{"id":21972576,"url":"https://github.com/florale/multilevelcoda","last_synced_at":"2025-04-28T13:13:55.871Z","repository":{"id":40619561,"uuid":"455436450","full_name":"florale/multilevelcoda","owner":"florale","description":"multilevelcoda R package for Bayesian multilevel compositional data analysis","archived":false,"fork":false,"pushed_at":"2025-03-27T08:01:42.000Z","size":237540,"stargazers_count":17,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-28T13:13:41.808Z","etag":null,"topics":["bayesian-inference","compositional-data-analysis","multilevel-models","multilevelcoda","r","r-package"],"latest_commit_sha":null,"homepage":"https://florale.github.io/multilevelcoda/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/florale.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","contributing":null,"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}},"created_at":"2022-02-04T05:47:39.000Z","updated_at":"2025-04-28T12:04:49.000Z","dependencies_parsed_at":"2024-03-10T13:30:14.039Z","dependency_job_id":"adf75b93-b732-435b-8c6a-70f94a1fb3d9","html_url":"https://github.com/florale/multilevelcoda","commit_stats":{"total_commits":232,"total_committers":4,"mean_commits":58.0,"dds":"0.12068965517241381","last_synced_commit":"69204041e1c25a02fbec4508f46120c6ec14b70e"},"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/florale%2Fmultilevelcoda","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/florale%2Fmultilevelcoda/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/florale%2Fmultilevelcoda/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/florale%2Fmultilevelcoda/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/florale","download_url":"https://codeload.github.com/florale/multilevelcoda/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251319593,"owners_count":21570428,"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":["bayesian-inference","compositional-data-analysis","multilevel-models","multilevelcoda","r","r-package"],"created_at":"2024-11-29T15:19:07.184Z","updated_at":"2025-04-28T13:13:55.841Z","avatar_url":"https://github.com/florale.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\n# multilevelcoda\n\u003c!-- badges: start --\u003e\n[![R-CMD-check](https://github.com/florale/multilevelcoda/workflows/R-CMD-check/badge.svg)](https://github.com/florale/multilevelcoda/actions)\n[![CRAN Version](https://www.r-pkg.org/badges/version/multilevelcoda)](https://cran.r-project.org/package=multilevelcoda)\n[![lifecycle](https://lifecycle.r-lib.org/articles/figures/lifecycle-experimental.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)\n\u003c!-- [![Coverage Status](https://codecov.io/gh/florale/multilevelcoda/branch/main/graphs/badge.svg?branch=main)](https://app.codecov.io/gh/florale/multilevelcoda)  --\u003e\n\u003c!-- badges: end --\u003e\n\n## Overview\n\nThis package provides functions to model compositional data in \na multilevel framework using full Bayesian inference.\nIt integrates the principes of Compositional Data Analysis (CoDA) \nand Multilevel Modelling and supports both compositional data as \nan outcome and predictors in a wide range of \ngeneralized (non-)linear multivariate multilevel models.\n\n## Installation\nTo install the latest release version from CRAN, run\n\n```r \ninstall.packages(\"multilevelcoda\")\n\n```\n\nThe current developmental version can be downloaded from github via\n\n```r\nif (!requireNamespace(\"remotes\")) {\n  install.packages(\"remotes\")\n}\nremotes::install_github(\"florale/multilevelcoda\")\n```\n\nBecause multilevelcoda is built on brms, which is based on Stan, a C++ compiler is required. \nThe program Rtools (available on https://cran.r-project.org/bin/windows/Rtools/) comes with a C++ compiler for Windows. On Mac, Xcode is required. For further instructions on how to get the compilers running, see the prerequisites section on https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started.\n\n## Resources\n\nYou can learn about the package from these vignettes:\n\n- [Introduction to Compositional Multilevel Modelling](https://florale.github.io/multilevelcoda/articles/A-introduction.html)\n- [Multilevel Models with Compositional Predictors](https://florale.github.io/multilevelcoda/articles/B-composition-MLM.html)\n- [Multilevel Models with Compositional Outcome](https://florale.github.io/multilevelcoda/articles/C-composition-MMLM.html)\n- [Compositional Substitution Multilevel Analysis](https://florale.github.io/multilevelcoda/articles/D-substitution.html)\n\n## Citing `multilevelcoda` and related software \nWhen using multilevelcoda, please cite one or more of the following publications:\n\n-   Le F., Dumuid D., Stanford T. E., Wiley J. F. (2024). \n    Bayesian multilevel compositional data analysis with the R package multilevelcoda.\n    *arXiv preprint arXiv:2411.12407*.\n-   Le, F., Stanford, T. E., Dumuid, D., \u0026 Wiley, J. F. (2024). \n    Bayesian Multilevel Compositional Data Analysis: \n    Introduction, Evaluation, and Application. \n    *arXiv preprint arXiv:2405.03985*.\n\nAs multilevelcoda depends on brms and Stan, please also consider citing:\n\n-   Bürkner P. C. (2017). brms: An R Package for Bayesian Multilevel\n    Models using Stan. *Journal of Statistical Software*. 80(1), 1-28.\n    doi.org/10.18637/jss.v080.i01\n-   Bürkner P. C. (2018). Advanced Bayesian Multilevel Modeling with the\n    R Package brms. *The R Journal*. 10(1), 395-411.\n    doi.org/10.32614/RJ-2018-017\n-   Bürkner P. C. (2021). Bayesian Item Response Modeling in R with brms\n    and Stan. *Journal of Statistical Software*, 100(5), 1-54.\n    doi.org/10.18637/jss.v100.i05\n-   Stan Development Team. YEAR. Stan Modeling Language Users Guide and\n    Reference Manual, VERSION. \u003chttps://mc-stan.org\u003e\n-   Carpenter B., Gelman A., Hoffman M. D., Lee D., Goodrich B.,\n    Betancourt M., Brubaker M., Guo J., Li P., and Riddell A. (2017).\n    Stan: A probabilistic programming language. *Journal of Statistical\n    Software*. 76(1). doi.org/10.18637/jss.v076.i01\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fflorale%2Fmultilevelcoda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fflorale%2Fmultilevelcoda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fflorale%2Fmultilevelcoda/lists"}