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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/base/
H A Dcovtype.py234 res.cov_kwds['adjust_df'] = adjust_df
253 res.cov_kwds['maxlags'] = maxlags
255 res.cov_kwds['weights_func'] = weights_func
257 res.cov_kwds['use_correction'] = use_correction
273 res.cov_kwds['groups'] = groups
275 res.cov_kwds['use_correction'] = use_correction
313 res.cov_kwds['use_correction'] = use_correction
315 res.cov_kwds['weights_func'] = weights_func
337 res.cov_kwds['time'] = time = kwds['time']
346 res.cov_kwds['weights_func'] = weights_func
[all …]
H A Dmodel.py551 cov_kwds = kwargs.get('cov_kwds', {})
553 if cov_kwds:
729 res._results.cov_kwds = res_constr.cov_kwds
1353 cov_kwds = kwargs.get('cov_kwds', {})
1362 if cov_kwds is None:
1363 cov_kwds = {}
1367 use_t=use_t, **cov_kwds)
1374 use_t=None, **cov_kwds): argument
1379 if cov_kwds is None:
1380 cov_kwds = {}
[all …]
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/
H A Doaxaca.py127 cov_kwds=None, argument
136 self.cov_kwds = cov_kwds
178 cov_type=cov_type, cov_kwds=cov_kwds
181 cov_type=cov_type, cov_kwds=cov_kwds
234 cov_type=self.cov_type, cov_kwds=self.cov_kwds
237 cov_type=self.cov_type, cov_kwds=self.cov_kwds
273 cov_type=self.cov_type, cov_kwds=self.cov_kwds
279 cov_type=self.cov_type, cov_kwds=self.cov_kwds
440 cov_type=self.cov_type, cov_kwds=self.cov_kwds
446 cov_type=self.cov_type, cov_kwds=self.cov_kwds
H A D_diagnostic_other.py748 cov_type='HC1', cov_kwds=None, use_t=False): argument
791 res_ols = OLS(endog_v, ex).fit(cov_type=cov_type, cov_kwds=cov_kwds,
814 cov_type='OPG', cov_kwds=None): argument
907 cov_type='OPG', cov_kwds=None): argument
937 res = OLS(endog, ex).fit(cov_type=cov_type, cov_kwds=cov_kwds)
/dports/math/py-spglm/spglm-1.0.8/spglm/
H A Dbase.py200 cov_kwds = kwargs.get('cov_kwds', {})
204 self.cov_kwds = {'description': 'Standard Errors assume that the ' +
209 if cov_kwds is None:
210 cov_kwds = {}
214 use_t=use_t, **cov_kwds)
220 use_t=None, **cov_kwds): argument
222 if cov_kwds is None:
223 cov_kwds = {}
227 self.cov_kwds = {'description': 'Standard Errors assume that the ' +
233 use_t=use_t, **cov_kwds)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/discrete/tests/
H A Dtest_sandwich_cov.py183 cov_kwds=dict(groups=group,
324 cov_kwds=dict(groups=group,
413 cov_kwds=dict(groups=group,
545 cls.res1 = mod1.fit(cov_type='HAC', cov_kwds=kwds)
548 cls.res2 = mod2.fit(cov_type='HAC', cov_kwds=kwds)
561 cls.res1 = mod1.fit(cov_type='HAC', cov_kwds=kwds)
565 cls.res2 = mod2.fit(cov_type='HAC', cov_kwds=kwds2)
577 cls.res1 = mod1.fit(cov_type='HAC', cov_kwds=kwds)
580 cls.res2 = mod2.fit(cov_type='HAC', cov_kwds=kwds)
621 cls.res1 = mod1.fit(cov_type='HAC', cov_kwds=kwds)
[all …]
H A Dtest_constrained.py522 cov_kwds = {'scaling_factor': 32/31}
526 cov_kwds=cov_kwds, atol=1e-10)
531 'cov_kwds': cov_kwds})
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/regression/
H A Dlinear_model.py348 cov_type=cov_type, cov_kwds=cov_kwds, use_t=use_t)
353 cov_type=cov_type, cov_kwds=cov_kwds, use_t=use_t,
