{"action":"created","issue":{"url":"https://api.github.com/repos/harvard-P01/causalMediation/issues/10","labels_url":"https://api.github.com/repos/harvard-P01/causalMediation/issues/10/labels{/name}","comments_url":"https://api.github.com/repos/harvard-P01/causalMediation/issues/10/comments","events_url":"https://api.github.com/repos/harvard-P01/causalMediation/issues/10/events","html_url":"https://github.com/harvard-P01/causalMediation/issues/10","id":118219684,"number":10,"title":"Add proportion mediated ","user":{"login":"cchoirat","id":4173925,"avatar_url":"https://avatars.githubusercontent.com/u/4173925?v=3","gravatar_id":"","url":"https://api.github.com/users/cchoirat","html_url":"https://github.com/cchoirat","followers_url":"https://api.github.com/users/cchoirat/followers","following_url":"https://api.github.com/users/cchoirat/following{/other_user}","gists_url":"https://api.github.com/users/cchoirat/gists{/gist_id}","starred_url":"https://api.github.com/users/cchoirat/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cchoirat/subscriptions","organizations_url":"https://api.github.com/users/cchoirat/orgs","repos_url":"https://api.github.com/users/cchoirat/repos","events_url":"https://api.github.com/users/cchoirat/events{/privacy}","received_events_url":"https://api.github.com/users/cchoirat/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":3,"created_at":"2015-11-21T20:43:03Z","updated_at":"2016-01-21T16:13:13Z","closed_at":null,"body":""},"comment":{"url":"https://api.github.com/repos/harvard-P01/causalMediation/issues/comments/173620467","html_url":"https://github.com/harvard-P01/causalMediation/issues/10#issuecomment-173620467","issue_url":"https://api.github.com/repos/harvard-P01/causalMediation/issues/10","id":173620467,"user":{"login":"eggevdp","id":5628349,"avatar_url":"https://avatars.githubusercontent.com/u/5628349?v=3","gravatar_id":"","url":"https://api.github.com/users/eggevdp","html_url":"https://github.com/eggevdp","followers_url":"https://api.github.com/users/eggevdp/followers","following_url":"https://api.github.com/users/eggevdp/following{/other_user}","gists_url":"https://api.github.com/users/eggevdp/gists{/gist_id}","starred_url":"https://api.github.com/users/eggevdp/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/eggevdp/subscriptions","organizations_url":"https://api.github.com/users/eggevdp/orgs","repos_url":"https://api.github.com/users/eggevdp/repos","events_url":"https://api.github.com/users/eggevdp/events{/privacy}","received_events_url":"https://api.github.com/users/eggevdp/received_events","type":"User","site_admin":false},"created_at":"2016-01-21T16:13:13Z","updated_at":"2016-01-21T16:13:13Z","body":"Linda:\r\n\"\r\nYes, I’d like the macro to output PM with standard error and confidence interval.\r\nFor the delta method we can save the standard errors of NIE and NDE that we previously compute and then the delta method is very easy to implement. delta method (~x1/(x1+x2), estmean, estsigma)\r\n\""}}