Statsmodels: statistical modeling and econometrics in Python
This report summarizes scanner evidence for statsmodels/statsmodels, a Python project with 11,681 GitHub stars. SynthScan examined 543,222 lines of code across 1575 source files, recording 5191 pattern matches distributed across 25 syntactic categories. The legacy adjusted pattern signal is 13.7 (Moderate pattern signal); it is not an authorship estimate.
Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 3510 finding(s).
Are imports declared and package/API names verified? 1112 finding(s).
Are wrappers, guards, and repeated structures necessary? 2737 finding(s).
Which changes need focused human review? 5191 finding(s).
This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.
Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.
This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.
The scanner identified 5191 distinct pattern matches across 25 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tools/validate_docstrings.py | 656 | def get_returns_not_on_nested_functions(node): | CODE |
| LOW | tools/validate_docstrings.py | 692 | def mentioned_private_classes(self): | CODE |
| LOW | archive/tests/test_predict_functional.py | 115 | def test_glm_formula_contrast(self, close_figures): | CODE |
| LOW | archive/tests/test_predict_functional.py | 296 | def test_noformula_prediction(self, close_figures): | CODE |
| LOW | archive/distributions/tests/test_gof_new.py | 7 | def test_loop_vectorized_batch_equivalence(): | CODE |
| LOW | archive/stats/stats_mstats_short.py | 332 | def edf_normal_inverse_transformed(x, alpha=3.0 / 8, beta=3.0 / 8, axis=0): | CODE |
| LOW | examples/python/lowess.py | 67 | def lowess_with_confidence_bounds( | CODE |
| LOW | examples/python/statespace_tvpvar_mcmc_cfa.py | 493 | def plot_coefficients_by_equation(states): | CODE |
| LOW | statsmodels/conftest.py | 169 | def pytest_collection_modifyitems(config, items): | CODE |
| LOW | statsmodels/conftest.py | 206 | def check_global_randomstate_usage(request): | CODE |
| LOW | statsmodels/robust/tools.py | 567 | def _convert_to_dict_mvmean_effs(eff_mean=True): | CODE |
| LOW | statsmodels/robust/covariance.py | 51 | def _check_standardizing_scale(scale): | STRING |
| LOW | statsmodels/robust/covariance.py | 118 | def _naive_ledoit_wolf_shrinkage(x, center): | STRING |
| LOW | statsmodels/robust/covariance.py | 153 | def coef_normalize_cov_truncated(frac, k_vars): | STRING |
| LOW | statsmodels/robust/covariance.py | 194 | def _coef_normalize_cov_truncated_(frac, k_vars): | STRING |
| LOW | statsmodels/robust/covariance.py | 1012 | def cov_tyler_pairs_regularized( | CODE |
| LOW | statsmodels/robust/robust_linear_model.py | 320 | >>> def avg_abs_deviation_with_model(model, resid): | CODE |
| LOW | statsmodels/robust/tests/test_rlm.py | 329 | def test_rlm_start_values_errors(): | CODE |
| LOW | statsmodels/robust/tests/test_rlm.py | 342 | def test_rlm_scale_est_callback_receives_model(): | CODE |
| LOW | statsmodels/robust/tests/test_rlm.py | 363 | def test_rlm_scale_est_one_and_two_inputs(): | CODE |
| LOW | statsmodels/robust/tests/test_rlm.py | 383 | def test_rlm_scale_est_resid_callable_df_correction(): | CODE |
| LOW | statsmodels/robust/tests/test_rlm.py | 471 | def test_summary_after_remove_data(): | CODE |
| LOW | statsmodels/robust/tests/test_rlm.py | 501 | def test_fit_invalid_options_raise(): | CODE |
| LOW | statsmodels/robust/tests/test_rlm.py | 525 | def test_rlm_results_direct_construction_validates_cov(): | CODE |
| LOW | statsmodels/robust/tests/test_rlm.py | 546 | def test_mad_iqr_nonpositive_c_raises(): | CODE |
| LOW | statsmodels/robust/tests/test_scale.py | 258 | def test_qn_too_few_observations(self): | CODE |
| LOW | statsmodels/robust/tests/test_scale.py | 519 | def test_scale_trimmed_approx(): | CODE |
| LOW | statsmodels/robust/tests/test_scale.py | 554 | def test_scale_trimmed_distarge(): | CODE |
| LOW⚡ | statsmodels/robust/tests/test_covariance.py | 738 | def test_covdet_large_k_det_normalization(cov_class): | CODE |
| LOW⚡ | statsmodels/robust/tests/test_covariance.py | 746 | def test_cov_tyler_regularized_n_iter(): | CODE |
| LOW⚡ | statsmodels/robust/tests/test_covariance.py | 756 | def test_cov_tyler_regularized_corr_uses_scale(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 295 | def test_naive_ledoit_wolf_shrinkage(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 358 | def test_naive_ledoit_wolf_shrinkage_spherical(x): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 365 | def test_cov_starting_small_nobs(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 395 | def test_cov_starting_keeps_trimmed_starts(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 409 | def test_mahalanobis_singular_cov(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 424 | def test_mahalanobis_singular_cov_additional(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 439 | def test_mahalanobis_numerically_singular_cov(seed): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 458 | def test_mahalanobis_ill_conditioned_cov(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 471 | def test_mahalanobis_badly_scaled_cov(span): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 493 | def test_mahalanobis_cov_not_square(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 501 | def test_cov_weighted_det_non_finite_determinant(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 527 | def test_covdetmcd_rank_by_logdet(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 650 | def test_cov_tyler_regularized_one_column(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 678 | def test_naive_ledoit_wolf_one_column(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 688 | def test_cov_starting_one_column(nobs): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 727 | def test_det_root_does_not_overflow(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 841 | def test_cov_iter_invalid_rescale_raises(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 850 | def test_get_detcov_startidx_invalid_methods_cov_raises(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 872 | def test_get_detcov_startidx_r_deterministic_mcd(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 902 | def test_get_detcov_startidx_ranks_by_mahalanobis_distance(): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 928 | def test_covdet_zero_standardizing_scale_raises(cov_class, fit_kw): | CODE |
| LOW | statsmodels/robust/tests/test_covariance.py | 941 | def test_cov_starting_zero_mad_raises(): | CODE |
| LOW | statsmodels/robust/tests/test_resistant_linear_model.py | 89 | def test_fit_moment_condition_and_start_results(self): | CODE |
| LOW | statsmodels/robust/tests/test_resistant_linear_model.py | 103 | def test_fit_beats_ols_under_contamination(self): | CODE |
| LOW | statsmodels/robust/tests/test_resistant_linear_model.py | 123 | def test_univariate_quantile_start(self): | CODE |
| LOW | statsmodels/robust/tests/test_resistant_linear_model.py | 138 | def test_include_endog_and_col_indices(self): | CODE |
| LOW | statsmodels/robust/tests/test_resistant_linear_model.py | 170 | def test_fit_beats_ols_under_contamination(self): | CODE |
| LOW | statsmodels/robust/tests/test_resistant_linear_model.py | 182 | def test_efficiency_close_to_ols_on_clean_data(self): | CODE |
| LOW | statsmodels/robust/tests/test_resistant_linear_model.py | 204 | def test_start_tuple_skips_s_stage(self): | CODE |
| 1848 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | statsmodels/tools/grouputils.py | 795 | if not test.index.equals(test_sorted.index): | CODE |
| HIGH | statsmodels/tools/tests/test_grouputils.py | 56 | assert not index.equals(self.grouping.index) | CODE |
| HIGH | statsmodels/tools/tests/test_grouputils.py | 60 | assert not sorted_data.equals(self.data) | CODE |
| HIGH | statsmodels/tools/tests/test_grouputils.py | 75 | assert not sorted_data.equals(series) | CODE |
| HIGH | statsmodels/datasets/utils.py | 159 | if data.index.equals(Index(lrange(1, len(data) + 1))): | CODE |
| HIGH | statsmodels/imputation/mice.py | 329 | ix_obs = np.flatnonzero(~null) | CODE |
| HIGH | statsmodels/imputation/mice.py | 330 | ix_miss = np.flatnonzero(null) | CODE |
| HIGH | statsmodels/genmod/generalized_linear_model.py | 2069 | self._endog, self.null, self._var_weights, self._freq_weights | STRING |
| HIGH | statsmodels/genmod/generalized_linear_model.py | 2079 | self.null, | STRING |
| HIGH | statsmodels/genmod/tests/test_glm.py | 1045 | assert_allclose(mod1.null, mod2.null, rtol=1e-10) | STRING |
| HIGH⚡ | statsmodels/tsa/statespace/tools.py | 380 | if s.columns.equals(pd.Index([None])): | STRING |
| HIGH⚡ | statsmodels/tsa/statespace/tools.py | 386 | if axis == 0 and not base_columns.equals(s_columns): | STRING |
| HIGH⚡ | statsmodels/tsa/statespace/tools.py | 389 | elif axis == 1 and not series[0].index.equals(s.index): | STRING |
| HIGH | statsmodels/tsa/statespace/dynamic_factor_mq.py | 1360 | endog_mean.index.equals(pd.Index(endog_names))): | STRING |
| HIGH | statsmodels/tsa/statespace/dynamic_factor_mq.py | 1367 | endog_std.index.equals(pd.Index(endog_names))): | STRING |
