useDynLib(randomUniformForest, .registration = TRUE)
exportPattern("^[[:alpha:]]+")
import(methods, parallel, doParallel, foreach, ggplot2, cluster, MASS)
importFrom("grDevices", "dev.new", "dev.off", "graphics.off", "heat.colors")
importFrom("graphics", "abline", "barplot", "grid", "legend", "mosaicplot", "par", "persp", "points", "title")
importFrom("stats", "aggregate", "cor", "dist", "kmeans", "lm", "median", "model.frame", "model.matrix", "model.response", "na.omit", "pbinom", "predict", "qnorm", "quantile", "rnorm", "runif", "sd", "var", "cov", "density")
importFrom("utils", "head", "memory.limit")
importFrom("pROC", "auc")
importFrom("Rcpp", "loadModule", "evalCpp")

export(randomUniformForest.default, importance.randomUniformForest, rUniformForest.big, rUniformForest.combine, rUniformForest.grow, partialDependenceBetweenPredictors, partialDependenceOverResponses, partialImportance, postProcessingVotes, rm.trees, roc.curve, fillNA2.randomUniformForest, bCI, init_values, getTree.randomUniformForest, plotTree, as.supervised, clusteringObservations, mergeClusters, modifyClusters, splitClusters, unsupervised.randomUniformForest, generic.cv, model.stats, update.unsupervised, combineUnsupervised, clusterAnalysis, rm.coordinates, rufImpute)

S3method(print, randomUniformForest)
S3method(predict, randomUniformForest)
S3method(plot, randomUniformForest)
S3method(summary, randomUniformForest)
S3method(importance, randomUniformForest)
S3method(randomUniformForest, formula)
S3method(randomUniformForest, default)
S3method(plot, importance)
S3method(print, importance)
S3method(plot, unsupervised)
S3method(print, unsupervised)
S3method(update, unsupervised)
S3method(unsupervised, randomUniformForest)
