knnflex: A more flexible KNN

#'A more flexible KNN
#'
#'A K-Nearest Neighbor (KNN) implementation which allows the specification of
#'the distance used to calculate nearest neighbors (euclidean, binary, etc.),
#'the aggregation method used to summarize response (majority class, mean,
#'etc.) and the method of handling ties (all, random selection, etc.).
#'Additionally, responses are not limited to classes, but can be continuous.
#'
#'
#'@name knnflex-package
#'@docType package
#'@note This work was funded by the National Institutes of Health through the
#'NIH Roadmap for Medical Research, Grant 1 P20 HG003900-01. Information on the
#'Molecular Libraries Roadmap Initiative can be obtained from
#'\url{http://nihroadmap.nih.gov/molecularlibraries/}.
#'@author Atina Dunlap Brooks
#'@seealso \code{\link[class]{knn}}
#'@keywords package
#'@examples
#'
#'# a quick classification example
#'x1 <- c(rnorm(20,mean=1),rnorm(20,mean=5))
#'x2 <- c(rnorm(20,mean=5),rnorm(20,mean=1))
#'x  <- cbind(x1,x2)
#'y <- c(rep(1,20),rep(0,20))
#'train <- sample(1:40,30)
#'# plot the training cases
#'plot(x1[train],x2[train],col=y[train]+1,xlab="x1",ylab="x2")
#'# predict the other cases
#'test <- (1:40)[-train]
#'kdist <- knn.dist(x)
#'preds <- knn.predict(train,test,y,kdist,k=3,agg.meth="majority")
#'# add the predictions to the plot
#'points(x1[test],x2[test],col=as.integer(preds)+1,pch="+")
#'# display the confusion matrix
#'table(y[test],preds)
#'
#'
#'# the iris example used by knn(class)
#'library(class)
#'data(iris3)
#'train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
#'test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
#'cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
#'# how to get predictions from knn(class)
#'pred<-knn(train, test, cl, k = 3)
#'# display the confusion matrix
#'table(pred,cl)
#'
#'# how to get predictions with knn.dist and knn.predict
#'x <- rbind(train,test)
#'kdist <- knn.dist(x)
#'pred <- knn.predict(1:75, 76:150, cl, kdist, k=3)
#'# display the confusion matrix
#'table(pred,cl)
#'
#'# note any small differences are a result of both methods
#'# breaking ties in majority class randomly
#'
#'
#'# 5-fold cross-validation to select k for above example
#'fold <- sample(1:5,75,replace=TRUE)
#'cvpred <- matrix(NA,nrow=75,ncol=10)
#'for (k in 1:10)
#'  for (i in 1:5)
#'    cvpred[which(fold==i),k] <- knn.predict(train=which(fold!=i),test=which(fold==i),cl,kdist,k=k)
#'# display misclassification rates for k=1:10
#'apply(cvpred,2,function(x) sum(cl!=x))
#'
NULL