| HIGH | statsmodels/tsa/statespace/mlemodel.py | 73 | if given_index is not None and not desired_index.equals(given_index): | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_simulate.py | 1905 | assert_(actual.columns.equals(desired.columns)) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_simulate.py | 1915 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_simulate.py | 1916 | assert_(actual.columns.equals(desired.columns)) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_simulate.py | 1957 | assert_(actual.columns.equals(desired.columns)) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_simulate.py | 1967 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_simulate.py | 1968 | assert_(actual.columns.equals(desired.columns)) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_simulate.py | 2019 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_simulate.py | 2020 | assert_(actual.columns.equals(desired.columns)) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_simulate.py | 2093 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_simulate.py | 2100 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH | statsmodels/tsa/statespace/tests/test_simulate.py | 1889 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH | statsmodels/tsa/statespace/tests/test_simulate.py | 1931 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH | statsmodels/tsa/statespace/tests/test_simulate.py | 1989 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH | statsmodels/tsa/statespace/tests/test_simulate.py | 2005 | assert_(actual.columns.equals(desired.columns)) | CODE |
| HIGH | statsmodels/tsa/statespace/tests/test_simulate.py | 2042 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH | statsmodels/tsa/statespace/tests/test_simulate.py | 2059 | assert_(actual.columns.equals(desired.columns)) | CODE |
| HIGH | statsmodels/tsa/statespace/tests/test_simulate.py | 2073 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH | statsmodels/tsa/statespace/tests/test_simulate.py | 2074 | assert_(actual.columns.equals(desired.columns)) | CODE |
| HIGH⚡ | …smodels/tsa/statespace/tests/test_impulse_responses.py | 860 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH⚡ | …smodels/tsa/statespace/tests/test_impulse_responses.py | 873 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH⚡ | …smodels/tsa/statespace/tests/test_impulse_responses.py | 885 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH⚡ | …smodels/tsa/statespace/tests/test_impulse_responses.py | 898 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH | …smodels/tsa/statespace/tests/test_impulse_responses.py | 913 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH | …smodels/tsa/statespace/tests/test_impulse_responses.py | 918 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_sarimax.py | 2967 | assert_(mod._index.equals(endog.index[1:])) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_sarimax.py | 2975 | assert_(mod._index.equals(endog.index[1:])) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_sarimax.py | 2985 | assert_(mod._index.equals(endog.index[1:])) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_sarimax.py | 2997 | assert_(mod._index.equals(pd.RangeIndex(start=0, stop=len(values) - 1))) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_sarimax.py | 2998 | assert_(mod.data.row_labels.equals(index[1:])) | CODE |
| HIGH | statsmodels/tsa/statespace/tests/test_sarimax.py | 2278 | assert_equal(out.index.equals(pd.Index(["a"])), True) | CODE |
| HIGH⚡ | …smodels/tsa/statespace/tests/test_dynamic_factor_mq.py | 1603 | assert_(mod._index.equals(mod_base._index)) | CODE |
| HIGH⚡ | …smodels/tsa/statespace/tests/test_dynamic_factor_mq.py | 1610 | assert_(mod._index.equals(mod_base._index)) | CODE |
| HIGH⚡ | …smodels/tsa/statespace/tests/test_dynamic_factor_mq.py | 1617 | assert_(mod._index.equals(mod_base._index)) | CODE |
| HIGH | …smodels/tsa/statespace/tests/test_dynamic_factor_mq.py | 1692 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH | …smodels/tsa/statespace/tests/test_dynamic_factor_mq.py | 1693 | assert_(actual.columns.equals(desired.columns)) | CODE |
| HIGH | …smodels/tsa/statespace/tests/test_dynamic_factor_mq.py | 1708 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH | …smodels/tsa/statespace/tests/test_dynamic_factor_mq.py | 1726 | assert_(actual.index.equals(desired.index)) | CODE |
| HIGH | …smodels/tsa/statespace/tests/test_dynamic_factor_mq.py | 1727 | assert_(actual.columns.equals(desired.columns)) | CODE |
| HIGH | statsmodels/tsa/statespace/tests/test_varmax.py | 976 | assert out.index.equals(pd.Index(["a"])) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_mlemodel.py | 1309 | assert states.predicted.index.equals(predicted_ix) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_mlemodel.py | 1310 | assert states.predicted.columns.equals(cols) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_mlemodel.py | 1312 | assert states.predicted_cov.index.equals(predicted_cov_ix) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_mlemodel.py | 1313 | assert states.predicted.columns.equals(cols) | CODE |
| HIGH⚡ | statsmodels/tsa/statespace/tests/test_mlemodel.py | 1316 | assert states.filtered.index.equals(ix) | CODE |
| 144 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tools/check_public_api_coverage.py | 46 | CODE | |
| LOW | tools/class_coverage_report.py | 46 | CODE | |
| LOW | archive/examples/ex_wald_anova.py | 10 | CODE | |
| LOW | archive/examples/ex_generic_mle_tdist.py | 17 | CODE | |
| LOW | archive/examples/tests/test_notebooks.py | 8 | CODE | |
| LOW | archive/examples/tests/test_notebooks.py | 28 | CODE | |
| LOW | archive/examples/tests/test_notebooks.py | 35 | CODE | |
| LOW | archive/examples/tests/test_notebooks.py | 42 | CODE | |
| LOW | archive/examples/tests/test_notebooks.py | 49 | CODE | |
| LOW | archive/examples/thirdparty/ex_ratereturn.py | 95 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 3 | CODE | |
| LOW | archive/stats/diagnostic.py | 23 | CODE | |
| LOW | docs/source/conf.py | 60 | CODE | |
| LOW | statsmodels/__init__.py | 1 | CODE | |
| LOW | statsmodels/api.py | 79 | CODE | |
| LOW | statsmodels/api.py | 79 | CODE | |
| LOW | statsmodels/api.py | 84 | CODE | |
| LOW | statsmodels/api.py | 84 | CODE | |
| LOW | statsmodels/api.py | 84 | CODE | |
| LOW | statsmodels/api.py | 84 | CODE | |
| LOW | statsmodels/api.py | 84 | CODE | |
| LOW | statsmodels/api.py | 84 | CODE | |
| LOW | statsmodels/api.py | 85 | CODE | |
| LOW | statsmodels/api.py | 86 | CODE | |
| LOW | statsmodels/api.py | 86 | CODE | |
| LOW | statsmodels/api.py | 86 | CODE | |
| LOW | statsmodels/api.py | 91 | CODE | |
| LOW | statsmodels/api.py | 91 | CODE | |
| LOW | statsmodels/api.py | 91 | CODE | |
| LOW | statsmodels/api.py | 96 | CODE | |
| LOW | statsmodels/api.py | 96 | CODE | |
| LOW | statsmodels/api.py | 96 | CODE | |
| LOW | statsmodels/api.py | 96 | CODE | |
| LOW | statsmodels/api.py | 96 | CODE | |
| LOW | statsmodels/api.py | 96 | CODE | |
| LOW | statsmodels/api.py | 96 | CODE | |
| LOW | statsmodels/api.py | 105 | CODE | |
| LOW | statsmodels/api.py | 105 | CODE | |
| LOW | statsmodels/api.py | 105 | CODE | |
| LOW | statsmodels/api.py | 110 | CODE | |
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| LOW | statsmodels/api.py | 112 | CODE | |
| LOW | statsmodels/api.py | 113 | CODE | |
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| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | pyproject.toml | 201 | # flake8-logging-format | COMMENT |
| LOW | pyproject.toml | 221 | # flake8-django (no Django usage, currently clean) | COMMENT |
| LOW | pyproject.toml | 281 | # Counts below are from `ruff check . --select ALL` | COMMENT |
| LOW | pyproject.toml | 321 | # mutable-class-default | COMMENT |
| LOW | tools/R2nparray/R/R2nparray-package.R | 1 | #' R2nparray | COMMENT |
| LOW | tools/R2nparray/R/R2nparray.R | 1 | #' Print R object contents to python expression | COMMENT |
| LOW | tools/R2nparray/R/R2nparray.R | 161 | } | COMMENT |
| LOW | tools/R2nparray/R/R2nparray.R | 201 | #SUR <- systemfit(inv ~ value + capital, method='SUR',data=panel) | COMMENT |
| LOW | tools/R2nparray/R/R2nparray.R | 221 | #equations = SUR[["eq"]] | COMMENT |
| LOW | archive/infotheo.py | 21 | # Biometrika 40:237-264. | COMMENT |
| LOW | archive/descstats.py | 141 | "{range:>20}".format(**kwargs) | COMMENT |
| LOW | archive/descstats.py | 161 | # dts=s2d.string2dummy(dta) | COMMENT |
| LOW | archive/descstats.py | 181 | data = sm.datasets.longley.load() | COMMENT |
| LOW | archive/nonparametric/kde2.py | 101 | [kde.density(np.matrix([xi, yi])) for xi, yi in zip(xr.ravel(), yr.ravel(), strict=True)] | COMMENT |
| LOW | archive/panel/mixed.py | 641 | COMMENT | |
| LOW | archive/panel/panelmod.py | 121 | # raise ValueError("If pandel_data is False then endog, exog, \ | COMMENT |
| LOW | archive/panel/panelmod.py | 161 | self.paneluniq = np.unique(panel) | COMMENT |
| LOW | archive/panel/panelmod.py | 181 | # TODO: can the above be simplified to slice notation? | COMMENT |
| LOW | archive/panel/panelmod.py | 281 | return self._fit_fixed(method, effects) | COMMENT |
| LOW | archive/panel/panelmod.py | 381 | panel_fe2 = panel_mod.fit(model="fixed", effects="twoways") | COMMENT |
| LOW | archive/examples/ex_misc_tmodel.py | 81 | # print pp.min(0) | COMMENT |