1593 self.cov_kwds = {
1601 if cov_kwds is None:
1602 cov_kwds = {}
1603 if 'use_t' in cov_kwds:
2171 maxlags = self.cov_kwds['maxlags']
2175 groups = self.cov_kwds['groups']
2502 res.cov_kwds['adjust_df'] = adjust_df
2522 res.cov_kwds['maxlags'] = maxlags
[all …]
H A Drecursive_ls.py169 cov_kwds = {
178 cov_kwds=cov_kwds)
192 cov_kwds = {
201 cov_kwds=cov_kwds)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/regime_switching/
H A Dmarkov_switching.py863 if cov_kwds is not None:
864 result_kwargs['cov_kwds'] = cov_kwds
1136 cov_type=cov_type, cov_kwds=cov_kwds)
1218 cov_type=cov_type, cov_kwds=cov_kwds)
1699 self.cov_kwds = {}
1706 if cov_kwds is None:
1707 cov_kwds = {}
1709 cov_kwds.pop('approx_complex_step', True))
1714 **cov_kwds)
1719 self.cov_kwds['cov_type'] = (
[all …]
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/examples/
H A Dex_ols_robustcov.py48 cov_kwds=dict(maxlags=4, use_correction=True))
55 res_hc1c = mod_olsg.fit(cov_type='HC1', cov_kwds={'use_t':True})
61 cov_kwds={'groups':decade, 'use_t':True})
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/gam/
H A Dgeneralized_additive_model.py606 scale=None, cov_type='nonrobust', cov_kwds=None, use_t=None, argument
635 cov_type=cov_type, cov_kwds=cov_kwds,
642 cov_type=cov_type, cov_kwds=cov_kwds,
649 cov_type=cov_type, cov_kwds=cov_kwds,
658 scale=None, cov_type='nonrobust', cov_kwds=None, use_t=None, argument
746 cov_type=cov_type, cov_kwds=cov_kwds,
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/genmod/
H A Dgeneralized_linear_model.py1077 cov_kwds=cov_kwds, use_t=use_t, **kwargs)
1089 cov_kwds=cov_kwds, use_t=use_t,
1099 cov_kwds=None, use_t=None, max_start_irls=3, argument
1114 cov_kwds=None, use_t=None,
1151 cov_type=cov_type, cov_kwds=cov_kwds,
1166 scale=None, cov_type='nonrobust', cov_kwds=None, argument
1239 cov_type=cov_type, cov_kwds=cov_kwds,
1487 cov_type='nonrobust', cov_kwds=None, use_t=None): argument
1546 if cov_kwds is None:
1547 cov_kwds = {}
[all …]
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/arima/
H A Dmodel.py224 cov_type=None, cov_kwds=None, return_params=False, argument
392 cov_type=cov_type, cov_kwds=cov_kwds, **method_kwargs)
481 cov_type=cov_type, cov_kwds=cov_kwds)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/
H A D_quarterly_ar1.py74 cov_kwds=None, maxiter=500, tolerance=1e-6, argument
123 cov_type=cov_type, cov_kwds=cov_kwds)
H A Dmlemodel.py729 cov_type=cov_type, cov_kwds=cov_kwds)
780 if cov_kwds is not None:
781 result_kwargs['cov_kwds'] = cov_kwds
2310 self.cov_kwds = {}
2319 if cov_kwds is None:
2320 cov_kwds = {}
2322 cov_kwds.pop('approx_complex_step', True))
2327 **cov_kwds)
2332 self.cov_kwds['cov_type'] = (
2518 res.cov_kwds = {}
[all …]
H A Dexponential_smoothing.py571 def filter(self, params, cov_type=None, cov_kwds=None, argument
579 params, cov_type=cov_type, cov_kwds=cov_kwds,
588 def smooth(self, params, cov_type=None, cov_kwds=None, argument
596 params, cov_type=cov_type, cov_kwds=cov_kwds,
