{"id":21625720,"url":"https://github.com/dcousin3/anopa","last_synced_at":"2025-08-01T11:34:35.512Z","repository":{"id":228107497,"uuid":"773175471","full_name":"dcousin3/ANOPA","owner":"dcousin3","description":"Analysis of proportions using Anscombe transform","archived":false,"fork":false,"pushed_at":"2025-07-18T11:38:36.000Z","size":9862,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-28T10:38:17.786Z","etag":null,"topics":["error-bars","proportions","r","statistical-testing","statistics","summary-statistics"],"latest_commit_sha":null,"homepage":"https://dcousin3.github.io/ANOPA/","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/dcousin3.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,"zenodo":null}},"created_at":"2024-03-16T23:56:47.000Z","updated_at":"2025-07-18T11:38:41.000Z","dependencies_parsed_at":"2024-03-19T22:32:57.118Z","dependency_job_id":"adf18e2e-ccee-40fe-bc46-02ead8f8a370","html_url":"https://github.com/dcousin3/ANOPA","commit_stats":null,"previous_names":["dcousin3/anopa"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dcousin3/ANOPA","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dcousin3%2FANOPA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dcousin3%2FANOPA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dcousin3%2FANOPA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dcousin3%2FANOPA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dcousin3","download_url":"https://codeload.github.com/dcousin3/ANOPA/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dcousin3%2FANOPA/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268215611,"owners_count":24214365,"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","status":"online","status_checked_at":"2025-08-01T02:00:08.611Z","response_time":67,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["error-bars","proportions","r","statistical-testing","statistics","summary-statistics"],"created_at":"2024-11-25T01:10:24.134Z","updated_at":"2025-08-01T11:34:35.491Z","avatar_url":"https://github.com/dcousin3.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\nbibliography: \"inst/REFERENCES.bib\"\ncsl: \"inst/apa-6th.csl\"\n---\n\n# ANOPA: Analysis of Proportions using Anscombe transform\n\n\u003c!-- badges: start --\u003e\n[![CRAN Status](https://www.r-pkg.org/badges/version/ANOPA)](https://cran.r-project.org/package=ANOPA)\n\u003c!-- badges: end --\u003e\n\n```{r, echo = FALSE, message = FALSE, results = 'hide', warning = FALSE}\ncat(\"this will be hidden; used for general initializations.\\n\")\nlibrary(ANOPA)\noptions(\"ANOPA.feedback\" = \"none\") # shut down all information\n```\n\nThe library `ANOPA` provides easy-to-use tools to analyze proportions . \nWith it, you can examine\nif proportions are significantly different (_show an effect_). In the\ncase where there is more than one factor, you can also test if the\ninteraction(s) are significant. You \ncan also test simple effects (a.k.a. _expected marginal_ analysis),\nas well as post-hoc tests (using Tukey's _Honestly Significant Difference_ test HSD). \nFinally, you can assess differences based on orthogonal contrasts.\nYou can consult @lc23 for details.\n\nANOPA also comes (a) with tools to make a plot of the proportions along\nwith 95% confidence intervals [these intervals are adjusted for pair-\nwise comparisons; @cgh21]; (b) with tools to compute statistical power given\nsome _a priori_ expected proportions or sample size to reach a certain\nstatistical power; (c) to generate random proportions if you wish to perform\nMonte Carlo simulations on proportions. \nIn sum, everything you need to analyse proportions!\n\nThe main function is `anopa()` which returns an omnibus analysis of the \nproportions for the factors given. For example, if you have a data frame\n`ArticleExample2` which contains a column called `s` where the \nnumber of successes per group are stored,\nand a column called `n` where the group sizes are stored, then the following\nperforms an analysis of proportions as a function of the groups based on the\ncolumns `SES` and `MofDiagnostic`:\n\n```{r, message=FALSE, warning=FALSE, echo=TRUE, eval=TRUE}\nw \u003c- anopa( {s; n} ~ SES * MofDiagnostic, ArticleExample2 )\nsummary(w)\n```\n\nAs the results suggest (consult the first three columns), there is\na main effect of the factor SES (F(2, inf) = 6.395, p = .002).\nA plot of the proportions can be obtained easily with \n\n```{r, fig.alt=\"anopa plot of proportions with confidence intervals\", message=FALSE, warning=FALSE, fig.width=5.25, fig.height=3}\nanopaPlot(w) \n```\n\nor just the main effect figure with \n\n```{r, fig.alt = \"anopa plot of proportions with confidence intervals\", message=FALSE, warning=FALSE, fig.width=4, fig.height=3}\nanopaPlot(w, ~ SES)\n```\n\nIf the interaction had been significant, simple effects can be analyzed from the _expected marginal\nfrequencies_ with `e \u003c- emProportions(w, ~ SES | MofDiagnostic )`.\n\nFollow-up analyses include contrasts examinations with `contrastProportions()`; finally,\npost-hoc pairwise comparisons can be obtained with `posthocProportions()`.\n\n\nPrior to running an experiment, you might consider some statistical power planning \non proportions using ``anopaPower2N()`` or\n``anopaN2Power()`` as long as you can anticipate the expected proportions. A \nconvenient effect size, the f-square and eta-square can be obtained with `anopaProp2fsq()`.\n\nFinally, `toCompiled()`, `toLong()` and `toWide()` \ncan be used to present the proportion in other formats.\n\n# Installation\n\nThe official **CRAN** version can be installed with \n\n```{r, echo = TRUE, eval = FALSE}\ninstall.packages(\"ANOPA\")\nlibrary(ANOPA)\n```\n\nThe development version `r packageVersion(\"ANOPA\")` can be accessed through GitHub:\n\n```{r, echo = TRUE, eval = FALSE}\ndevtools::install_github(\"dcousin3/ANOPA\")\nlibrary(ANOPA)\n```\n\nNote that the package `ANOPA` is named using UPPERCASE letters whereas the main function \n`anopa()` is written using lowercase letters.\n\nThe library is loaded with \n\n```{r, echo = TRUE, eval = FALSE, results = FALSE}\nlibrary(ANOPA)\n```\n\n# In sum\n\nAs seen, the library `ANOPA` makes it easy to analyze proportions using the\nsame general vocabulary found in ANOVAs.\n\nThe complete documentation is available on this \n[site](https://dcousin3.github.io/ANOPA/).\n\nA general introduction to the `ANOPA` framework underlying this \nlibrary can be found at @lc23.\n\n# References\n\n\\insertAllCited{}\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdcousin3%2Fanopa","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdcousin3%2Fanopa","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdcousin3%2Fanopa/lists"}