{"id":16260167,"url":"https://github.com/twolodzko/kernelboot","last_synced_at":"2025-03-19T22:31:07.293Z","repository":{"id":56936510,"uuid":"71496047","full_name":"twolodzko/kernelboot","owner":"twolodzko","description":"Smoothed bootstrap and functions for sampling from kernel densities","archived":false,"fork":false,"pushed_at":"2023-04-14T10:08:26.000Z","size":3031,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-17T11:50:34.892Z","etag":null,"topics":["bootstrap","density","kernel-density","r","random-generation","simulation"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/twolodzko.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-10-20T19:18:21.000Z","updated_at":"2024-03-01T09:32:38.000Z","dependencies_parsed_at":"2024-10-27T21:37:45.957Z","dependency_job_id":"7ad473b5-188c-4c15-b5df-526268f79d27","html_url":"https://github.com/twolodzko/kernelboot","commit_stats":{"total_commits":156,"total_committers":6,"mean_commits":26.0,"dds":0.2564102564102564,"last_synced_commit":"014753d9d0b5e45446f7a18950853700c97d4a9a"},"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/twolodzko%2Fkernelboot","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/twolodzko%2Fkernelboot/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/twolodzko%2Fkernelboot/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/twolodzko%2Fkernelboot/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/twolodzko","download_url":"https://codeload.github.com/twolodzko/kernelboot/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244515637,"owners_count":20464923,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bootstrap","density","kernel-density","r","random-generation","simulation"],"created_at":"2024-10-10T16:06:36.451Z","updated_at":"2025-03-19T22:31:06.803Z","avatar_url":"https://github.com/twolodzko.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# kernelboot\n\n[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/kernelboot)](https://CRAN.R-project.org/package=kernelboot)\n[![GitHub Actions CI](https://github.com/twolodzko/kernelboot/workflows/CI/badge.svg)](https://github.com/twolodzko/kernelboot/actions?query=workflow%3ACI)\n[![Coverage Status](https://img.shields.io/codecov/c/github/twolodzko/kernelboot/master.svg)](https://codecov.io/github/twolodzko/kernelboot?branch=master)\n[![Downloads](https://cranlogs.r-pkg.org/badges/grand-total/kernelboot)](https://CRAN.R-project.org/package=kernelboot)\n\n\nThis package implements random generation procedures for sampling from kernel\ndensities and smoothed bootstrap, that is an extension of standard bootstrap\nprocedure, where instead of drawing samples with replacement from the empirical\ndistribution, they are drawn from kernel density estimate of the distribution.\n\nThree functions are provided to sample from univariate kernel densities (`ruvk`),\nmultivariate product kernel densities (`rmvk`) and multivariate Gaussian kernel\ndensities (`rmvg`). The `ruvk` function samples from the kernel densities as \nestimated using the base R `density` function. It offers possibility of sampling\nfrom kernel densities with Gaussian, Epanechnikov, rectangular, triangular, biweight,\ncosine, and optcosine kernels. The `rmvk` offers sampling from a multivariate kernel\ndensity constructed from independent univariate kernel densities. It is also possible\nto sample from multivariate Gaussian kernel density using the `rmvg` function,\nthat allows for correlation between the variables.\n\nSmooth bootstrap is possible by using the `kernelboot` function, that draws with\nreplacement samples from the empirical distribution, enhances them using noise\ndrawn from the kernel density and evaluates the user-provided statistic on the\nsamples. This procedure can be thought as an extension of the basic bootstrap\nprocedure.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftwolodzko%2Fkernelboot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftwolodzko%2Fkernelboot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftwolodzko%2Fkernelboot/lists"}