| LOW | archive/examples/ex_onewaygls.py | 21 | # '2-2': 4 groups, | COMMENT |
| LOW | archive/examples/ex_kernel_regression_sigtest.py | 101 | # ax.plot(x, y, 'o', alpha=0.5) | COMMENT |
| LOW | archive/examples/ex_generic_mle_tdist.py | 421 | # 0.0164150896481 | COMMENT |
| LOW | archive/examples/ex_generic_mle_tdist.py | 441 | # Current function value: -679.950443 | COMMENT |
| LOW | archive/examples/ex_generic_mle_tdist.py | 461 | # | COMMENT |
| LOW | archive/examples/ex_generic_mle_tdist.py | 481 | # >>> y.min() | COMMENT |
| LOW | archive/examples/ex_generic_mle_tdist.py | 501 | # -4.4908502522786327 | COMMENT |
| LOW | archive/examples/example_pca_regression.py | 41 | print("OLS on original data") | COMMENT |
| LOW | archive/examples/example_gam_0.py | 161 | COMMENT | |
| LOW | archive/examples/ex_cusum.py | 21 | example = ["null", "smalldiff", "mediumdiff", "largediff"][1] | COMMENT |
| LOW | archive/examples/ex_cusum.py | 101 | COMMENT | |
| LOW | archive/examples/example_sysreg.py | 61 | print(grun_ires.params) | COMMENT |
| LOW | archive/examples/example_sysreg.py | 221 | print("We get the right estimate") | COMMENT |
| LOW | archive/examples/bayesprior.py | 201 | # NOTE: but it matches the language? I think mine is right! | COMMENT |
| LOW | archive/examples/bayesprior.py | 221 | # p_i | COMMENT |
| LOW | archive/examples/bayesprior.py | 241 | psigma2 = pymc.rinverse_gamma(1.0, 4.0, size=1e6) | COMMENT |
| LOW | archive/tsa/try_var_convolve.py | 161 | ar1[i] = 0.8 * ar1[i - 1] + rvs[i - 1] | COMMENT |
| LOW | archive/tsa/examples/example_var.py | 41 | COMMENT | |
| LOW | archive/regression/runmnl.py | 161 | # if self.endog where index then xb[self.endog] | COMMENT |
| LOW | archive/distributions/transform_functions.py | 161 | # | COMMENT |
| LOW | docs/source/conf.py | 1 | # | COMMENT |
| LOW | docs/source/conf.py | 141 | # documents. | COMMENT |
| LOW | docs/source/conf.py | 241 | # html_title = None | COMMENT |
| LOW | docs/source/conf.py | 281 | # If false, no module index is generated. | COMMENT |
| LOW | docs/source/conf.py | 301 | # base URL from which the finished HTML is served. | COMMENT |
| LOW | docs/source/conf.py | 381 | "Jonathan Taylor, statsmodels-developers" | COMMENT |
| LOW | docs/source/conf.py | 401 | COMMENT | |
| LOW | examples/python/statespace_local_linear_trend.py | 1 | #!/usr/bin/env python | COMMENT |
| LOW | examples/python/statespace_local_linear_trend.py | 21 | # Chapter 3.2 for all notation and details): | COMMENT |
| LOW | examples/python/statespace_local_linear_trend.py | 41 | # \end{align} | COMMENT |
| LOW | examples/python/statespace_local_linear_trend.py | 61 | import statsmodels.api as sm | COMMENT |
| LOW | examples/python/statespace_local_linear_trend.py | 81 | # the observation and state covariance matrices. More generally, the | COMMENT |
| LOW | examples/python/statespace_local_linear_trend.py | 101 | # initial distribution of the state vector. If the distribution is known, | COMMENT |
| LOW | examples/python/stl_decomposition.py | 1 | #!/usr/bin/env python | COMMENT |
| LOW | examples/python/lowess.py | 1 | #!/usr/bin/env python | COMMENT |
| LOW | examples/python/formulas.py | 1 | #!/usr/bin/env python | COMMENT |
| LOW | examples/python/formulas.py | 61 | # ``patsy`` formula. ``data`` takes a [pandas](https://pandas.pydata.org/) | COMMENT |
| LOW | examples/python/formulas.py | 101 | # explicitly as categorical, we could have done so by using the ``C()`` | COMMENT |
| LOW | examples/python/tsa_arma_1.py | 1 | #!/usr/bin/env python | COMMENT |
| 593 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | tools/check_public_api_coverage.py | 63 | # -------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tools/check_public_api_coverage.py | 65 | # -------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tools/check_public_api_coverage.py | 236 | # -------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tools/check_public_api_coverage.py | 238 | # -------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tools/check_public_api_coverage.py | 337 | # -------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tools/check_public_api_coverage.py | 339 | # -------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tools/R2nparray/R/R2nparray.R | 169 | #---------- | COMMENT |
| MEDIUM | archive/descstats.py | 14 | # ============================================ | COMMENT |
| MEDIUM | archive/descstats.py | 16 | # ============================================ | COMMENT |
| MEDIUM | archive/tools/try_mctools.py | 52 | # ---------------------- | COMMENT |
| MEDIUM | archive/examples/ex_onewaygls.py | 16 | # -------------- | COMMENT |
| MEDIUM | archive/examples/ex_onewaygls.py | 25 | # ---------------- | COMMENT |
| MEDIUM | archive/examples/ex_onewaygls.py | 191 | # ----------------------- | STRING |
| MEDIUM | archive/examples/ex_regressionplots.py | 20 | # --------------------------------------------- | COMMENT |
| MEDIUM | archive/examples/try_gmm_other.py | 67 | # ---------------------------- | COMMENT |
| MEDIUM | archive/examples/try_gmm_other.py | 120 | # ------------------------------------------------- | COMMENT |
| MEDIUM | archive/examples/ex_cusum.py | 20 | # -------------- | COMMENT |
| MEDIUM | archive/examples/ex_cusum.py | 28 | # ---------------- | COMMENT |
| MEDIUM | archive/examples/tut_ols_ancova.py | 46 | # ------------------------------------------- | COMMENT |
| MEDIUM | archive/regression/ols_anova_original.py | 319 | # ------------------- | STRING |
| MEDIUM | archive/regression/try_ols_anova.py | 201 | # ------------------------------ | STRING |
| MEDIUM | archive/regression/try_ols_anova.py | 239 | # ------------------- | STRING |
| MEDIUM | archive/regression/ar_panel.py | 137 | # ------------ | COMMENT |
| MEDIUM | archive/regression/runmnl.py | 236 | # ------------------------------ | COMMENT |
| MEDIUM | archive/regression/runmnl.py | 309 | # ---------------------- | COMMENT |
| MEDIUM | archive/distributions/estimators.py | 451 | # ----------------------------- | COMMENT |
| MEDIUM | archive/distributions/estimators.py | 490 | # --------------------------- | COMMENT |
| MEDIUM | archive/distributions/estimators.py | 522 | # ------------------------ | COMMENT |
| MEDIUM | archive/distributions/estimators.py | 664 | # =================================================== | COMMENT |
| MEDIUM | archive/distributions/estimators.py | 667 | # ------------------------------- | COMMENT |
| MEDIUM | archive/distributions/estimators.py | 690 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | examples/python/mixed_lm_example.py | 41 | # ======================================= | COMMENT |
| MEDIUM | examples/incomplete/wls_extended.py | 51 | # ---------------------------------------------------- | COMMENT |
| MEDIUM | examples/incomplete/wls_extended.py | 84 | # ---------------------------------------------------- | COMMENT |
| MEDIUM | examples/incomplete/wls_extended.py | 150 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | examples/incomplete/wls_extended.py | 260 | # -------------------------- | COMMENT |
| MEDIUM | examples/incomplete/wls_extended.py | 386 | # ----------------------------------------------------------------- | STRING |
| MEDIUM | examples/incomplete/dates.py | 10 | # --------------- | COMMENT |
| MEDIUM | examples/incomplete/dates.py | 19 | # ------------ | COMMENT |
| MEDIUM | examples/incomplete/dates.py | 33 | # -------------------- | COMMENT |
| MEDIUM | statsmodels/robust/tests/test_rlm.py | 211 | # -------------------------------------------------------------------- | COMMENT |
| MEDIUM | statsmodels/tools/eval_measures.py | 389 | # --------------------- | COMMENT |
| MEDIUM | statsmodels/iolib/summary.py | 193 | # ------------------------------------ | COMMENT |
| MEDIUM | statsmodels/iolib/summary.py | 242 | # ------------- | COMMENT |
| MEDIUM | statsmodels/iolib/summary.py | 247 | # -------------- | COMMENT |
| MEDIUM | statsmodels/iolib/summary.py | 480 | # ------------------------------------ | COMMENT |
| MEDIUM | statsmodels/iolib/summary.py | 569 | # ------------------------------------ | COMMENT |
| MEDIUM | statsmodels/sandbox/regression/tests/test_gmm.py | 101 | # ----------------- | COMMENT |
| MEDIUM | statsmodels/sandbox/regression/tests/test_gmm.py | 187 | # ----------------- | COMMENT |
| MEDIUM | statsmodels/sandbox/regression/tests/test_gmm.py | 858 | # ------------------ | COMMENT |
| MEDIUM | statsmodels/sandbox/distributions/tests/test_transf.py | 104 | # ----------------------------- | COMMENT |
| MEDIUM | statsmodels/sandbox/stats/multicomp.py | 498 | # ============================ | COMMENT |
| MEDIUM | statsmodels/sandbox/stats/multicomp.py | 502 | # ============================ | COMMENT |
| MEDIUM⚡ | statsmodels/tsa/filters/tests/test_hamilton_filter.py | 18 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | statsmodels/tsa/filters/tests/test_hamilton_filter.py | 20 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | statsmodels/tsa/filters/tests/test_hamilton_filter.py | 26 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | statsmodels/tsa/filters/tests/test_hamilton_filter.py | 28 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | statsmodels/tsa/filters/tests/test_hamilton_filter.py | 85 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | statsmodels/tsa/filters/tests/test_hamilton_filter.py | 87 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | statsmodels/tsa/filters/tests/test_hamilton_filter.py | 180 | # --------------------------------------------------------------------------- | COMMENT |
