`rbyb(p, m, eta) rbyp(p, m, eta) rbyv(p, m, nu) rbyz(p, m) rbyz.sim(p, m, nsim=1000) rbyz.geo(p, m=floor(sqrt(p)), rmax=p) rbylambda(p, m, lambda=1) knn(train, test, cl, k=1) knn.cv (train, cl, k=1) knn.reg(train, test = NULL, y, k = 3) pressresid(obj)`

m

Number of elements in a subset to be drawn.

p

Total number of available features.

mtry

Number of features to be drawn for each KNN.

eta

Coverage Probability.

nu

mean mutiplicity of a feature

rmax

number of series terms for independent geometric approximation

nsim

number of simulations for geometric simulation.

lambda

mean number of silient features.

samples

A vector of indice for a set of observations.

cl

A factor for classification labels.

train

A data matrix.

test

A data matrix.

y

A vector of responses.

k

Number of nearest neighbors.

cl

A vector of class labels.

K

Number of folds for cross-validation.

pk

A real number between 0 and to indicate the proportion of the feature set to be kept in each step.

r

Number of KNN to be generated.

seed

An integer seed.

criterion

either uses mean_accuracy or mean_support for best.

obj

A linear model.