• KernSmooth

  • Referenced in 880 articles [sw04586]
  • kernel smoothers the authors concentrate on the simplest nonparametric curve estimation setting, namely density ... amount of research in the field of kernel smoothing. But the bibliographical notes...
  • WAFO

  • Referenced in 22 articles [sw07370]
  • density of rainflow cycles Sea modelling: Simulation of linear and non-linear Gaussian waves; Estimation ... period distributions Statistics: Extreme value analysis; Kernel density estimation, Hidden markov models...
  • AS 176

  • Referenced in 15 articles [sw27946]
  • Algorithm AS 176. Kernel density estimation using the fast Fourier transform...
  • DENCLUE

  • Referenced in 10 articles [sw28734]
  • DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation. The Denclue algorithm employs a cluster ... model based on kernel density estimation. A cluster is defined by a local maximum ... estimated density function. Data points are assigned to clusters by hill climbing, i.e. points going ... hill climbing procedure for Gaussian kernels, which adjusts the step size automatically at no extra...
  • sm

  • Referenced in 66 articles [sw12256]
  • Smoothing methods for nonparametric regression and density estimation. This is software linked to the book ... Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations’ Oxford University...
  • evmix

  • Referenced in 6 articles [sw19523]
  • Modelling, Threshold Estimation and Boundary Corrected Kernel Density Estimation. The usual distribution functions, maximum likelihood ... extreme value mixture models are provided. Kernel density estimation including various boundary corrected kernel density...
  • KDE Toolbox

  • Referenced in 7 articles [sw20503]
  • Kernel Density Estimation Toolbox for MATLAB. The KDE class is a general matlab class ... dimensional kernel density estimation. It is written in a mix of matlab ”.m” files...
  • pdfCluster

  • Referenced in 11 articles [sw14640]
  • performs cluster analysis via nonparametric density estimation. Operationally, the kernel method is used throughout ... routine to estimate the probability density function obtained by the kernel method, given...
  • SCWRL4

  • Referenced in 7 articles [sw22393]
  • backbone-dependent rotamer library based on kernel density estimates; 2) averaging over samples of conformations...
  • lsbs

  • Referenced in 6 articles [sw28186]
  • Estimation. Bandwidth selection for kernel density estimators of 2-d level sets and highest density...
  • feature

  • Referenced in 3 articles [sw23520]
  • Local Inferential Feature Significance for Multivariate Kernel Density Estimation. Local inferential feature significance for multivariate ... kernel density estimation...
  • kdevine

  • Referenced in 3 articles [sw20086]
  • package kdevine: Multivariate Kernel Density Estimation with Vine Copulas. Implements the vine copula based kernel...
  • kdensity

  • Referenced in 2 articles [sw31518]
  • package kdensity: Kernel Density Estimation with Parametric Starts and Asymmetric Kernels. Handles univariate non-parametric ... estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density ... estimation with parametric starts involves fitting a parametric density to the data before making ... density estimation, see Hjort & Glad (1995) . Asymmetric kernels make kernel density estimation...
  • GoFKernel

  • Referenced in 4 articles [sw32640]
  • Testing Goodness-of-Fit with the Kernel Density Estimator. Tests of goodness-of-fit based...
  • pyMEF

  • Referenced in 2 articles [sw07455]
  • Learning mixtures by simplifying kernel density estimators Gaussian mixture models are a widespread tool ... means, often using expectation-maximization or kernel density estimation. In addition to these well-known ... compute. On the other hand, kernel density estimation yields large models which are computationally cheap ... accomplished by a simplification of the kernel density estimator. The simplification is a clustering method...
  • nprobust

  • Referenced in 2 articles [sw30833]
  • Methods using Local Polynomial Regression and Kernel Density Estimation. Tools for data-driven statistical analysis ... using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo ... polynomial bandwidth selection, kdrobust() for kernel density point estimation and robust bias-corrected inference, kdbwselect...
  • logKDE

  • Referenced in 2 articles [sw31519]
  • package logKDE: Computing Log-Transformed Kernel Density Estimates for Positive Data. Computes log-transformed kernel ... density estimates for positive data using a variety of kernels. It follows the methods described...
  • hdrcde

  • Referenced in 5 articles [sw08066]
  • dimensions, kernel estimation of univariate density functions conditional on one covariate, and multimodal regression...
  • decon

  • Referenced in 16 articles [sw11088]
  • with nonparametric measurement error problems using deconvolution kernel methods. We focus two measurement error models ... model, where the goal is to estimate the density or distribution function from contaminated data ... algorithm for density estimation with error-free data to the deconvolution kernel estimation. Several methods...
  • LS-SVMlab

  • Referenced in 24 articles [sw07367]
  • density estimation which has also led to many other recent developments in kernel based methods...