{"id":32202607,"url":"https://github.com/xsswang/remiod","last_synced_at":"2026-02-19T23:01:11.906Z","repository":{"id":56933460,"uuid":"466250614","full_name":"xsswang/remiod","owner":"xsswang","description":"R package for controlled multiple imputation of ordinal or binary responses with missing data in clinical study","archived":false,"fork":false,"pushed_at":"2023-02-18T18:39:50.000Z","size":4719,"stargazers_count":1,"open_issues_count":2,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-01-12T06:23:21.262Z","etag":null,"topics":["bayesian","control-based","copy-reference","delta-adjustment","generalized-linear-models","glm","jags","jump-to-reference","mcmc","missing-at-random","missing-data","missing-not-at-random","multiple-imputation","non-ignorable","ordinal-regression","pattern-mixture-model","r-package","reference-based","statistics"],"latest_commit_sha":null,"homepage":"https://xsswang.github.io/remiod","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/xsswang.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2022-03-04T19:44:04.000Z","updated_at":"2025-12-29T00:20:07.000Z","dependencies_parsed_at":"2023-11-12T16:15:02.814Z","dependency_job_id":"c77c26ae-b579-4af9-8d3d-bcec56057339","html_url":"https://github.com/xsswang/remiod","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/xsswang/remiod","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xsswang%2Fremiod","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xsswang%2Fremiod/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xsswang%2Fremiod/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xsswang%2Fremiod/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/xsswang","download_url":"https://codeload.github.com/xsswang/remiod/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xsswang%2Fremiod/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29636035,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-19T22:32:43.237Z","status":"ssl_error","status_checked_at":"2026-02-19T22:32:38.330Z","response_time":117,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["bayesian","control-based","copy-reference","delta-adjustment","generalized-linear-models","glm","jags","jump-to-reference","mcmc","missing-at-random","missing-data","missing-not-at-random","multiple-imputation","non-ignorable","ordinal-regression","pattern-mixture-model","r-package","reference-based","statistics"],"created_at":"2025-10-22T04:04:57.754Z","updated_at":"2026-02-19T23:01:11.896Z","avatar_url":"https://github.com/xsswang.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\nlink-citation: yes\npkgdown:\n   as_is:true\nreferences:\n- id: tang2017\n  title: \"Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout\"\n  author:\n  - family: Tang\n    given: Y\n  container-title: Statistics in Medicine\n  volume: 37\n  URL: 'https://dx.doi.org/10.1002/sim.7583'\n  DOI: 10.1002/sim.7583\n  issue: 9\n  page: 1467 -- 81\n  type: article-journal\n  issued:\n    year: 2018\n- id: Erler2021\n  title: \"JointAI: Joint Analysis and Imputation of Incomplete Data in R\"\n  author:\n  - family: Erler\n    given: NS\n  - family: Rizopoulos\n    given: D\n  - family: Lesaffre\n    given: EMEH\n  container-title: Journal of Statistical Software\n  volume: 100\n  URL: 'https://dx.doi.org/10.18637/jss.v100.i20'\n  DOI: 10.18637/jss.v100.i20\n  issue: 20\n  page: 1 -- 56\n  type: article-journal\n  issued:\n    year: 2021\n- id: wang2022\n  title: \"Remiod: Reference-based Controlled Multiple Imputation of Longitudinal Binary and Ordinal Outcomes with non-ignorable missingness\"\n  author:\n  - family: Wang\n    given: T\n  - family: Liu\n    given: Y\n  container-title: arXiv\n  volume: 2203.02771\n  URL: 'https://arxiv.org/pdf/2203.02771'\n  type: article-journal\n  issued:\n    year: 2022    \n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  fig.align = 'center'\n)\n```\n\n# \u003cspan style=\"color: blue;\"\u003eremiod\u003c/span\u003e: Reference-based Controlled Multiple Imputation of Longitudinal Binary and Ordinal Outcomes with non-ignorable missingness \n\n\u003c!-- badges: start --\u003e\n[![CRAN Status](https://www.r-pkg.org/badges/version/remiod)](https://CRAN.R-project.org/package=remiod)\n[![CRAN Downloads](https://cranlogs.r-pkg.org/badges/remiod)](https://cran.r-project.org/package=remiod)\n[![GPL-3.0](https://img.shields.io/github/license/xsswang/remiod?logo=GNU\u0026logoColor=FFFFFF\u0026style=flat-square)](https://github.com/xsswang/remiod/main/LICENSE)\n[![R build\nstatus](https://github.com/xsswang/remiod/workflows/R-CMD-check/badge.svg)](https://github.com/xsswang/remiod/actions)\n\u003c!-- badges: end --\u003e\n\nThe package **remiod** provides functionality to perform controlled multiple\nimputation of binary and ordinal response in the Bayesian framework. Implemented are\n(generalized) linear regression models for binary data and cumulative logistic models for\nordered categorical data [@wang2022]. It is also possible to fit multiple models of mixed types\nsimultaneously. Missing values in (if present) will be imputed automatically.\n\n**remiod** has two algorithmic backend. One is [JAGS](https://mcmc-jags.sourceforge.io/), with which the function performs some preprocessing of the data and creates a JAGS model, which will then automatically be\npassed to [JAGS](https://mcmc-jags.sourceforge.io/) with the help of the R package [**rjags**](https://CRAN.R-project.org/package=rjags). The another is based on the method proposed by Tang [@tang2017].\n\nBesides the main modelling functions, **remiod** also provides functions to summarize and visualize results.\n\n    \n## Installation\n\n**remiod** Can be from [CRAN](https://cran.r-project.org/web/packages/remiod/index.html):\n```{r cran-install, eval = FALSE}\ninstall.packages(\"remiod\")\n```\nOr, it can be installed from GitHub:\n```{r gh-installation, eval = FALSE}\n# install.packages(\"remotes\")\nremotes::install_github(\"xsswang/remiod\")\n```\n\n\n## Main functions\n**remiod** provides the following main functions:\n\n``` r\nremiod                      #processing data and implementing MCMC sampling\nextract_MIdata              #extract imputed data sets\nmiAnalyze                   #Perform analyses using imputed data and pool results\n```\n\nCurrently, methods **remiod** implements include  missing at random (`MAR`), jump-to-reference (`J2R`), copy reference (`CR`), and delta adjustment (`delta`). For `method = \"delta\"`, argument `delta` should follow to specify a numerical values used in delta adjustment. These methods can be requested through `extract_MIdata()`, and imputed datasets can be analyzed using `miAnalyze()`.\n\nFunctions `summary()`, `coef()`, and `mcmcplot()` provide a summary of the posterior distribution under MAR and its visualization.\n\n\n\n\n## Minimal Example\n\n```{r, eval = FALSE}\n\ndata(schizow)\n\ntest = remiod(formula = y6 ~ tx + y0 + y1 + y3, data = schizow,\n              trtvar = 'tx', algorithm = 'jags', method=\"MAR\",\n              ord_cov_dummy = FALSE, n.adapt = 10, n.chains = 1,\n              n.iter = 100, thin = 2, warn = FALSE, seed = 1234)\n\nextdt = extract_MIdata(object=test, method=\"J2R\",mi.setting=NULL, M=10, minspace=2)\nresult = miAnalyze(y6 ~ y1 + tx, data = extdt, pool = TRUE)\n\n```\n\n## Support\nFor any help with regards to using the package or if you find a bug please create a [GitHub issue](https://github.com/xsswang/remiod/issues).\n\n## Reference\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxsswang%2Fremiod","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fxsswang%2Fremiod","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxsswang%2Fremiod/lists"}