NNS.distance

REGRESSION.POINT.MATRIX from <a rd-options="" href="/link/NNS.reg?package=NNS&version=0.8.5" data-mini-rdoc="NNS::NNS.reg">NNS.reg</a>

rpm_class

Vector to generate distances from.

dist.estimate

type

class

Internal kernel function for NNS multivariate regression <a rd-options="" href="/link/NNS.reg?package=NNS&version=0.8.5" data-mini-rdoc="NNS::NNS.reg">NNS.reg</a> parallel instances.

Nonlinear nonparametric statistics using partial moments.  Partial moments are the elements of variance and asymptotically approximate the area of f(x).  These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences.  NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance.  All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).

Fred Viole

Nonlinear Nonparametric Statistics

NNS.distance function

REGRESSION.POINT.MATRIX from <a rd-options='' href='NNS.reg'>NNS.reg</a>

Internal kernel function for NNS multivariate regression <a rd-options='' href='NNS.reg'>NNS.reg</a> parallel instances.

NNS.distance: NNS Distance

Description

Usage

Arguments

Value