| 130 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | archive/nonparametric/kernel_extras.py | 0 | multivariate conditional and unconditional kernel regression with mixed data types references ---------- [1] racine, j., | STRING |
| HIGH | statsmodels/nonparametric/kernel_density.py | 0 | multivariate conditional and unconditional kernel regression with mixed data types references ---------- [1] racine, j., | STRING |
| HIGH | statsmodels/nonparametric/kernel_regression.py | 0 | multivariate conditional and unconditional kernel regression with mixed data types references ---------- [1] racine, j., | STRING |
| HIGH | archive/examples/ex_mixed_lls_0.py | 0 | if z is not included in x: >>> np.dot(b_re.t, b_re)/100 array([[ 0.03270611, -0.00916051], [-0.00916051, 0.26432783]]) > | STRING |
| HIGH | archive/examples/ex_mixed_lls_timecorr.py | 0 | if z is not included in x: >>> np.dot(b_re.t, b_re)/100 array([[ 0.03270611, -0.00916051], [-0.00916051, 0.26432783]]) > | STRING |
| HIGH | archive/examples/ex_mixed_lls_re.py | 0 | if z is not included in x: >>> np.dot(b_re.t, b_re)/100 array([[ 0.03270611, -0.00916051], [-0.00916051, 0.26432783]]) > | STRING |
| HIGH | archive/examples/ex_kernel_semilinear_dgp.py | 0 | created on sun jan 06 09:50:54 2013 author: josef perktold | STRING |
| HIGH | archive/examples/ex_kernel_singleindex_dgp.py | 0 | created on sun jan 06 09:50:54 2013 author: josef perktold | STRING |
| HIGH | archive/examples/ex_kernel_regression_dgp.py | 0 | created on sun jan 06 09:50:54 2013 author: josef perktold | STRING |
| HIGH | archive/examples/ex_generic_mle_tdist.py | 0 | maximum likelihood estimation of poisson model this is an example for generic mle which has the same statistical model a | STRING |
| HIGH | archive/examples/ex_generic_mle_t.py | 0 | maximum likelihood estimation of poisson model this is an example for generic mle which has the same statistical model a | STRING |
| HIGH | statsmodels/miscmodels/count.py | 0 | maximum likelihood estimation of poisson model this is an example for generic mle which has the same statistical model a | STRING |
| HIGH | archive/distributions/transform_functions.py | 0 | class to hold quadratic function with inverse function and derivative using instance methods instead of class methods, i | STRING |
| HIGH | archive/distributions/transformed.py | 0 | class to hold quadratic function with inverse function and derivative using instance methods instead of class methods, i | STRING |
| HIGH | statsmodels/sandbox/distributions/extras.py | 0 | class to hold quadratic function with inverse function and derivative using instance methods instead of class methods, i | STRING |
| HIGH | statsmodels/discrete/discrete_model.py | 0 | derivative of the expected endog with respect to the parameters. parameters ---------- params : ndarray parameter at whi | STRING |
| HIGH | statsmodels/discrete/count_model.py | 0 | derivative of the expected endog with respect to the parameters. parameters ---------- params : ndarray parameter at whi | STRING |
| HIGH | statsmodels/genmod/generalized_linear_model.py | 0 | derivative of the expected endog with respect to the parameters. parameters ---------- params : ndarray parameter at whi | STRING |
| HIGH | statsmodels/discrete/discrete_model.py | 0 | generic censored model hessian matrix of the log-likelihood. parameters ---------- params : array_like the parameters of | STRING |
| HIGH | statsmodels/discrete/count_model.py | 0 | generic censored model hessian matrix of the log-likelihood. parameters ---------- params : array_like the parameters of | STRING |
| HIGH | statsmodels/discrete/truncated_model.py | 0 | generic censored model hessian matrix of the log-likelihood. parameters ---------- params : array_like the parameters of | STRING |
| HIGH | statsmodels/discrete/discrete_model.py | 0 | likelihood ratio chi-squared statistic; `-2*(llnull - llf)` | STRING |
| HIGH | statsmodels/miscmodels/ordinal_model.py | 0 | likelihood ratio chi-squared statistic; `-2*(llnull - llf)` | STRING |
| HIGH | statsmodels/base/model.py | 0 | likelihood ratio chi-squared statistic; `-2*(llnull - llf)` | STRING |
| HIGH | statsmodels/discrete/discrete_model.py | 0 | the chi-squared probability of getting a log-likelihood ratio statistic greater than llr. llr has a chi-squared distribu | STRING |
| HIGH | statsmodels/miscmodels/ordinal_model.py | 0 | the chi-squared probability of getting a log-likelihood ratio statistic greater than llr. llr has a chi-squared distribu | STRING |
| HIGH | statsmodels/base/model.py | 0 | the chi-squared probability of getting a log-likelihood ratio statistic greater than llr. llr has a chi-squared distribu | STRING |
| HIGH | statsmodels/discrete/conditional_models.py | 0 | summarize the regression results parameters ---------- yname : str, optional default is `y` xname : list of str, optiona | STRING |
| HIGH | statsmodels/genmod/generalized_estimating_equations.py | 0 | summarize the regression results parameters ---------- yname : str, optional default is `y` xname : list of str, optiona | STRING |
| HIGH | statsmodels/genmod/qif.py | 0 | summarize the regression results parameters ---------- yname : str, optional default is `y` xname : list of str, optiona | STRING |
| HIGH | statsmodels/genmod/generalized_linear_model.py | 0 | summarize the regression results parameters ---------- yname : str, optional default is `y` xname : list of str, optiona | STRING |
| HIGH | statsmodels/duration/hazard_regression.py | 0 | summarize the regression results parameters ---------- yname : str, optional default is `y` xname : list of str, optiona | STRING |
| HIGH | statsmodels/base/model.py | 0 | summarize the regression results parameters ---------- yname : str, optional default is `y` xname : list of str, optiona | STRING |
| HIGH | statsmodels/datasets/template_data.py | 0 | load the stack loss data and returns a dataset class instance. returns ------- dataset a `dataset` instance with ``data` | STRING |
| HIGH | statsmodels/datasets/engel/data.py | 0 | load the stack loss data and returns a dataset class instance. returns ------- dataset a `dataset` instance with ``data` | STRING |
| HIGH | statsmodels/datasets/cancer/data.py | 0 | load the stack loss data and returns a dataset class instance. returns ------- dataset a `dataset` instance with ``data` | STRING |
| HIGH | statsmodels/datasets/fair/data.py | 0 | load the stack loss data and returns a dataset class instance. returns ------- dataset a `dataset` instance with ``data` | STRING |
| HIGH | statsmodels/datasets/strikes/data.py | 0 | load the stack loss data and returns a dataset class instance. returns ------- dataset a `dataset` instance with ``data` | STRING |
| HIGH | statsmodels/datasets/statecrime/data.py | 0 | load the stack loss data and returns a dataset class instance. returns ------- dataset a `dataset` instance with ``data` | STRING |
| HIGH | statsmodels/datasets/heart/data.py | 0 | load the stack loss data and returns a dataset class instance. returns ------- dataset a `dataset` instance with ``data` | STRING |
| HIGH | statsmodels/datasets/modechoice/data.py | 0 | load the stack loss data and returns a dataset class instance. returns ------- dataset a `dataset` instance with ``data` | STRING |
| HIGH | statsmodels/datasets/scotland/data.py | 0 | load the stack loss data and returns a dataset class instance. returns ------- dataset a `dataset` instance with ``data` | STRING |
| HIGH | statsmodels/datasets/spector/data.py | 0 | load the stack loss data and returns a dataset class instance. returns ------- dataset a `dataset` instance with ``data` | STRING |
| HIGH | statsmodels/datasets/stackloss/data.py | 0 | load the stack loss data and returns a dataset class instance. returns ------- dataset a `dataset` instance with ``data` | STRING |
| HIGH | statsmodels/datasets/committee/data.py | 0 | used with express permission from the original author, who retains all rights. | STRING |
| HIGH | statsmodels/datasets/cpunish/data.py | 0 | used with express permission from the original author, who retains all rights. | STRING |
| HIGH | statsmodels/datasets/copper/data.py | 0 | used with express permission from the original author, who retains all rights. | STRING |
| HIGH | statsmodels/datasets/star98/data.py | 0 | used with express permission from the original author, who retains all rights. | STRING |
| HIGH | statsmodels/datasets/scotland/data.py | 0 | used with express permission from the original author, who retains all rights. | STRING |
| HIGH | statsmodels/datasets/committee/data.py | 0 | jeff gill's *generalized linear models: a unified approach* http://jgill.wustl.edu/research/books.html | STRING |
| HIGH | statsmodels/datasets/cpunish/data.py | 0 | jeff gill's *generalized linear models: a unified approach* http://jgill.wustl.edu/research/books.html | STRING |
