{"action":"created","issue":{"url":"https://api.github.com/repos/easystats/performance/issues/31","repository_url":"https://api.github.com/repos/easystats/performance","labels_url":"https://api.github.com/repos/easystats/performance/issues/31/labels{/name}","comments_url":"https://api.github.com/repos/easystats/performance/issues/31/comments","events_url":"https://api.github.com/repos/easystats/performance/issues/31/events","html_url":"https://github.com/easystats/performance/issues/31","id":426930645,"node_id":"MDU6SXNzdWU0MjY5MzA2NDU=","number":31,"title":"check_assumptions()","user":{"login":"strengejacke","id":26301769,"node_id":"MDQ6VXNlcjI2MzAxNzY5","avatar_url":"https://avatars3.githubusercontent.com/u/26301769?v=4","gravatar_id":"","url":"https://api.github.com/users/strengejacke","html_url":"https://github.com/strengejacke","followers_url":"https://api.github.com/users/strengejacke/followers","following_url":"https://api.github.com/users/strengejacke/following{/other_user}","gists_url":"https://api.github.com/users/strengejacke/gists{/gist_id}","starred_url":"https://api.github.com/users/strengejacke/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/strengejacke/subscriptions","organizations_url":"https://api.github.com/users/strengejacke/orgs","repos_url":"https://api.github.com/users/strengejacke/repos","events_url":"https://api.github.com/users/strengejacke/events{/privacy}","received_events_url":"https://api.github.com/users/strengejacke/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":4,"created_at":"2019-03-29T10:57:53Z","updated_at":"2019-06-03T10:17:09Z","closed_at":null,"author_association":"MEMBER","body":"Maybe we should think of a new \"class\" of functions, prefixed with `check_*`, made for... checking stuff for our models. This could be a way of regrouping them together and making them more discoverable. They would become `check_overdispersion()`, `check_zeroinflation()` etc.\r\n\r\nBased on recent questions that I had from students, other possible functions (not necesserily implemented in `performance`) come to my mind, such as `check_normality` and `check_homogeneity` (of variances), and other functions that would facilitates the checks to see if it's suited for parametric analyses... which would be very useful for students.\r\n\r\n_Originally posted by @DominiqueMakowski in https://github.com/easystats/performance/pull/24#issuecomment-473315082_"},"comment":{"url":"https://api.github.com/repos/easystats/performance/issues/comments/498199114","html_url":"https://github.com/easystats/performance/issues/31#issuecomment-498199114","issue_url":"https://api.github.com/repos/easystats/performance/issues/31","id":498199114,"node_id":"MDEyOklzc3VlQ29tbWVudDQ5ODE5OTExNA==","user":{"login":"DominiqueMakowski","id":8875533,"node_id":"MDQ6VXNlcjg4NzU1MzM=","avatar_url":"https://avatars2.githubusercontent.com/u/8875533?v=4","gravatar_id":"","url":"https://api.github.com/users/DominiqueMakowski","html_url":"https://github.com/DominiqueMakowski","followers_url":"https://api.github.com/users/DominiqueMakowski/followers","following_url":"https://api.github.com/users/DominiqueMakowski/following{/other_user}","gists_url":"https://api.github.com/users/DominiqueMakowski/gists{/gist_id}","starred_url":"https://api.github.com/users/DominiqueMakowski/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/DominiqueMakowski/subscriptions","organizations_url":"https://api.github.com/users/DominiqueMakowski/orgs","repos_url":"https://api.github.com/users/DominiqueMakowski/repos","events_url":"https://api.github.com/users/DominiqueMakowski/events{/privacy}","received_events_url":"https://api.github.com/users/DominiqueMakowski/received_events","type":"User","site_admin":false},"created_at":"2019-06-03T10:17:09Z","updated_at":"2019-06-03T10:17:09Z","author_association":"MEMBER","body":"That's awesome. I would maybe emphasise the \"bad\" results, either by colour or by something like \"warning: ...\".\r\n\r\nFor instance:\r\n\r\n- `Residuals are normally distributed (p = %.3f).` -> `OK: The residuals appear as normally distributed (p = %.3f).` \r\n- `Non-normality of residuals detected (p = %.3f).` -> `Warning: Non-normality of residuals detected (p = %.3f).` \r\n\r\n"}}
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