{"action":"created","comment":{"url":"https://api.github.com/repos/statsmodels/statsmodels/pulls/comments/189603550","pull_request_review_id":121814545,"id":189603550,"diff_hunk":"@@ -510,6 +510,164 @@ def hess(params, *args):\n         mlefit.mle_settings = optim_settings\n         return mlefit\n \n+    def _fit_zeros(self, keep_index=None, start_params=None,\n+                   return_auxiliary=False, k_params=None, **fit_kwds):\n+        \"\"\"experimental fit the model subject to zero constraints\n+\n+        Intended for internal use cases until we know what we need.\n+        API will need to change to handle models with two exog.\n+        This is not yet supported by all model subclasses.\n+\n+        This is essentially a simplified version of `fit_constrained`, and\n+        does not need to use `offset`.\n+\n+        The estimation creates a new model with transformed design matrix,\n+        exog, and converts the results back to the original parameterization.\n+\n+        Some subclasses could use a more efficient calculation than using a\n+        new model.\n+\n+        Parameters\n+        ----------\n+        keep_index : array_like (int or bool) or slice\n+            variables that should be dropped.\n+        start_params : None or array_like\n+            starting values for the optimization. `start_params` needs to be\n+            given in the original parameter space and are internally\n+            transformed.\n+        k_params : int or None\n+            If None, then we try to infer from start_params or model.\n+        **fit_kwds : keyword arguments\n+            fit_kwds are used in the optimization of the transformed model.\n+\n+        Returns\n+        -------\n+        results : Results instance\n+\n+        \"\"\"\n+        # we need to append index of extra params to keep_index as in\n+        # NegativeBinomial\n+        if hasattr(self, 'k_extra') and self.k_extra > 0:\n+            # we cannot change the original, TODO: should we add keep_index_params?\n+            keep_index = np.array(keep_index, copy=True)\n+            k = self.exog.shape[1]\n+            extra_index = np.arange(k, k + self.k_extra)\n+            keep_index_p = np.concatenate((keep_index, extra_index))\n+        else:\n+            keep_index_p = keep_index\n+\n+        # not all models support start_params, drop if None, hide them in fit_kwds\n+        if start_params is not None:\n+            fit_kwds['start_params'] = start_params[keep_index_p]\n+            k_params = len(start_params)\n+            # ignore k_params in this case, or verify consisteny?\n+\n+        # build auxiliary model and fit\n+        init_kwds = self._get_init_kwds()\n+        mod_constr = self.__class__(self.endog, self.exog[:, keep_index],\n+                                    **init_kwds)\n+        res_constr = mod_constr.fit(**fit_kwds)\n+        # switch name, only need keep_index for params below\n+        keep_index = keep_index_p\n+\n+        if k_params is None:\n+            k_params = self.exog.shape[1]\n+            k_params += getattr(self, 'k_extra', 0)\n+\n+        params_full = np.zeros(k_params)\n+        params_full[keep_index] = res_constr.params\n+\n+        # create dummy results Instance, TODO: wire up properly\n+        # TODO: this could be moved into separate private method if needed\n+        # discrete L1 fit_regularized doens't reestimate AFAICS\n+        # RLM doesn't have method, disp nor warn_convergence keywords\n+        # OLS, WLS swallows extra kwds with **kwargs, but doesn't have method='nm'\n+        try:\n+            # Note: addding full_output=False causes exceptions\n+            res = self.fit(maxiter=0, disp=0, method='nm', skip_hessian=True,\n+                           warn_convergence=False, start_params=params_full)\n+            # we get a wrapper back\n+        except (TypeError, ValueError):\n+            res = self.fit()\n+\n+        # Warning: make sure we are not just changing the wrapper instead of\n+        # results #2400\n+        if hasattr(res_constr.model, 'scale'):\n+            # GLM problem, see #2399\n+            res.model.scale = res._results.scale = res_constr.model.scale","path":"statsmodels/base/model.py","position":88,"original_position":88,"commit_id":"eed924ba67ea90e5a9027d8ca849741aa9213a90","original_commit_id":"eed924ba67ea90e5a9027d8ca849741aa9213a90","user":{"login":"josef-pkt","id":440735,"avatar_url":"https://avatars1.githubusercontent.com/u/440735?v=4","gravatar_id":"","url":"https://api.github.com/users/josef-pkt","html_url":"https://github.com/josef-pkt","followers_url":"https://api.github.com/users/josef-pkt/followers","following_url":"https://api.github.com/users/josef-pkt/following{/other_user}","gists_url":"https://api.github.com/users/josef-pkt/gists{/gist_id}","starred_url":"https://api.github.com/users/josef-pkt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/josef-pkt/subscriptions","organizations_url":"https://api.github.com/users/josef-pkt/orgs","repos_url":"https://api.github.com/users/josef-pkt/repos","events_url":"https://api.github.com/users/josef-pkt/events{/privacy}","received_events_url":"https://api.github.com/users/josef-pkt/received_events","type":"User","site_admin":false},"body":"> Is res.model not self?\r\n\r\nYes, but I hadn't realized it, I leave it but added a code comment\r\n\r\nGLM is in 2399 cited on the line above","created_at":"2018-05-21T14:21:40Z","updated_at":"2018-05-21T14:21:40Z","html_url":"https://github.com/statsmodels/statsmodels/pull/4576#discussion_r189603550","pull_request_url":"https://api.github.com/repos/statsmodels/statsmodels/pulls/4576","author_association":"MEMBER","_links":{"self":{"href":"https://api.github.com/repos/statsmodels/statsmodels/pulls/comments/189603550"},"html":{"href":"https://github.com/statsmodels/statsmodels/pull/4576#discussion_r189603550"},"pull_request":{"href":"https://api.github.com/repos/statsmodels/statsmodels/pulls/4576"}},"in_reply_to_id":189478300},"pull_request":{"url":"https://api.github.com/repos/statsmodels/statsmodels/pulls/4576","id":185004246,"html_url":"https://github.com/statsmodels/statsmodels/pull/4576","diff_url":"https://github.com/statsmodels/statsmodels/pull/4576.diff","patch_url":"https://github.com/statsmodels/statsmodels/pull/4576.patch","issue_url":"https://api.github.com/repos/statsmodels/statsmodels/issues/4576","number":4576,"state":"open","locked":false,"title":"Penalized mle scad rebased2","user":{"login":"josef-pkt","id":440735,"avatar_url":"https://avatars1.githubusercontent.com/u/440735?v=4","gravatar_id":"","url":"https://api.github.com/users/josef-pkt","html_url":"https://github.com/josef-pkt","followers_url":"https://api.github.com/users/josef-pkt/followers","following_url":"https://api.github.com/users/josef-pkt/following{/other_user}","gists_url":"https://api.github.com/users/josef-pkt/gists{/gist_id}","starred_url":"https://api.github.com/users/josef-pkt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/josef-pkt/subscriptions","organizations_url":"https://api.github.com/users/josef-pkt/orgs","repos_url":"https://api.github.com/users/josef-pkt/repos","events_url":"https://api.github.com/users/josef-pkt/events{/privacy}","received_events_url":"https://api.github.com/users/josef-pkt/received_events","type":"User","site_admin":false},"body":"another rebase, rebased #3677, which is a rebase of #2374 \r\nnew PR because an error when running the test might be a incorrect merge conflict resolution.\r\n\r\nI haven't looked at any details yet, I just want to see how \"bad\" it is.\r\n\r\nsome of the generic results creation code in this branch might be outdated by now, or not my preferred design anymore\r\ni.e. #2393 still has no clean solution, AFAIK\r\n\r\n## Design issues\r\n\r\n**transformed params** might need a more closer look (e.g. NegativeBinomial)  \r\nI fixed it in the unit test, which is mostly checking internal structure, i.e. which start_params was used.\r\nBut I don't know if any code outside on unit tests will need to handle parameters that are transformed during the optimization.\r\n\r\n**possible source of bug:**\r\nif score is computed as sum(score_obs) in the super model class, then we might be adding the penality twice, once in score_obs (called on the final class) and once score (when final class calls super().score)\r\nGLM uses sum(score_obs) after refactoring in 0.9\r\n\r\nThis looks like a serious problem now, and I don't see a clean way of working around it.\r\nWe would need a flag or keyword to tell whether we should add the penalty in score_obs or not.