| HIGH | statsmodels/datasets/copper/data.py | 0 | jeff gill's *generalized linear models: a unified approach* http://jgill.wustl.edu/research/books.html | STRING |
| HIGH | statsmodels/datasets/star98/data.py | 0 | jeff gill's *generalized linear models: a unified approach* http://jgill.wustl.edu/research/books.html | STRING |
| HIGH | statsmodels/datasets/scotland/data.py | 0 | jeff gill's *generalized linear models: a unified approach* http://jgill.wustl.edu/research/books.html | STRING |
| HIGH | statsmodels/datasets/committee/data.py | 0 | load the credit card data and returns a dataset class. returns ------- dataset a `dataset` instance with ``data``, ``nam | STRING |
| HIGH | statsmodels/datasets/cpunish/data.py | 0 | load the credit card data and returns a dataset class. returns ------- dataset a `dataset` instance with ``data``, ``nam | STRING |
| HIGH | statsmodels/datasets/anes96/data.py | 0 | load the credit card data and returns a dataset class. returns ------- dataset a `dataset` instance with ``data``, ``nam | STRING |
| HIGH | statsmodels/datasets/copper/data.py | 0 | load the credit card data and returns a dataset class. returns ------- dataset a `dataset` instance with ``data``, ``nam | STRING |
| HIGH | statsmodels/datasets/longley/data.py | 0 | load the credit card data and returns a dataset class. returns ------- dataset a `dataset` instance with ``data``, ``nam | STRING |
| HIGH | statsmodels/datasets/ccard/data.py | 0 | load the credit card data and returns a dataset class. returns ------- dataset a `dataset` instance with ``data``, ``nam | STRING |
| 44 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | archive/sysreg.py | 212 | if X is self.endog: # definitely not a robust check | CODE |
| MEDIUM | archive/infotheo.py | 132 | # TODO: looks okay but needs more robust tests for corner cases | COMMENT |
| MEDIUM | archive/panel/sandwich_covariance_generic.py | 123 | """simple white heteroscedasticity robust covariance | STRING |
| MEDIUM | archive/examples/ex_sandwich3.py | 59 | # From the last line we see that the cluster and year robust standard errors | COMMENT |
| MEDIUM | archive/examples/ex_sandwich2.py | 1 | """Cluster robust standard errors for OLS | STRING |
| MEDIUM | archive/examples/ex_sandwich2.py | 54 | # get cluster robust standard errors and compare with STATA | COMMENT |
| MEDIUM | archive/examples/ex_sandwich2.py | 118 | # We see that in this case the robust standard errors of the parameter estimates | COMMENT |
| MEDIUM | archive/examples/example_kde.py | 30 | # get bw first because they're not streamlined | COMMENT |
| MEDIUM | archive/examples/tut_ols_rlm_short.py | 49 | # compare with robust estimator | COMMENT |
| MEDIUM | archive/examples/tut_ols_rlm_short.py | 61 | # module sm.robust.scale for scale options | COMMENT |
| MEDIUM | archive/examples/ex_random_panel.py | 70 | # heteroscedasticity robust does not help | COMMENT |
| MEDIUM | archive/examples/ex_random_panel.py | 72 | # compare with cluster robust se | COMMENT |
| MEDIUM | archive/examples/bayesprior.py | 44 | # TODO: is this robust for differential entropy in this case? closed form or | COMMENT |
| MEDIUM | archive/distributions/sppatch.py | 153 | eps = 0 # is this robust ? | CODE |
| MEDIUM | archive/distributions/sppatch.py | 443 | # based on _drv2_moment(self, n, *args), but streamlined | COMMENT |
| MEDIUM | examples/python/stl_decomposition.py | 73 | # Setting `robust` uses a data-dependent weighting function that re- | COMMENT |
| MEDIUM | examples/python/stl_decomposition.py | 75 | # robust estimation allows the model to tolerate larger errors that are | COMMENT |
| MEDIUM | examples/python/stl_decomposition.py | 87 | # Next, we estimate the model with and without robust weighting. The | COMMENT |
| MEDIUM | examples/python/stl_decomposition.py | 89 | # 2008. The non-robust estimate places equal weights on all observations and | COMMENT |
| LOW | examples/python/formulas.py | 39 | # Alternatively, you can just use the `formula` namespace of the main | COMMENT |
| MEDIUM | examples/python/treatment_effect.py | 117 | # The doubly robust estimators `aipw`, `aipw-wls`, `ipw-ra` include both | COMMENT |
| MEDIUM | examples/python/treatment_effect.py | 160 | # `ipw` for example regression adjustment `ra` and double robust `ipw_ra`. | COMMENT |
| LOW | examples/python/statespace_news.py | 128 | # just call the `forecast` method with the number of forecasts you want to | COMMENT |
| LOW | examples/python/statespace_news.py | 514 | # For the updated dataset, we'll just add in the | COMMENT |
| LOW | examples/python/statespace_news.py | 636 | # just call the `summary_details` method directly. | COMMENT |
| MEDIUM | examples/python/gee_score_test_simulation.py | 16 | # This notebook uses simulation to demonstrate robust GEE score tests. | COMMENT |
| MEDIUM | examples/python/gee_score_test_simulation.py | 18 | # about the mean structure. The tests are robust to miss-specification of | COMMENT |
| MEDIUM | examples/python/gee_score_test_simulation.py | 92 | # The GEE score test is robust to dependence and overdispersion. Here we | COMMENT |
| MEDIUM | examples/python/statespace_dfm_coincident.py | 362 | # facilitate comparison | COMMENT |
| MEDIUM | examples/python/robust_models_0.py | 62 | # See ``help(sm.RLM.fit)`` for more options and ``module sm.robust.scale`` | COMMENT |
| MEDIUM | examples/python/robust_models_0.py | 101 | # Draw a plot to compare OLS estimates to the robust estimates: | COMMENT |
| MEDIUM | examples/python/robust_models_0.py | 138 | # Draw a plot to compare OLS estimates to the robust estimates: | COMMENT |
| MEDIUM | examples/python/postestimation_poisson.py | 99 | # form which is robust to unspecified heteroscedasticity or serial or | COMMENT |
| MEDIUM⚡ | examples/python/postestimation_poisson.py | 133 | # correlation robust covariance type. | COMMENT |
| MEDIUM⚡ | examples/python/postestimation_poisson.py | 134 | # It currently uses the same implementation and defaults for the robust | COMMENT |
| MEDIUM⚡ | examples/python/postestimation_poisson.py | 142 | # corrections for heteroscedasticity and correlation robust score tests. | COMMENT |
| MEDIUM | examples/python/postestimation_poisson.py | 385 | # generalized leverage instead of the diagonal of the hat matrix in linear | COMMENT |
| MEDIUM | examples/python/linear_regression_diagnostics_plots.py | 55 | # d. leverage | COMMENT |
| MEDIUM | examples/python/linear_regression_diagnostics_plots.py | 472 | # A high leverage guideline can also be displayed using the convention: | COMMENT |
| LOW | examples/python/statespace_tvpvar_mcmc_cfa.py | 480 | # so we just pass an empty list) | COMMENT |
| LOW | examples/python/statespace_tvpvar_mcmc_cfa.py | 658 | # models. Here we'll just use the [arviz](https://arviz- | COMMENT |
| MEDIUM | examples/python/robust_models_1.py | 33 | # * The robust estimates $\hat{\beta}$ are computed by the iteratively re- | COMMENT |
| MEDIUM⚡ | examples/python/robust_models_1.py | 146 | # * The mean is not a robust estimator of location | COMMENT |
| MEDIUM⚡ | examples/python/robust_models_1.py | 152 | # * The median, on the other hand, is a robust estimator with a breakdown | COMMENT |
| MEDIUM⚡ | examples/python/robust_models_1.py | 160 | # * The standard deviation is not robust | COMMENT |
| MEDIUM | examples/python/robust_models_1.py | 193 | # Another robust estimator of scale is the Interquartile Range (IQR) | COMMENT |
| MEDIUM | examples/python/robust_models_1.py | 208 | # The IQR is less robust than the MAD in the sense that it has a lower | COMMENT |
| MEDIUM | examples/python/robust_models_1.py | 214 | # Yet another robust estimator of scale is the $Q_n$ estimator, introduced | COMMENT |
| MEDIUM | examples/python/robust_models_1.py | 376 | # * Why? Because M-estimators are not robust to leverage points. | COMMENT |
| MEDIUM | examples/python/robust_models_1.py | 376 | # * Why? Because M-estimators are not robust to leverage points. | COMMENT |
| MEDIUM⚡ | examples/python/influence_glm_logit.py | 72 | # and large Cook's distance, but not a large leverage. Observation 13 has | COMMENT |
| MEDIUM⚡ | examples/python/influence_glm_logit.py | 73 | # the largest leverage but only small Cook's distance and not a large | COMMENT |
| MEDIUM | examples/python/mstl_decomposition.py | 35 | # (e.g., `robust`, `seasonal_deg`, etc.). See [STL docs](https://www.statsmo | COMMENT |
| MEDIUM | examples/python/regression_plots.py | 53 | # leverage of each observation as measured by the hat matrix. | COMMENT |
| MEDIUM⚡ | examples/python/regression_plots.py | 80 | # and reporter have low leverage but a large residual. <br /> | COMMENT |
| MEDIUM⚡ | examples/python/regression_plots.py | 81 | # RR.engineer has small residual and large leverage. Conductor and | COMMENT |
| MEDIUM⚡ | examples/python/regression_plots.py | 82 | # minister have both high leverage and large residuals, and, <br /> | COMMENT |
| MEDIUM | examples/python/regression_plots.py | 252 | # Closely related to the influence_plot is the leverage-resid<sup>2</sup> | COMMENT |
| MEDIUM⚡ | examples/python/regression_plots.py | 267 | # ### Using robust regression to correct for outliers. | COMMENT |
