{"id":18733540,"url":"https://github.com/insightsengineering/rbmi","last_synced_at":"2025-04-12T18:31:47.726Z","repository":{"id":37867385,"uuid":"373926232","full_name":"insightsengineering/rbmi","owner":"insightsengineering","description":"Reference based multiple imputation R package","archived":false,"fork":false,"pushed_at":"2025-02-26T14:58:17.000Z","size":17517,"stargazers_count":18,"open_issues_count":31,"forks_count":9,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-04-11T11:53:18.483Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://insightsengineering.github.io/rbmi/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/insightsengineering.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.md","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},"funding":{"custom":["https://pharmaverse.org"]}},"created_at":"2021-06-04T18:16:44.000Z","updated_at":"2025-03-11T11:35:29.000Z","dependencies_parsed_at":"2023-02-08T13:15:34.189Z","dependency_job_id":"30376ba9-4e54-463b-a1a5-411ca43ac7bf","html_url":"https://github.com/insightsengineering/rbmi","commit_stats":{"total_commits":743,"total_committers":12,"mean_commits":"61.916666666666664","dds":0.4899057873485868,"last_synced_commit":"f82314d757e4d33c7cbf7cc2c0da68d927aea277"},"previous_names":[],"tags_count":15,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/insightsengineering%2Frbmi","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/insightsengineering%2Frbmi/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/insightsengineering%2Frbmi/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/insightsengineering%2Frbmi/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/insightsengineering","download_url":"https://codeload.github.com/insightsengineering/rbmi/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248613538,"owners_count":21133530,"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":"2024-11-07T15:10:20.529Z","updated_at":"2025-04-12T18:31:46.928Z","avatar_url":"https://github.com/insightsengineering.png","language":"R","funding_links":["https://pharmaverse.org"],"categories":[],"sub_categories":[],"readme":"\u003c!-- badges: start --\u003e\n[![CRAN\nstatus](https://www.r-pkg.org/badges/version/rbmi)](https://cran.r-project.org/package=rbmi)\n[![R-CMD-check](https://github.com/insightsengineering/rbmi/actions/workflows/on_push.yaml/badge.svg?branch=main)](https://github.com/insightsengineering/rbmi/actions/workflows/on_push.yaml)\n\u003c!-- badges: end --\u003e\n\n# Reference Based Multiple Imputation (`rbmi`) \u003ca href='https://insightsengineering.github.io/rbmi/'\u003e\u003cimg src=\"man/figures/logo.png\" align=\"right\" height=\"139\" style=\"max-width: 100%; max-height: 139px;\"/\u003e\u003c/a  \u003e\n\n\n## Overview\n\nThe `rbmi` package is used for the imputation of missing data in clinical trials with continuous multivariate normal longitudinal outcomes. \nIt supports imputation under a missing at random (MAR) assumption, reference-based imputation methods, \nand delta adjustments (as required for sensitivity analysis such as tipping point analyses). The package implements both Bayesian and \napproximate Bayesian multiple imputation combined with Rubin's rules for inference, and frequentist conditional mean imputation combined with \n(jackknife or bootstrap) resampling. \n\n## Installation\n\nThe package can be installed directly from CRAN via:\n\n```\ninstall.packages(\"rbmi\")\n```\n\nNote that the usage of Bayesian multiple imputation requires the installation of the suggested \npackage [rstan](https://CRAN.R-project.org/package=rstan).\n```\ninstall.packages(\"rstan\")\n```\n\n## Usage\n\nThe package is designed around its 4 core functions:\n\n- `draws()` - Fits multiple imputation models\n- `impute()` - Imputes multiple datasets\n- `analyse()` - Analyses multiple datasets\n- `pool()` - Pools multiple results into a single statistic\n\nThe basic usage of these core functions is described in the quickstart vignette:\n\n```\nvignette(topic = \"quickstart\", package = \"rbmi\")\n```\n\n## Validation\n\nFor clarification on the current validation status of `rbmi` please see the FAQ vignette.\n\n\n## Support\n\nFor any help with regards to using the package or if you find a bug please create a [GitHub issue](https://github.com/insightsengineering/rbmi/issues)\n \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finsightsengineering%2Frbmi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Finsightsengineering%2Frbmi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finsightsengineering%2Frbmi/lists"}