\r\nHowever, a keyword in score_obs with a default like penal=None does not work, because we want no penal if score_obs is called from score, and we need penalization if score_obs is called to create cov_params with robust cov_type.\r\n\r\nThe same might happen if loglike/loglike_obs is used. \r\n\r\nShort term workaround: score is NOT allowed to use score_obs, e.g. use extra helper method `_score_obs` in GLM that is called by both `score` and `score_obs`\r\nDONE this in GLM\r\n\r\n**inheritance and tsa**\r\nThe new methods, `_fit_zeros` and `_fit_collinear` are in `base.LikelihoodModel` and so they will also be inherited by tsa models which might not make much sense, and most likely will not work anyway.\r\n-> maybe use a pure Mixin class without method overlap for those extra methods and add to top level subclasses like DiscreteModel and GLM, and RLM, and GenericLikelihoodModel... Or, create a new intermediate class for non-tsa likelihood models.\r\n\r\n**df_effective, aic, bic**\r\nIn order to be able to use information criteria like aic, we need effective degrees of freedom, or effective variable count. I don't know how we should define this for general MLE.\r\nI don't remember the details: For Gauss-Newton Regression we can get the linear model analog which might have just the hessian, e.g. something like `(score_obs * (hessian dot score_obs.T).T).sum(1)` in analogy to the hat matrix and its trace.\r\n\r\n**standardization versus weights**\r\nOften exog are standardized or rescaled to make the relative weights less scale dependent. This is up to the user, but rescaled exog will not have nice interpretable params. For that case we could also to the rescaling internally and backtransform the result, as in `fit_transformed` idea. Alternatively, we can choose weights proportional to exog.std(0) in the univariate case, but full prior covariance matrix (arbitrary linear restrictions)  needs to be defined by the user or by statsmodels for built-in shrinkage targets, e.g. spline penalization or penalization of categorical exog.","created_at":"2018-04-30T16:36:48Z","updated_at":"2018-05-21T14:21:40Z","closed_at":null,"merged_at":null,"merge_commit_sha":"a3ebbc4e3275c9bcb70df5a95224c64a68565a7c","assignee":null,"assignees":[],"requested_reviewers":[],"requested_teams":[],"labels":[{"id":142970,"url":"https://api.github.com/repos/statsmodels/statsmodels/labels/comp-base","name":"comp-base","color":"ededed","default":false},{"id":448970997,"url":"https://api.github.com/repos/statsmodels/statsmodels/labels/topic-penalization","name":"topic-penalization","color":"b1e7cc","default":false},{"id":142961,"url":"https://api.github.com/repos/statsmodels/statsmodels/labels/type-enh","name":"type-enh","color":"99ffff","default":false}],"milestone":{"url":"https://api.github.com/repos/statsmodels/statsmodels/milestones/11","html_url":"https://github.com/statsmodels/statsmodels/milestone/11","labels_url":"https://api.github.com/repos/statsmodels/statsmodels/milestones/11/labels","id":2546431,"number":11,"title":"0.10","description":"currently for issues, PR that will not be available or ready for 0.9","creator":{"login":"josef-pkt","id":440735,"avatar_url":"https://avatars1.githubusercontent.com/u/440735?v=4","gravatar_id":"","url":"https://api.github.com/users/josef-pkt","html_url":"https://github.com/josef-pkt","followers_url":"https://api.github.com/users/josef-pkt/followers","following_url":"https://api.github.com/users/josef-pkt/following{/other_user}","gists_url":"https://api.github.com/users/josef-pkt/gists{/gist_id}","starred_url":"https://api.github.com/users/josef-pkt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/josef-pkt/subscriptions","organizations_url":"https://api.github.com/users/josef-pkt/orgs","repos_url":"https://api.github.com/users/josef-pkt/repos","events_url":"https://api.github.com/users/josef-pkt/events{/privacy}","received_events_url":"https://api.github.com/users/josef-pkt/received_events","type":"User","site_admin":false},"open_issues":229,"closed_issues":19,"state":"open","created_at":"2017-05-27T18:40:33Z","updated_at":"2018-05-21T13:33:56Z","due_on":null,"closed_at":null},"commits_url":"https://api.github.com/repos/statsmodels/statsmodels/pulls/4576/commits","review_comments_url":"https://api.github.com/repos/statsmodels/statsmodels/pulls/4576/comments","review_comment_url":"https://api.github.com/repos