Repository: statsmodels/statsmodels

Issue:
Docs: online docs are missing GEE
I have them in my local doc build, but they don't show up on http://statsmodels.sourceforge.net/devel/



Discussion:

Resolving patch:
diff --git a/statsmodels/base/model.py b/statsmodels/base/model.py
--- a/statsmodels/base/model.py
+++ b/statsmodels/base/model.py
@@ -206,7 +206,7 @@ def hessian(self, params):
 
     def fit(self, start_params=None, method='newton', maxiter=100,
             full_output=True, disp=True, fargs=(), callback=None,
-            retall=False, **kwargs):
+            retall=False, skip_hessian=False, **kwargs):
         """
         Fit method for likelihood based models
 
@@ -250,6 +250,11 @@ def fit(self, start_params=None, method='newton', maxiter=100,
         retall : bool, optional
             Set to True to return list of solutions at each iteration.
             Available in Results object's mle_retvals attribute.
+        skip_hessian : bool, optional
+            If False (default), then the negative inverse hessian is calculated
+            after the optimization. If True, then the hessian will not be
+            calculated. However, it will be available in methods that use the
+            hessian in the optimization (currently only with `"newton"`).
 
         Notes
         -----
@@ -398,7 +403,7 @@ def fit(self, start_params=None, method='newton', maxiter=100,
             Hinv = cov_params_func(self, xopt, retvals)
         elif method == 'newton' and full_output:
             Hinv = np.linalg.inv(-retvals['Hessian']) / nobs
-        else:
+        elif not skip_hessian:
             try:
                 Hinv = np.linalg.inv(-1 * self.hessian(xopt))
             except:
diff --git a/statsmodels/genmod/generalized_estimating_equations.py b/statsmodels/genmod/generalized_estimating_equations.py
--- a/statsmodels/genmod/generalized_estimating_equations.py
+++ b/statsmodels/genmod/generalized_estimating_equations.py
@@ -318,8 +318,8 @@ def unpack_cov(self, bcov):
         indicator for convergence of the optimization.
         True if the norm of the score is smaller than a threshold
     cov_type : string
-        string indicating whether a "robust", "naive" or "bias_
-        reduced" covariance is used as default
+        string indicating whether a "robust", "naive" or "bias_reduced"
+        covariance is used as default
     fit_history : dict
         Contains information about the iterations.
     fittedvalues : array
diff --git a/statsmodels/regression/mixed_linear_model.py b/statsmodels/regression/mixed_linear_model.py
--- a/statsmodels/regression/mixed_linear_model.py
+++ b/statsmodels/regression/mixed_linear_model.py
@@ -1622,8 +1622,9 @@ def fit(self, start_params=None, reml=True, niter_em=0,
                     # Only bfgs and lbfgs seem to work
                     fit_args["method"] = "bfgs"
                     pa = params.get_packed()
-                    rslt = super(MixedLM, self).fit(
-                        start_params=pa, **fit_args)
+                    rslt = super(MixedLM, self).fit(start_params=pa,
+                                                    skip_hessian=True,
+                                                    **fit_args)
                 except np.linalg.LinAlgError:
                     continue
 


Test patch: