{"id":15716422,"url":"https://github.com/thomasgladwin/teg_rma","last_synced_at":"2026-01-21T17:31:33.021Z","repository":{"id":57473815,"uuid":"97117781","full_name":"thomasgladwin/teg_RMA","owner":"thomasgladwin","description":"General framework for organizing data for N-way repeated measures analyses in Matlab (and partly Python), including an implementation of repeated measures ANOVA","archived":false,"fork":false,"pushed_at":"2020-06-07T22:10:41.000Z","size":198,"stargazers_count":1,"open_issues_count":2,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-19T14:16:41.990Z","etag":null,"topics":["anova","matlab","measures","n-way","python","repeated","repeated-measures-anova","statistics"],"latest_commit_sha":null,"homepage":"","language":"Python","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/thomasgladwin.png","metadata":{"files":{"readme":"README.md","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}},"created_at":"2017-07-13T11:59:52.000Z","updated_at":"2021-04-13T15:21:49.000Z","dependencies_parsed_at":"2022-09-26T17:40:58.970Z","dependency_job_id":null,"html_url":"https://github.com/thomasgladwin/teg_RMA","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thomasgladwin%2Fteg_RMA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thomasgladwin%2Fteg_RMA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thomasgladwin%2Fteg_RMA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thomasgladwin%2Fteg_RMA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/thomasgladwin","download_url":"https://codeload.github.com/thomasgladwin/teg_RMA/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247612625,"owners_count":20966787,"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":["anova","matlab","measures","n-way","python","repeated","repeated-measures-anova","statistics"],"created_at":"2024-10-03T21:45:36.147Z","updated_at":"2026-01-21T17:31:32.993Z","avatar_url":"https://github.com/thomasgladwin.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# teg_RMA\n\nRepeated measures ANOVA in Matlab and (work in progress, with fewer bells and whistles) Python.\n\nFor use in Python: pip install tegstats (or use the tegstats_local.py file), and see example_RMA.py for usage. The Python version so far only does pure within-subject analyses and within x between-factor interactions (with an arbitrary number of factors and levels per factor), and has the option to use randomization tests (hope you're not in a hurry though). The package includes the multiple and hierarchical regression function teg_regression (see example_regression.py for usage).\n\nFor Matlab, the basic usage is: O = teg_RMA(M, levels, varnames)\n\nwith M an observation x nested variable-combinations matrix; levels a vector with the number of levels per variable (from highest to lowest level of nesting); and varnames a cell array of strings.\n\nInteractions are explored by recursively testing the lower-level effects per level of the final factor.\n\nCategorical and continuous between-subject factors can be added. Note that in this implementation, tests are performed per effect separately. So, including a between-subject factor does not affect within-subject tests. Be aware that this differs from the tests in SPSS.\n\nThe F-tests for significant effects are reported, together with other statistical measures including partial eta squared estimates of effect size. Various descriptive statistics are given. Note that the p-values printed with means reflects the difference of the raw scores from zero when there is no within-subject factor, and the differences of the scores after subtraction of the subject-mean from zero when there is a within-subject factor.\n\nSet perm_test = 1 in the settings variables at the top of teg_RMA to use randomization and permutation testing to determine p-values (slow but nicely avoids assumptions); set perm_test = 0 otherwise. Set nIts_perm to adjust the number of iterations. \n\nTo use the program, you need to download the latest teg_RMA.zip, teg_basic_stats.zip and teg_basic_funcs.zip, unpack them to their own directories, and add them all to the Matlab path.\n\nPlease cite as:\nThomas Edward Gladwin (2020). An implementation of repeated measures ANOVA: effect coding, automated exploration of interactions, and randomization testing. MethodsX, doi:10.1016/j.mex.2020.100947. https://www.sciencedirect.com/science/article/pii/S2215016120301679?via%3Dihub.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthomasgladwin%2Fteg_rma","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthomasgladwin%2Fteg_rma","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthomasgladwin%2Fteg_rma/lists"}