/statsmodels/statsmodels/pulls/comments{/number}","comments_url":"https://api.github.com/repos/statsmodels/statsmodels/issues/4576/comments","statuses_url":"https://api.github.com/repos/statsmodels/statsmodels/statuses/eed924ba67ea90e5a9027d8ca849741aa9213a90","head":{"label":"josef-pkt:penalized_mle_scad_rebased2","ref":"penalized_mle_scad_rebased2","sha":"eed924ba67ea90e5a9027d8ca849741aa9213a90","user":{"login":"josef-pkt","id":440735,"avatar_url":"https://avatars1.githubusercontent.com/u/440735?v=4","gravatar_id":"","url":"https://api.github.com/users/josef-pkt","html_url":"https://github.com/josef-pkt","followers_url":"https://api.github.com/users/josef-pkt/followers","following_url":"https://api.github.com/users/josef-pkt/following{/other_user}","gists_url":"https://api.github.com/users/josef-pkt/gists{/gist_id}","starred_url":"https://api.github.com/users/josef-pkt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/josef-pkt/subscriptions","organizations_url":"https://api.github.com/users/josef-pkt/orgs","repos_url":"https://api.github.com/users/josef-pkt/repos","events_url":"https://api.github.com/users/josef-pkt/events{/privacy}","received_events_url":"https://api.github.com/users/josef-pkt/received_events","type":"User","site_admin":false},"repo":{"id":1885313,"name":"statsmodels","full_name":"josef-pkt/statsmodels","owner":{"login":"josef-pkt","id":440735,"avatar_url":"https://avatars1.githubusercontent.com/u/440735?v=4","gravatar_id":"","url":"https://api.github.com/users/josef-pkt","html_url":"https://github.com/josef-pkt","followers_url":"https://api.github.com/users/josef-pkt/followers","following_url":"https://api.github.com/users/josef-pkt/following{/other_user}","gists_url":"https://api.github.com/users/josef-pkt/gists{/gist_id}","starred_url":"https://api.github.com/users/josef-pkt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/josef-pkt/subscriptions","organizations_url":"https://api.github.com/users/josef-pkt/orgs","repos_url":"https://api.github.com/users/josef-pkt/repos","events_url":"https://api.github.com/users/josef-pkt/events{/privacy}","received_events_url":"https://api.github.com/users/josef-pkt/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/josef-pkt/statsmodels","description":"main repo of statsmodels","fork":true,"url":"https://api.github.com/repos/josef-pkt/statsmodels","forks_url":"https://api.github.com/repos/josef-pkt/statsmodels/forks","keys_url":"https://api.github.com/repos/josef-pkt/statsmodels/keys{/key_id}","collaborators_url":"https://api.github.com/repos/josef-pkt/statsmodels/collaborators{/collaborator}","teams_url":"https://api.github.com/repos/josef-pkt/statsmodels/teams","hooks_url":"https://api.github.com/repos/josef-pkt/statsmodels/hooks","issue_events_url":"https://api.github.com/repos/josef-pkt/statsmodels/issues/events{/number}","events_url":"https://api.github.com/repos/josef-pkt/statsmodels/events","assignees_url":"https://api.github.com/repos/josef-pkt/statsmodels/assignees{/user}","branches_url":"https://api.github.com/repos/josef-pkt/statsmodels/branches{/branch}","tags_url":"https://api.github.com/repos/josef-pkt/statsmodels/tags","blobs_url":"https://api.github.com/repos/josef-pkt/statsmodels/git/blobs{/sha}","git_tags_url":"https://api.github.com/repos/josef-pkt/statsmodels/git/tags{/sha}","git_refs_url":"https://api.github.com/repos/josef-pkt/statsmodels/git/refs{/sha}","trees_url":"https://api.github.com/repos/josef-pkt/statsmodels/git/trees{/sha}","statuses_url":"https://api.github.com/repos/josef-pkt/statsmodels/statuses/{sha}","languages_url":"https://api.github.com/repos/josef-pkt/statsmodels/languages","stargazers_url":"https://api.github.com/repos/josef-pkt/statsmodels/stargazers","contributors_url":"https://api.github.com/repos/josef-pkt/statsmodels/contributors","subscribers_url":"https://api.github.com/repos/josef-pkt/statsmodels/subscribers","subscription_url":"https://api.github.com/repos/josef-pkt/statsmodels/subscription","commits_url":"https://api.github.com/repos/josef-pkt/statsmodels/commits{/sha}","git_commits_url":"https://api.github.com/repos/josef-pkt/statsmodels/git/commits{/sha}","comments_url":"https://api.github.com/repos/josef-pkt/statsmodels/comments{/number}","issue_comment_url":"https://api.github.com/repos/josef-pkt/statsmodels/issues/comments{/number}","contents_url":"https://api.github.com/repos/josef-pkt/statsmodels/contents/{+path}","compare_url":"https://api.github.com/repos/josef-pkt/statsmodels/compare/{base}...