{"id":25109954,"url":"https://github.com/smac-group/ib","last_synced_at":"2025-04-22T07:24:18.243Z","repository":{"id":42470115,"uuid":"270962024","full_name":"SMAC-Group/ib","owner":"SMAC-Group","description":null,"archived":false,"fork":false,"pushed_at":"2024-10-16T16:37:36.000Z","size":218,"stargazers_count":2,"open_issues_count":1,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-10T21:40:20.076Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://smac-group.github.io/ib/","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/SMAC-Group.png","metadata":{"files":{"readme":"README.Rmd","changelog":"NEWS.md","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":"2020-06-09T09:39:30.000Z","updated_at":"2024-01-08T08:21:34.000Z","dependencies_parsed_at":"2024-10-18T12:14:00.449Z","dependency_job_id":null,"html_url":"https://github.com/SMAC-Group/ib","commit_stats":{"total_commits":78,"total_committers":1,"mean_commits":78.0,"dds":0.0,"last_synced_commit":"ef6816915a0a55c05f7454fed5b38c947cc84cbe"},"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SMAC-Group%2Fib","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SMAC-Group%2Fib/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SMAC-Group%2Fib/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SMAC-Group%2Fib/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SMAC-Group","download_url":"https://codeload.github.com/SMAC-Group/ib/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249837120,"owners_count":21332398,"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":[],"created_at":"2025-02-08T00:33:47.074Z","updated_at":"2025-04-20T01:29:16.098Z","avatar_url":"https://github.com/SMAC-Group.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\ntitle: \"\"\noutput: github_document\n---\n\n[![R-CMD-check](https://github.com/SMAC-Group/ib/workflows/R-CMD-check/badge.svg)](https://github.com/SMAC-Group/ib/actions)\n[![Last-changedate](https://img.shields.io/badge/last%20change-`r gsub('-', '--', Sys.Date())`-green.svg)](https://github.com/SMAC-Group/ib)\n[![license](https://img.shields.io/badge/license-GPL--3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.en.html)\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE)\n```\n\n# Bias correction via the iterative bootstrap\n\nThis is an under-development package that proposes the iterative bootstrap algorithm\nof [Kuk (1995)](https://doi.org/10.1111/j.2517-6161.1995.tb02035.x)\nand further studied by [Guerrier et al (2019)](https://doi.org/10.1080/01621459.2017.1380031)\nand [Guerrier et al (2020)](https://arxiv.org/pdf/2002.08757.pdf).\n\nIn order to install the package\n```{r, installation, eval = FALSE}\n## if not installed\n## install.packages(\"remotes\")\nremotes::install_github(\"SMAC-Group/ib\")\n```\n\nThe `ib` package is conceived as a wrapper: an `object` that needs a bias correction is supplied to the `ib()` function. For example, for a negative binomial regression:   \n\n```{r, glm.nb, eval=FALSE}\nlibrary(ib)\nlibrary(MASS)\nfit_nb \u003c- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine)\nfit_ib1 \u003c- ib(fit_nb)\nsummary(fit_ib1)\n\n## correct for overdispersion with H=100\nfit_ib2 \u003c- ib(fit_nb, control=list(H=100), extra_param = TRUE)\nsummary(fit_ib2)\n```\n\nCurrently we support `lm`, `glm`, `glm.nb`, `lmer`, `nls` and `vglm` classes, as shown in the example above with the overdispersion parameter of the negative binomial regression. More details\nare in `help(ib)`.\n\nOn top of `simulate`, we also consider cases where the response variable is generated using censoring, missing at random and outliers mechanisms (see `help(ibControl)` for more details). For example\n\n```{r, glm.nb with censoring, eval=FALSE}\n## suppose values above 30 are censored\nquine2 \u003c- transform(quine, Days=pmin(Days,30))\nfit_nb \u003c- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine2)\nfit_ib1 \u003c- ib(fit_nb, control = list(cens=TRUE, right=30))\nsummary(fit_ib1)\n\n## correct for overdispersion with H=100\nfit_ib2 \u003c- ib(fit_nb, control=list(H=100, cens=TRUE, right=30), extra_param = TRUE)\nsummary(fit_ib2)\n```\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmac-group%2Fib","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsmac-group%2Fib","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmac-group%2Fib/lists"}