| MEDIUM⚡ | examples/python/regression_plots.py | 270 | # M-estimators are not robust to leverage points. MM-estimators should do | COMMENT |
| 119 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | tools/releasing/release_note.py | 143 | # Create a What's New Dictionary to automatically populate the template | COMMENT |
| MEDIUM | archive/panel/panelmod.py | 171 | # Initialize pandas objects. | COMMENT |
| MEDIUM | archive/examples/example_functional_plots.py | 20 | # Create a functional boxplot: | COMMENT |
| MEDIUM | archive/examples/example_functional_plots.py | 40 | # Create a rainbow plot: | COMMENT |
| MEDIUM | archive/examples/example_enhanced_boxplots.py | 25 | # Create a violin plot. | COMMENT |
| MEDIUM | archive/examples/example_enhanced_boxplots.py | 46 | # Create a bean plot. | COMMENT |
| MEDIUM | archive/examples/example_enhanced_boxplots.py | 67 | # Create a jitter plot. | COMMENT |
| MEDIUM | archive/examples/example_enhanced_boxplots.py | 89 | # Create an asymmetrical jitter plot. | COMMENT |
| MEDIUM | archive/examples/l1_demo/demo.py | 381 | # Create the probability of entering the different classes, | STRING |
| MEDIUM | archive/examples/l1_demo/demo.py | 387 | # Create the endog | STRING |
| MEDIUM | archive/examples/l1_demo/demo.py | 402 | # Create the probability of entering the different classes, | STRING |
| MEDIUM | archive/examples/l1_demo/demo.py | 406 | # Create the endog | STRING |
| MEDIUM | archive/examples/l1_demo/demo.py | 427 | # Create the noiseless exog | STRING |
| MEDIUM | docs/source/conf.py | 5 | # This file is execfile()d with the current directory set to its | COMMENT |
| MEDIUM | docs/source/plots/graphics_functional_rainbowplot.py | 19 | # Create a rainbow plot: | COMMENT |
| MEDIUM | docs/source/plots/graphics_functional_hdrboxplot.py | 11 | # Create a HDR functional boxplot. We see that the years 1982-83 and 1997-98 are | COMMENT |
| MEDIUM | docs/source/plots/graphics_plot_fit_ex.py | 26 | # Create a plot just for the variable 'Poverty': | COMMENT |
| MEDIUM | docs/source/plots/graphics_functional_fboxplot.py | 19 | # Create a functional boxplot. We see that the years 1982-83 and 1997-98 are | COMMENT |
| MEDIUM | examples/python/formulas.py | 155 | # Define a custom function: | COMMENT |
| MEDIUM | examples/python/plots_boxplots.py | 139 | # Create a violin plot. | COMMENT |
| MEDIUM | examples/python/plots_boxplots.py | 160 | # Create a bean plot. | COMMENT |
| MEDIUM | examples/python/plots_boxplots.py | 181 | # Create a jitter plot. | COMMENT |
| MEDIUM | examples/python/plots_boxplots.py | 203 | # Create an asymmetrical jitter plot. | COMMENT |
| MEDIUM⚡ | examples/python/kernel_density.py | 224 | # Create a figure | COMMENT |
| MEDIUM⚡ | examples/python/kernel_density.py | 230 | # Initialize the kernel object | COMMENT |
| MEDIUM⚡ | examples/python/kernel_density.py | 238 | # Create a subplot, set the title | COMMENT |
| MEDIUM⚡ | examples/python/kernel_density.py | 259 | # Create a figure | COMMENT |
| MEDIUM⚡ | examples/python/kernel_density.py | 265 | # Create a subplot, set the title | COMMENT |
| MEDIUM⚡ | examples/python/kernel_density.py | 272 | # Create the plot | COMMENT |
| MEDIUM | examples/python/statespace_sarimax_pymc.py | 111 | # Define the inflation series that we'll use in analysis | COMMENT |
| MEDIUM | examples/python/statespace_sarimax_pymc.py | 132 | # Create an SARIMAX model instance - here we use it to estimate | COMMENT |
| MEDIUM | examples/python/statespace_news.py | 127 | # Creating the forecasts from the results object `res` is easy - you can | COMMENT |
| MEDIUM | examples/python/statespace_news.py | 191 | # Create a new results object by passing the new observations to the | COMMENT |
| MEDIUM | examples/python/statespace_news.py | 586 | # Create the news results | COMMENT |
| MEDIUM | examples/python/statespace_dfm_coincident.py | 227 | # Create the model | COMMENT |
| MEDIUM | examples/python/statespace_dfm_coincident.py | 806 | # Create the model | COMMENT |
| MEDIUM | examples/python/theta-model.py | 67 | # This module is based on: | COMMENT |
| MEDIUM | examples/python/statespace_tvpvar_mcmc_cfa.py | 391 | # Create a matrix with [y_t' : y_{t-1}'] for t = 2, ..., T | COMMENT |
| MEDIUM | examples/python/statespace_tvpvar_mcmc_cfa.py | 443 | # The above class defined the state space model for any given dataset. Now | COMMENT |
| MEDIUM | examples/python/statespace_tvpvar_mcmc_cfa.py | 449 | # Create an instance of our TVPVAR class with our observed dataset y | COMMENT |
| MEDIUM | examples/python/predict.py | 54 | # ## Create a new sample of explanatory variables Xnew, predict and plot | COMMENT |
| MEDIUM | examples/python/mstl_decomposition.py | 72 | # ### Create a toy dataset with multiple seasonalities | COMMENT |
| MEDIUM | statsmodels/nonparametric/kernel_regression.py | 234 | # Create the matrix on p.492 in [7], after the multiplication w/ K_h,ij | COMMENT |
| MEDIUM | statsmodels/nonparametric/kernel_regression.py | 753 | # Create the matrix on p.492 in [7], after the multiplication w/ K_h,ij | COMMENT |
| MEDIUM | statsmodels/nonparametric/tests/test_lowess.py | 235 | # Create a curve that is easy to fit at first but gets | COMMENT |
| MEDIUM | statsmodels/datasets/utils.py | 318 | # The below function were taken from sklearn | COMMENT |
| MEDIUM | statsmodels/sandbox/stats/multicomp.py | 1607 | # Create the full d matrix given all known dij vals | COMMENT |
| MEDIUM | statsmodels/imputation/mice.py | 604 | # Create an indicator matrix for missing values. | COMMENT |
| MEDIUM | statsmodels/genmod/cov_struct.py | 1480 | # Create the pairs and order them | COMMENT |
| MEDIUM | …atsmodels/genmod/tests/gee_poisson_simulation_check.py | 78 | # Create an exog matrix so that E[Y] = log(dot(exog1, params)) | COMMENT |
| MEDIUM | statsmodels/tsa/statespace/structural.py | 553 | # Create a bitmask holding the level/trend specification | STRING |
| MEDIUM | statsmodels/tsa/statespace/structural.py | 562 | # Create the trend specification, if it was not given | STRING |
| MEDIUM | statsmodels/tsa/statespace/structural.py | 975 | # Create the starting parameter list | COMMENT |
| MEDIUM | statsmodels/tsa/statespace/structural.py | 1840 | # Create the model name | COMMENT |
| MEDIUM | statsmodels/tsa/statespace/_pykalman_smoother.py | 211 | # Create a temporary matrix | COMMENT |
| MEDIUM | statsmodels/tsa/statespace/varmax.py | 570 | # Create the state covariance matrix | STRING |
| MEDIUM | statsmodels/tsa/statespace/varmax.py | 647 | # Create the state covariance matrix | STRING |
| MEDIUM | statsmodels/tsa/statespace/varmax.py | 1158 | # Create the model name | STRING |
| MEDIUM | statsmodels/tsa/statespace/varmax.py | 1248 | # Create the table | STRING |
| MEDIUM | statsmodels/tsa/statespace/dynamic_factor.py | 1172 | # Create the new axis | STRING |
| 97 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
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| HIGH | tools/validate_docstrings.py | 416 | except TypeError: | CODE |
| HIGH | tools/validate_docstrings.py | 433 | except (OSError, TypeError): | CODE |
| HIGH | tools/validate_docstrings.py | 365 | except ImportError: | CODE |
| HIGH | tools/releasing/release_note.py | 98 | except GithubException: | CODE |
| HIGH | archive/tests/test_predict_functional.py | 10 | except ImportError: | CODE |
| HIGH | archive/examples/tests/test_notebooks.py | 18 | except ImportError: | CODE |
| HIGH | archive/regression/onewaygls.py | 118 | except (KeyError, ValueError): | CODE |
| HIGH | docs/source/conf.py | 61 | except ImportError as err: # noqa: F841 | CODE |
| HIGH | statsmodels/conftest.py | 116 | except ImportError: | CODE |
| HIGH | statsmodels/tools/_testing.py | 106 | except AttributeError: | CODE |
| HIGH | statsmodels/tools/validation/validation.py | 341 | except Exception: | CODE |
| HIGH | statsmodels/tools/validation/validation.py | 417 | except Exception: | CODE |
| HIGH | statsmodels/compat/patsy.py | 23 | except ImportError: | CODE |
| HIGH | statsmodels/discrete/discrete_model.py | 788 | except KeyError: | CODE |
| HIGH | statsmodels/discrete/conditional_models.py | 374 | except KeyError: | CODE |
| HIGH | statsmodels/discrete/conditional_models.py | 405 | except KeyError: | CODE |
| HIGH | statsmodels/discrete/tests/test_truncated_model.py | 552 | except ImportError: | CODE |
| HIGH | statsmodels/discrete/tests/test_predict.py | 413 | except AttributeError: | CODE |
| HIGH | statsmodels/datasets/utils.py | 81 | except Exception: | CODE |
| HIGH | statsmodels/datasets/utils.py | 222 | except Exception: | CODE |
| HIGH | statsmodels/iolib/summary.py | 379 | except (AttributeError, NotImplementedError): | CODE |
| HIGH | statsmodels/iolib/table.py | 813 | except AttributeError: | CODE |
| HIGH | statsmodels/iolib/table.py | 992 | except AttributeError: | CODE |
| HIGH | statsmodels/iolib/table.py | 997 | except AttributeError: | CODE |
| HIGH | statsmodels/iolib/table.py | 284 | except AttributeError: | CODE |
| HIGH | statsmodels/iolib/summary2.py | 400 | except (AttributeError, KeyError, NotImplementedError): | CODE |
| HIGH | statsmodels/iolib/tests/test_pickle.py | 35 | except OSError: | CODE |
| HIGH | statsmodels/iolib/tests/test_table_econpy.py | 63 | except TypeError: | CODE |
