kernelboot

Smoothed bootstrap and functions for sampling from kernel densities

https://github.com/twolodzko/kernelboot

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (7.9%) to scientific vocabulary

Keywords

bootstrap density kernel-density r random-generation simulation
Last synced: 6 months ago · JSON representation

Repository

Smoothed bootstrap and functions for sampling from kernel densities

Basic Info
  • Host: GitHub
  • Owner: twolodzko
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 2.89 MB
Statistics
  • Stars: 3
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 2
Topics
bootstrap density kernel-density r random-generation simulation
Created over 9 years ago · Last pushed almost 3 years ago
Metadata Files
Readme

README.md

kernelboot

CRAN_Status_Badge GitHub Actions CI Coverage Status Downloads

This package implements random generation procedures for sampling from kernel densities and smoothed bootstrap, that is an extension of standard bootstrap procedure, where instead of drawing samples with replacement from the empirical distribution, they are drawn from kernel density estimate of the distribution.

Three functions are provided to sample from univariate kernel densities (ruvk), multivariate product kernel densities (rmvk) and multivariate Gaussian kernel densities (rmvg). The ruvk function samples from the kernel densities as estimated using the base R density function. It offers possibility of sampling from kernel densities with Gaussian, Epanechnikov, rectangular, triangular, biweight, cosine, and optcosine kernels. The rmvk offers sampling from a multivariate kernel density constructed from independent univariate kernel densities. It is also possible to sample from multivariate Gaussian kernel density using the rmvg function, that allows for correlation between the variables.

Smooth bootstrap is possible by using the kernelboot function, that draws with replacement samples from the empirical distribution, enhances them using noise drawn from the kernel density and evaluates the user-provided statistic on the samples. This procedure can be thought as an extension of the basic bootstrap procedure.

Owner

  • Name: Timothy Wolodzko
  • Login: twolodzko
  • Kind: user
  • Location: Warsaw, Poland

GitHub Events

Total
Last Year

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 156
  • Total Committers: 6
  • Avg Commits per committer: 26.0
  • Development Distribution Score (DDS): 0.256
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Tymoteusz Wołodźko t****o@g****m 116
Tymoteusz Wołodźko t****o 21
Tymoteusz Wołodźko t****t@g****m 12
Tymoteusz Wolodzko t****o@a****l 3
Tymoteusz Wołodźko tw@g****m 3
Przemysław Biecek p****k 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 8
  • Total pull requests: 3
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 1 day
  • Total issue authors: 3
  • Total pull request authors: 2
  • Average comments per issue: 3.38
  • Average comments per pull request: 0.67
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • HenrikBengtsson (5)
  • twolodzko (2)
  • abnormally-distributed (1)
Pull Request Authors
  • pbiecek (1)
  • twolodzko (1)
Top Labels
Issue Labels
bug (1) unconfirmed (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 236 last-month
  • Total docker downloads: 21,777
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 10
  • Total maintainers: 1
cran.r-project.org: kernelboot

Smoothed Bootstrap and Random Generation from Kernel Densities

  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 236 Last month
  • Docker Downloads: 21,777
Rankings
Forks count: 21.9%
Stargazers count: 28.5%
Dependent packages count: 29.8%
Average: 29.8%
Downloads: 33.4%
Dependent repos count: 35.5%
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.1.0 depends
  • Rcpp * imports
  • future * imports
  • future.apply * imports
  • parallelly * imports
  • KernSmooth * suggests
  • covr * suggests
  • cramer * suggests
  • ks * suggests
  • testthat * suggests
.github/workflows/main.yml actions
  • actions/checkout v1 composite
  • r-lib/actions/setup-r v2 composite