Package: KDEmcmc 0.0.2

KDEmcmc: Kernel Density Estimation with a Markov Chain Monte Carlo Sample

Provides methods for selecting the optimal bandwidth in kernel density estimation for dependent samples, such as those generated by Markov chain Monte Carlo (MCMC). Implements a modified biased cross-validation (mBCV) approach that accounts for sample dependence, improving the accuracy of estimated density functions.

Authors:Juhee Lee [aut, cre], Hang J. Kim [aut], Young-Min Kim [aut]

KDEmcmc_0.0.2.tar.gz
KDEmcmc_0.0.2.zip(r-4.7)KDEmcmc_0.0.2.zip(r-4.6)KDEmcmc_0.0.2.zip(r-4.5)
KDEmcmc_0.0.2.tgz(r-4.6-x86_64)KDEmcmc_0.0.2.tgz(r-4.6-arm64)KDEmcmc_0.0.2.tgz(r-4.5-x86_64)KDEmcmc_0.0.2.tgz(r-4.5-arm64)
KDEmcmc_0.0.2.tar.gz(r-4.7-arm64)KDEmcmc_0.0.2.tar.gz(r-4.7-x86_64)KDEmcmc_0.0.2.tar.gz(r-4.6-arm64)KDEmcmc_0.0.2.tar.gz(r-4.6-x86_64)
KDEmcmc_0.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
KDEmcmc/json (API)

# Install 'KDEmcmc' in R:
install.packages('KDEmcmc', repos = c('https://jhlee-projects.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • simMCMC - Simulated Markov Chain Monte Carlo Sample

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

1.00 score 170 downloads 4 exports 2 dependencies

Last updated from:fe4aab57d1. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK133
linux-devel-x86_64OK117
source / vignettesOK143
linux-release-arm64OK109
linux-release-x86_64OK121
macos-release-arm64OK88
macos-release-x86_64OK168
macos-oldrel-arm64OK92
macos-oldrel-x86_64OK264
windows-develOK178
windows-releaseOK133
windows-oldrelOK134
wasm-releaseOK92

Exports:cKDEmBCVplot.mBCV_objprint.mBCV_obj

Dependencies:RcppRcppArmadillo