| HIGH | statsmodels/imputation/tests/test_mice.py | 16 | except ImportError: | CODE |
| HIGH | statsmodels/formula/_manager.py | 52 | except ImportError: | CODE |
| HIGH | statsmodels/formula/tests/test_manager.py | 17 | except ImportError: | CODE |
| HIGH | statsmodels/formula/tests/test_manager.py | 24 | except ImportError: | CODE |
| HIGH | statsmodels/genmod/generalized_estimating_equations.py | 595 | except ValueError: | CODE |
| HIGH | …tsmodels/genmod/tests/gee_gaussian_simulation_check.py | 245 | except TypeError: | CODE |
| HIGH | statsmodels/tsa/x13.py | 98 | except OSError: | CODE |
| HIGH | …smodels/tsa/statespace/tests/test_dynamic_factor_mq.py | 1771 | except ImportError: | CODE |
| HIGH | statsmodels/tsa/statespace/tests/test_structural.py | 117 | except ImportError: | CODE |
| HIGH | statsmodels/tsa/regime_switching/markov_switching.py | 1581 | except Exception: # FIXME: catch something specific | CODE |
| HIGH | statsmodels/tsa/tests/test_x13.py | 247 | except OSError: | CODE |
| HIGH | statsmodels/tsa/tests/test_exponential_smoothing.py | 623 | except ValueError: | CODE |
| HIGH | statsmodels/tsa/base/tests/test_tsa_indexes.py | 66 | except AttributeError: | CODE |
| HIGH | statsmodels/graphics/correlation.py | 118 | except AttributeError: | CODE |
| HIGH | statsmodels/graphics/tests/test_tsaplots.py | 35 | except ImportError: | CODE |
| HIGH | statsmodels/graphics/tests/test_mosaicplot.py | 17 | except ImportError: | CODE |
| HIGH | statsmodels/graphics/tests/test_regressionplots.py | 28 | except ImportError: | CODE |
| HIGH | statsmodels/graphics/tests/test_regressionplots.py | 113 | except AttributeError: | CODE |
| HIGH | statsmodels/graphics/tests/test_gofplots.py | 762 | except ImportError: | CODE |
| HIGH | statsmodels/graphics/tests/test_gofplots.py | 26 | except ImportError: | CODE |
| HIGH | statsmodels/graphics/tests/test_gofplots.py | 423 | except ImportError: | CODE |
| HIGH | statsmodels/graphics/tests/test_gofplots.py | 511 | except ImportError: | CODE |
| HIGH | statsmodels/graphics/tests/test_functional.py | 17 | except ImportError: | CODE |
| HIGH | statsmodels/graphics/tests/test_dotplot.py | 9 | except ImportError: | CODE |
| HIGH | statsmodels/graphics/tests/test_boxplots.py | 9 | except ImportError: | CODE |
| HIGH | statsmodels/graphics/tests/test_factorplots.py | 10 | except ImportError: | CODE |
| HIGH | statsmodels/duration/tests/test_survfunc.py | 17 | except ImportError: | CODE |
| HIGH | statsmodels/base/wrapper.py | 27 | except AttributeError: | CODE |
| HIGH | statsmodels/base/wrapper.py | 32 | except AttributeError: | CODE |
| HIGH | statsmodels/base/model.py | 151 | except KeyError: # panel already pops keys in data handling | CODE |
| HIGH | statsmodels/base/model.py | 2552 | except AttributeError: | CODE |
| HIGH | statsmodels/base/model.py | 2568 | except AttributeError: | CODE |
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| LOW | tools/validate_docstrings.py | 378 | CODE | |
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| LOW | tools/check_public_api_coverage.py | 84 | CODE | |
| LOW | tools/class_coverage_report.py | 124 | CODE | |
| LOW | tools/class_coverage_report.py | 194 | CODE | |
| LOW | tools/export_notebooks_to_python.py | 104 | CODE | |
| LOW | tools/releasing/enumerate-api.py | 65 | CODE | |
| LOW | tools/releasing/enumerate-api.py | 104 | CODE | |
| LOW | archive/sysreg.py | 166 | CODE | |
| LOW | archive/bspline.py | 35 | CODE | |
| LOW | archive/pca.py | 156 | CODE | |
| LOW | archive/gam.py | 64 | CODE | |
| LOW | archive/panel/panelmod.py | 210 | CODE | |
| LOW | archive/tests/test_predict_functional.py | 152 | CODE | |
| LOW | archive/examples/example_nbin.py | 128 | CODE | |
| LOW | archive/regression/ols_anova_original.py | 208 | CODE | |
| LOW | archive/regression/try_ols_anova.py | 125 | CODE | |
| LOW | archive/regression/treewalkerclass.py | 329 | CODE | |
| LOW | archive/distributions/genpareto.py | 108 | CODE | |
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| LOW | archive/stats/stats_dhuard.py | 123 | CODE | |
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| LOW | statsmodels/conftest.py | 169 | CODE | |
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| LOW | statsmodels/nonparametric/smoothers_lowess.py | 15 | CODE | |
| LOW | statsmodels/tools/web.py | 10 | CODE | |
| LOW | statsmodels/tools/rng_qrng.py | 7 | CODE | |
| LOW | statsmodels/tools/tools.py | 85 | CODE | |
| LOW | statsmodels/tools/rootfinding.py | 56 | CODE | |
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| LOW | statsmodels/tools/validation/validation.py | 304 | CODE | |
| LOW | statsmodels/tools/validation/validation.py | 375 | CODE | |
| LOW | statsmodels/compat/scipy.py | 156 | CODE | |
| LOW | statsmodels/multivariate/multivariate_ols.py | 286 | CODE | |
| LOW | statsmodels/multivariate/multivariate_ols.py | 459 | CODE | |
| LOW | statsmodels/multivariate/multivariate_ols.py | 622 | CODE | |
| LOW | statsmodels/multivariate/manova.py | 78 | CODE | |
| LOW | statsmodels/multivariate/pca.py | 335 | CODE | |
| LOW | statsmodels/multivariate/factor_rotation/_wrappers.py | 17 | CODE | |
| LOW | …tsmodels/multivariate/factor_rotation/_gpa_rotation.py | 29 | CODE | |
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| LOW | statsmodels/discrete/discrete_margins.py | 207 | CODE | |
| LOW | statsmodels/discrete/diagnostic.py | 174 | CODE | |
| LOW | statsmodels/discrete/count_model.py | 461 | CODE | |
| LOW | statsmodels/discrete/truncated_model.py | 302 | CODE | |
| LOW | statsmodels/discrete/truncated_model.py | 1568 | CODE | |
| LOW | statsmodels/datasets/utils.py | 396 | CODE | |
| LOW | statsmodels/miscmodels/ordinal_model.py | 126 | CODE | |
| LOW | statsmodels/iolib/summary.py | 735 | CODE | |
| LOW | statsmodels/iolib/summary.py | 781 | CODE | |
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| LOW | archive/mle.py | 25 | CODE | |
| LOW | archive/mle.py | 34 | CODE | |
| LOW | archive/nonparametric/densityorthopoly.py | 339 | CODE | |
| LOW | archive/examples/l1_demo/demo.py | 168 | CODE | |
| LOW | examples/python/statespace_sarimax_pymc.py | 224 | CODE | |
| LOW | examples/python/statespace_custom_models.py | 783 | CODE | |
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| LOW | statsmodels/robust/resistant_linear_model.py | 143 | CODE | |
| LOW | statsmodels/robust/robust_linear_model.py | 223 | CODE | |
| LOW | statsmodels/robust/scale.py | 704 | CODE | |
| LOW | statsmodels/robust/scale.py | 821 | CODE | |
| LOW | statsmodels/nonparametric/smoothers_lowess_old.py | 136 | CODE | |
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| LOW | statsmodels/multivariate/factor.py | 161 | CODE | |
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| LOW | archive/multilinear.py | 22 | logger = logging.getLogger(__name__) | CODE |
| LOW | archive/nonparametric/kernel_extras.py | 44 | __all__ = ["SemiLinear", "SingleIndexModel", "TestFForm"] | CODE |
| LOW | archive/panel/panelmod.py | 16 | __all__ = ["PanelModel"] | CODE |
| LOW | archive/stats/diagnostic.py | 25 | __all__ = [ | CODE |
| LOW | statsmodels/conftest.py | 20 | logger = logging.getLogger(__name__) | CODE |
| LOW | statsmodels/__init__.py | 29 | __all__ = ["__version__", "__version_info__", "__version_tuple__", "test"] | CODE |
| LOW | statsmodels/api.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/robust/__init__.py | 2 | __all__ = [ | CODE |
| LOW | statsmodels/robust/robust_linear_model.py | 29 | __all__ = ["RLM"] | CODE |
| LOW | statsmodels/nonparametric/kernel_regression.py | 55 | __all__ = ["KernelCensoredReg", "KernelReg"] | CODE |
| LOW | statsmodels/nonparametric/kernel_density.py | 47 | __all__ = ["EstimatorSettings", "KDEMultivariate", "KDEMultivariateConditional"] | CODE |
| LOW | statsmodels/nonparametric/api.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/tools/eval_measures.py | 682 | __all__ = [ | STRING |
| LOW | statsmodels/tools/__init__.py | 7 | __all__ = ["add_constant", "test"] | CODE |
| LOW | statsmodels/tools/linalg.py | 5 | __all__ = ["logdet_symm", "matrix_sqrt", "stationary_solve", "transf_constraints"] | CODE |
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| LOW | statsmodels/tools/typing.py | 34 | __all__ = [ | CODE |
| LOW | statsmodels/tools/validation/__init__.py | 14 | __all__ = [ | CODE |
| LOW | statsmodels/compat/matplotlib.py | 11 | __all__ = ["MPL_LT_310"] | CODE |
| LOW | statsmodels/compat/pandas.py | 27 | __all__ = [ | CODE |
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| LOW | statsmodels/compat/numpy.py | 45 | __all__ = [ | CODE |
| LOW | statsmodels/compat/platform.py | 7 | __all__ = [ | CODE |
| LOW | statsmodels/compat/pytest.py | 11 | __all__ = ["pytest_warns"] | CODE |
| LOW | statsmodels/compat/scipy.py | 274 | __all__ = [ | CODE |
| LOW | statsmodels/compat/python.py | 33 | __all__ = [ | CODE |
| LOW | statsmodels/multivariate/api.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/multivariate/factor_rotation/__init__.py | 29 | __all__ = [ | CODE |
| LOW | statsmodels/multivariate/factor_rotation/_wrappers.py | 14 | __all__ = [] | CODE |
