
SemiPar
 Referenced in 714 articles
[sw07116]
 existing regression texts treat either parametric or nonparametric regression exclusively. In this book the authors ... argue that nonparametric regression can be viewed as a relatively simple extension of parametric regression...

KernSmooth
 Referenced in 947 articles
[sw04586]
 smoothers the authors concentrate on the simplest nonparametric curve estimation setting, namely density and regression...

SAS/STAT
 Referenced in 423 articles
[sw18788]
 survival analysis, psychometric analysis, cluster analysis, and nonparametric analysis. A few examples include mixed models...

bootlib
 Referenced in 408 articles
[sw40642]
 with single samples from parametric and nonparametric models. Chapter 3 extends the basic ideas...

gss
 Referenced in 289 articles
[sw06099]
 Smoothing spline ANOVA models Nonparametric function estimation with stochastic data, otherwise known as smoothing...

np
 Referenced in 97 articles
[sw10543]
 Nonparametric Econometrics: The np Package. We describe the R np package via a series ... package implements a variety of nonparametric and semiparametric kernelbased estimators that are popular among ... econometricians. There are also procedures for nonparametric tests of significance and consistent model specification tests...

DPpackage
 Referenced in 68 articles
[sw10495]
 DPpackage: Bayesian Semi and Nonparametric Modeling in R. Data analysis sometimes requires the relaxation ... programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently...

ftnonpar
 Referenced in 67 articles
[sw11128]
 ftnonpar: Features and Strings for Nonparametric Regression. The package contains Rfunctions to perform ... methods in nonparametric regression and density estimation, described in Davies, P. L. and Kovac...

TwoCop
 Referenced in 56 articles
[sw12359]
 TwoCop: Nonparametric test of equality between two copulas. This package implements the nonparametric test...

BartPy
 Referenced in 73 articles
[sw40584]
 from a posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive...

sm
 Referenced in 70 articles
[sw12256]
 package sm: Smoothing methods for nonparametric regression and density estimation. This is software linked...

BayesTree
 Referenced in 60 articles
[sw07995]
 from a posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive...

reccv
 Referenced in 36 articles
[sw26388]
 Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study. Wavelet analysis has been found ... powerful tool for the nonparametric estimation of spatiallyvariable objects. We discuss in detail wavelet ... methods in nonparametric regression, where the data are modelled as observations of a signal contaminated...

MNM
 Referenced in 45 articles
[sw06075]
 package MNM: Multivariate Nonparametric Methods. An Approach Based on Spatial Signs and Ranks , The package...

S+WAVELETS
 Referenced in 36 articles
[sw12244]
 diverse as data visualization and analysis, nonparametric statistical estimation, signal and image compression, signal processing...

faraway
 Referenced in 24 articles
[sw04357]
 with R. Generalized linear, mixed effects and nonparametric regression models. Linear models are central ... linear models (GLMs), mixed effect models, and nonparametric regression models. The author’s treatment...

ConfBands
 Referenced in 31 articles
[sw12330]
 methods: (a) as a standard (fixed effect) nonparametric model, (b) using the mixedmodel framework...

missForest
 Referenced in 31 articles
[sw19483]
 package missForest: Nonparametric Missing Value Imputation using Random Forest. The function ’missForest’ in this package...

bnpmr
 Referenced in 20 articles
[sw11018]
 bnpmr: Bayesian monotonic nonparametric regression. Implements the Bayesian nonparametric monotonic regression method described in Bornkamp...

decon
 Referenced in 20 articles
[sw11088]
 collection of functions to deal with nonparametric measurement error problems using deconvolution kernel methods ... distribution function from contaminated data; (2) nonparametric regression model with errorsinvariables...