package. <e2><80><98>PIMP<e2><80><99>
                        implements the test approach of Altmann et al.
                        (2010) for the permutation variable importance
                        measure <e2><80><98>VarImp<e2><80><99> returned
                        by the randomForest package (Liaw and Wiener
                        (2002)) for classification and regression.
plotQ                   plotQ
propTrueNullByLocalFDR
                        propTrueNullByLocalFDR
run.it.importances      run.it.importances
significant.genes       significant.genes