H A Ddynamic_factor_mq.py2265 cov_type='none', cov_kwds=None, method='em', maxiter=500, argument
2407 cov_type=cov_type, cov_kwds=cov_kwds, maxiter=maxiter,
2417 cov_kwds=cov_kwds, method=method, maxiter=maxiter,
2426 cov_kwds=None, maxiter=500, tolerance=1e-6, disp=False, argument
2626 cov_type=cov_type, cov_kwds=cov_kwds,
2716 cov_type=cov_type, cov_kwds=cov_kwds)
3023 complex_step=False, cov_type='none', cov_kwds=None, argument
3051 complex_step=complex_step, cov_type=cov_type, cov_kwds=cov_kwds,
3056 complex_step=False, cov_type='none', cov_kwds=None, argument
3089 complex_step=complex_step, cov_type=cov_type, cov_kwds=cov_kwds,
/dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/
H A Dordinal_regression.py314 cov_kwds={"maxlags": 2})
318 cov_kwds={"maxlags": 2})
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/base/tests/
H A Dtest_penalized.py237 cov_kwds = {'description': 'Standard Errors are heteroscedasticity '
240 assert_equal(self.res1.cov_kwds, cov_kwds)
385 cov_kwds = {'description': 'Standard Errors are heteroscedasticity '
388 assert_equal(self.res1.cov_kwds, cov_kwds)
389 assert_equal(self.res1.cov_kwds, self.res1.results_constrained.cov_kwds)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/exponential_smoothing/
H A Dbase.py229 cov_kwds=None, argument
238 if cov_kwds is not None:
239 result_kwargs["cov_kwds"] = cov_kwds
965 if hasattr(self, "cov_type") and "description" in self.cov_kwds:
966 etext.append(self.cov_kwds["description"])
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/genmod/tests/
H A Dtest_glm_weights.py274 cov_kwds = {'groups': gid, 'use_correction':False}
279 cls.res1 = mod.fit(cov_type='cluster', cov_kwds=cov_kwds)
777 cov_kwds = {'groups': gid, 'use_correction': False}
782 ).fit(cov_type='cluster', cov_kwds=cov_kwds)
788 ).fit(cov_type='cluster', cov_kwds=cov_kwds)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/ardl/
H A Dmodel.py450 cov_kwds: Dict[str, Any] = None,
457 cov_type=cov_type, cov_kwds=cov_kwds, use_t=use_t
473 cov_kwds: Dict[str, Any] = None,
533 cov_type=cov_type, cov_kwds=cov_kwds, use_t=use_t
1917 cov_kwds: Dict[str, Any] = None,
1921 cov_type=cov_type, cov_kwds=cov_kwds, use_t=use_t
2258 cov_kwds: Dict[str, Any] = None,
2381 res = uecm.fit(cov_type=cov_type, cov_kwds=cov_kwds, use_t=use_t)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/discrete/
H A Ddiscrete_model.py1553 if cov_kwds is None:
1554 cov_kwds = {}
2916 cov_type='nonrobust', cov_kwds=None, use_t=None, argument
2975 if cov_kwds is None:
2976 cov_kwds = {} #TODO: make this unnecessary ?
3293 cov_type='nonrobust', cov_kwds=None, use_t=None, argument
3344 if cov_kwds is None:
3345 cov_kwds = {}
3489 if cov_kwds is None:
3490 cov_kwds = {}
[all …]
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/tests/
H A Dtest_mlemodel.py396 res.cov_kwds['description'],
402 res.cov_kwds['description'],
409 res.cov_kwds['description'],
416 res.cov_kwds['description'],
423 res.cov_kwds['description'],
431 res.cov_kwds['description'],
439 res.cov_kwds['description'],

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