# Back-Projected Kernel Density Estimation

The following source code and examples are used for Back-Projected Kernel Density Estimation that carry out Nonparametric multivariate kernel density \ estimation using a back-projected kernel.

The following source code and examples are used for Nonparametric tests of independence between random vectors that is testing mutual independence between many numerical random vectors or serial independence of a multivariate stationary sequence.

The following source code and examples are used for Features and Strings for Nonparametric Regression that contains R-functions to perform the methods in nonparametric regression and density estimation, described in Davies, P. L. and Kovac, A.

The following source code and examples are used for Sample size calculations for normal means using three different Bayesian criteria in the context of designing an experiment.

The following source code and examples are used for GEV conditional density estimation network that provide A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology.

The following source code and examples are used for nonparametric relative contrast effects that compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout.

This package and source code implements nonparametric test of equality between two copulas.

The following package and source code is used to compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave.

The following package and source code is an extension of the Kruskal-Wallis Test that allow selection of arbitrary reference group.

The following package Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases.

This package provides tests of significance for covariates in a fully nonparametric regression model and a variable selection procedure based on False Discovery Rate.

This package is designed to perform nonparametric analysis of longitudinal data in factorial experiments.