{head}","merges_url":"https://api.github.com/repos/josef-pkt/statsmodels/merges","archive_url":"https://api.github.com/repos/josef-pkt/statsmodels/{archive_format}{/ref}","downloads_url":"https://api.github.com/repos/josef-pkt/statsmodels/downloads","issues_url":"https://api.github.com/repos/josef-pkt/statsmodels/issues{/number}","pulls_url":"https://api.github.com/repos/josef-pkt/statsmodels/pulls{/number}","milestones_url":"https://api.github.com/repos/josef-pkt/statsmodels/milestones{/number}","notifications_url":"https://api.github.com/repos/josef-pkt/statsmodels/notifications{?since,all,participating}","labels_url":"https://api.github.com/repos/josef-pkt/statsmodels/labels{/name}","releases_url":"https://api.github.com/repos/josef-pkt/statsmodels/releases{/id}","deployments_url":"https://api.github.com/repos/josef-pkt/statsmodels/deployments","created_at":"2011-06-12T17:30:37Z","updated_at":"2018-04-18T13:28:59Z","pushed_at":"2018-05-20T20:32:19Z","git_url":"git://github.com/josef-pkt/statsmodels.git","ssh_url":"git@github.com:josef-pkt/statsmodels.git","clone_url":"https://github.com/josef-pkt/statsmodels.git","svn_url":"https://github.com/josef-pkt/statsmodels","homepage":"http://www.statsmodels.org/devel","size":29606,"stargazers_count":17,"watchers_count":17,"language":"Python","has_issues":false,"has_projects":true,"has_downloads":true,"has_wiki":true,"has_pages":false,"forks_count":15,"mirror_url":null,"archived":false,"open_issues_count":1,"license":{"key":"other","name":"Other","spdx_id":null,"url":null},"forks":15,"open_issues":1,"watchers":17,"default_branch":"master"}},"base":{"label":"statsmodels:master","ref":"master","sha":"ec3b6d02c96cd9c8f4b993434f0bbae4b3e91a21","user":{"login":"statsmodels","id":717666,"avatar_url":"https://avatars3.githubusercontent.com/u/717666?v=4","gravatar_id":"","url":"https://api.github.com/users/statsmodels","html_url":"https://github.com/statsmodels","followers_url":"https://api.github.com/users/statsmodels/followers","following_url":"https://api.github.com/users/statsmodels/following{/other_user}","gists_url":"https://api.github.com/users/statsmodels/gists{/gist_id}","starred_url":"https://api.github.com/users/statsmodels/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/statsmodels/subscriptions","organizations_url":"https://api.github.com/users/statsmodels/orgs","repos_url":"https://api.github.com/users/statsmodels/repos","events_url":"https://api.github.com/users/statsmodels/events{/privacy}","received_events_url":"https://api.github.com/users/statsmodels/received_events","type":"Organization","site_admin":false},"repo":{"id":1885237,"name":"statsmodels","full_name":"statsmodels/statsmodels","owner":{"login":"statsmodels","id":717666,"avatar_url":"https://avatars3.githubusercontent.com/u/717666?v=4","gravatar_id":"","url":"https://api.github.com/users/statsmodels","html_url":"https://github.com/statsmodels","followers_url":"https://api.github.com/users/statsmodels/followers","following_url":"https://api.github.com/users/statsmodels/following{/other_user}","gists_url":"https://api.github.com/users/statsmodels/gists{/gist_id}","starred_url":"https://api.github.com/users/statsmodels/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/statsmodels/subscriptions","organizations_url":"https://api.github.com/users/statsmodels/orgs","repos_url":"https://api.github.com/users/statsmodels/repos","events_url":"https://api.github.com/users/statsmodels/events{/privacy}","received_events_url":"https://api.github.com/users/statsmodels/received_events","type":"Organization","site_admin":false},"private":false,"html_url":"https://github.com/statsmodels/statsmodels","description":"Statsmodels: statistical modeling and econometrics in Python","fork":false,"url":"https://api.github.com/repos/statsmodels/statsmodels","forks_url":"https://api.github.com/repos/statsmodels/statsmodels/forks","keys_url":"https://api.github.com/repos/statsmodels/statsmodels/keys{/key_id}","collaborators_url":"https://api.github.com/repos/statsmodels/statsmodels/collaborators{/collaborator}","teams_url":"https://api.github.com/repos/statsmodels/statsmodels/teams","hooks_url":"https://api.github.com/repos/statsmodels/statsmodels/hooks","issue_events_url":"https://api.github.com/repos/statsmodels/statsmodels/issues/events{/number}","events_url":"https://api.github.com/repos/statsmodels/statsmodels/events","assignees_url":"https://api.github.com/repos/statsmodels/statsmodels/assignees{/user}","branches_url":"https://api.github.com/repos/statsmodels/statsmodels/branches{/branch}","tags_url":"https://api.github.com/repos/statsmodels/statsmodels/tags","blobs_url":"https://api.github.com/repos/statsmodels/statsmodels/git/blobs{/sha}","git_tags_url":"https://api.github.com/repos/statsmodels/statsmodels/git/tags{/sha}","git_refs_url":"https://api.github.com/repos/statsmodels/statsmodels/git/refs{/sha}","trees_url":"https://api.github.com/repos/statsmodels/statsmodels/git/trees{/sha}","statuses_url":"https://api.github.com/repos/statsmodels/statsmodels/statuses/{sha}","languages_url":"https://api.github.com/repos/statsmodels/statsmodels/languages","stargazers_url":"https://api.github.com/repos/statsmodels/statsmodels/stargazers","contributors_url":"https://api.github.com/repos/statsmodels/statsmodels/contributors","subscribers_url":"https://api.github.com/repos/statsmodels/statsmodels/subscribers","subscription_url":"https://api.github.com/repos/statsmodels/statsmodels/subscription","commits_url":"https://api.github.com/repos/statsmodels/statsmodels/commits{/sha}","git_commits_url":"https://api.github.com/repos/statsmodels/statsmodels/git/commits{/sha}","comments_url":"https://api.github.com/repos/statsmodels/statsmodels/comments{/number}","issue_comment_url":"https://api.github.com/repos/statsmodels/statsmodels/issues/comments{/number}","contents_url":"https://api.github.com/repos/statsmodels/statsmodels/contents/{+path}","compare_url":"https://api.github.com/repos/statsmodels/statsmodels/compare/{base}...{head}","merges_url":"https://api.github.com/repos/statsmodels/statsmodels/merges","archive_url":"https://api.github.com/repos/statsmodels/statsmodels/{archive_format}{/ref}","downloads_url":"https://api.github.com/repos/statsmodels/statsmodels/downloads","issues_url":"https://api.github.com/repos/statsmodels/statsmodels/issues{/number}","pulls_url":"https://api.github.com/repos/statsmodels/statsmodels/pulls{/number}","milestones_url":"https://api.github.com/repos/statsmodels/statsmodels/milestones{/number}","notifications_url":"https://api.github.com/repos/statsmodels/statsmodels/notifications{?since,all,participating}","labels_url":"https://api.github.com/repos/statsmodels/statsmodels/labels{/name}","releases_url":"https://api.github.com/repos/statsmodels/statsmodels/releases{/id}","deployments_url":"https://api.github.com/repos/statsmodels/statsmodels/deployments","created_at":"2011-06-12T17:04:50Z","updated_at":"2018-05-21T12:21:21Z","pushed_at":"2018-05-21T14:12:11Z","git_url":"git://github.com/statsmodels/statsmodels.git","ssh_url":"git@github.com:statsmodels/statsmodels.git","clone_url":"https://github.com/statsmodels/statsmodels.git","svn_url":"https://github.com/statsmodels/statsmodels","homepage":"http://www.statsmodels.org/devel/","size":28066,"stargazers_count":2882,"watchers_count":2882,"language":"Python","has_issues":true,"has_projects":true,"has_downloads":true,"has_wiki":true,"has_pages":false,"forks_count":1243,"mirror_url":null,"archived":false,"open_issues_count":1895,"license":{"key":"other","name":"Other","spdx_id":null,"url":null},"forks":1243,"open_issues":1895,"watchers":2882,"default_branch":"master"}},"_links":{"self":{"href":"https://api.github.com/repos/statsmodels/statsmodels/pulls/4576"},"html":{"href":"https://github.com/statsmodels/statsmodels/pull/4576"},"issue":{"href":"https://api.github.com/repos/statsmodels/statsmodels/issues/4576"},"comments":{"href":"https://api.github.com/repos/statsmodels/statsmodels/issues/4576/comments"},"review_comments":{"href":"https://api.github.com/repos/statsmodels/statsmodels/pulls/4576/comments"},"review_comment":{"href":"https://api.github.com/repos/statsmodels/statsmodels/pulls/comments{/number}"},"commits":{"href":"https://api.github.com/repos/statsmodels/statsmodels/pulls/4576/commits"},"statuses":{"href":"https://api.github.com/repos/statsmodels/statsmodels/statuses/eed924ba67ea90e5a9027d8ca849741aa9213a90"}},"author_association":"MEMBER"}}
{"actor":{"display_login":"josef-pkt"}}