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| LOW | statsmodels/datasets/__init__.py | 42 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/statecrime/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/committee/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/engel/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/strikes/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/cpunish/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/anes96/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/heart/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/modechoice/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/randhie/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/copper/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/fertility/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/elec_equip/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/sunspots/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/china_smoking/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/star98/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/macrodata/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/scotland/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/longley/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/spector/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/cancer/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/fair/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/nile/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/ccard/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/danish_data/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/stackloss/__init__.py | 1 | __all__ = [ | CODE |
| LOW | statsmodels/datasets/elnino/__init__.py | 1 | __all__ = [ | CODE |
| 66 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | archive/panel/mixed.py | 643 | # # note I have written this already as helper function, get it | COMMENT |
| HIGH | examples/python/glm.py | 31 | # In this example, we use the Star98 dataset which was taken with | COMMENT |
| HIGH | examples/python/glm_weights.py | 27 | # In this example, we'll use the affair dataset using a handful of | COMMENT |
| HIGH | examples/python/statespace_varmax.py | 95 | # uncorrelated. In this example we leave out the exogenous regressor but now | COMMENT |
| HIGH | examples/python/kernel_density.py | 34 | # In this example we will show the following: | COMMENT |
| HIGH | examples/python/statespace_news.py | 462 | # In this example, we'll consider forecasting monthly core price inflation | COMMENT |
| HIGH | examples/python/categorical_interaction_plot.py | 16 | # In this example, we will visualize the interaction between categorical | COMMENT |
| HIGH | examples/python/multivariate_ls.py | 153 | # In this example, the pvalues agree at 3 decimals. | COMMENT |
| HIGH | examples/python/wls.py | 54 | # In this example, `w` is the standard deviation of the error. `WLS` | COMMENT |
| HIGH⚡ | examples/python/influence_glm_logit.py | 71 | # In this example observation 4 and 18 have a large standardized residual | COMMENT |
| HIGH | examples/python/metaanalysis1.py | 153 | # In this example the effect size is the mean of measurements in a lab. We | COMMENT |
| HIGH | examples/python/rolling_ls.py | 149 | # available for the full window length. In this example, we start once we | COMMENT |
| HIGH | examples/python/ordinal_regression.py | 252 | # In this example, the created dummy variables `C(dummy)[0.0]` and | COMMENT |
| HIGH | examples/incomplete/ols_table.py | 13 | # Note: In this example using zscored/standardized variables has no effect on | COMMENT |
| HIGH | …statespace/tests/test_dynamic_factor_mq_monte_carlo.py | 490 | # In this example, both endog_M and endog_Q are equal to the factors, | COMMENT |
| HIGH | …statespace/tests/test_dynamic_factor_mq_monte_carlo.py | 652 | # In this example, both endog_M and endog_Q are equal to the factors, | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tools/validate_docstrings.py | 544 | except Exception as exc: | CODE |
| LOW | tools/check_public_api_coverage.py | 125 | except Exception: | CODE |
| LOW | tools/check_public_api_coverage.py | 155 | except Exception: | CODE |
| LOW | tools/check_public_api_coverage.py | 183 | except Exception: | CODE |
| LOW | tools/check_public_api_coverage.py | 250 | except Exception: | CODE |
| LOW | tools/check_public_api_coverage.py | 302 | except Exception: | CODE |
| MEDIUM | tools/check_public_api_coverage.py | 150 | def _srcinfo(obj): | CODE |
| LOW | tools/nbgenerate.py | 137 | except Exception as e: | STRING |
| LOW | tools/releasing/enumerate-api.py | 55 | except Exception: | CODE |
| LOW | tools/releasing/enumerate-api.py | 96 | except Exception: | CODE |
| LOW | archive/sysreg.py | 331 | except Exception as exc: | CODE |
| LOW | archive/examples/ex_emplike_2.py | 91 | except Exception: | CODE |
| LOW | archive/examples/try_tukey_hsd.py | 391 | except Exception as e: | CODE |
| LOW | archive/examples/run_all.py | 72 | except Exception: | CODE |
| LOW | archive/examples/example_sysreg.py | 159 | except Exception as exc: | CODE |
| LOW | archive/examples/thirdparty/ex_ratereturn.py | 23 | except Exception: # blanket for any unpickling error | CODE |
| MEDIUM | archive/examples/thirdparty/ex_ratereturn.py | 24 | print("Error with unpickling, a new pickle file can be created with findow_1") | CODE |
| LOW | archive/distributions/gof_new.py | 121 | except Exception: | CODE |
| LOW | archive/distributions/tests/check_moments.py | 102 | except Exception: | CODE |
| LOW | docs/source/conf.py | 511 | except Exception as exc: | STRING |
| LOW | examples/run_all.py | 38 | except Exception as e: | CODE |
| LOW | examples/python/statespace_sarimax_internet.py | 91 | except Exception: | CODE |
| LOW | examples/python/statespace_sarimax_internet.py | 101 | except Exception: | CODE |
| LOW | statsmodels/nonparametric/kde.py | 51 | except Exception as exc: | CODE |
| MEDIUM | statsmodels/nonparametric/kde.py | 48 | def _checkisfit(self): | CODE |
| LOW | statsmodels/tools/validation/validation.py | 298 | except Exception as exc: | CODE |
| LOW | statsmodels/tools/validation/validation.py | 341 | except Exception: | CODE |
| LOW | statsmodels/tools/validation/validation.py | 417 | except Exception: | CODE |
| LOW | statsmodels/compat/patsy.py | 149 | except Exception: | CODE |
| LOW | statsmodels/datasets/utils.py | 81 | except Exception: | CODE |
| LOW | statsmodels/datasets/utils.py | 222 | except Exception: | CODE |
| LOW | statsmodels/genmod/generalized_linear_model.py | 1265 | except Exception as exc: | STRING |
| LOW | statsmodels/tsa/arima_process.py | 897 | except Exception as exc: | STRING |
| LOW | statsmodels/tsa/x13.py | 591 | except Exception as os_err: | STRING |
| LOW | statsmodels/tsa/deterministic.py | 116 | except Exception as exc: | CODE |
| LOW | statsmodels/tsa/tsatools.py | 131 | except Exception: | CODE |
| MEDIUM | statsmodels/tsa/tsatools.py | 128 | def safe_is_const(s): | CODE |
| LOW | statsmodels/tsa/ar_model.py | 1887 | except Exception as exc: | CODE |
| LOW⚡ | statsmodels/tsa/statespace/mlemodel.py | 5577 | except Exception: # FIXME: catch something specific | STRING |
| LOW⚡ | statsmodels/tsa/statespace/mlemodel.py | 5581 | except Exception: # FIXME: catch something specific | STRING |
| LOW⚡ | statsmodels/tsa/statespace/mlemodel.py | 5585 | except Exception: # FIXME: catch something specific | STRING |
| LOW | statsmodels/tsa/vector_ar/util.py | 440 | except Exception: | CODE |
| LOW | statsmodels/tsa/regime_switching/markov_switching.py | 1581 | except Exception: # FIXME: catch something specific | CODE |
| LOW⚡ | statsmodels/tsa/arima/tests/test_model.py | 502 | except Exception as exc: | STRING |
| LOW⚡ | statsmodels/tsa/arima/tests/test_model.py | 512 | except Exception as exc: | STRING |
| LOW | statsmodels/tsa/ardl/model.py | 1220 | except Exception as exc: | CODE |
| LOW | statsmodels/tsa/stattools/_arma_order_selection.py | 41 | except Exception: # no idea what happened | CODE |
| LOW | statsmodels/tsa/holtwinters/model.py | 259 | except Exception as exc: | CODE |
| LOW⚡ | statsmodels/tsa/exponential_smoothing/base.py | 845 | except Exception: # FIXME: catch something specific | STRING |
| LOW⚡ | statsmodels/tsa/exponential_smoothing/base.py | 849 | except Exception: # FIXME: catch something specific | STRING |
| LOW⚡ | statsmodels/tsa/exponential_smoothing/base.py | 853 | except Exception: # FIXME: catch something specific | STRING |
| LOW | statsmodels/tsa/base/tsa_model.py | 572 | except Exception as exc: | CODE |
| LOW | statsmodels/graphics/gofplots.py | 249 | except Exception as exc: | CODE |
| LOW | statsmodels/graphics/gofplots.py | 281 | except Exception as exc: | CODE |
| LOW | statsmodels/base/model.py | 1290 | except Exception as exc: | CODE |
| LOW | statsmodels/base/tests/test_polars_compat.py | 290 | except Exception as e: | CODE |
| LOW | statsmodels/stats/tabledist.py | 94 | except Exception as exc: | CODE |
| LOW | statsmodels/stats/diagnostic.py | 1625 | except Exception as exc: | STRING |
| LOW | statsmodels/stats/libqsturng/qsturng_.py | 1915 | except Exception: | CODE |
| LOW | statsmodels/stats/libqsturng/qsturng_.py | 2099 | except Exception as e: | CODE |
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| Severity | File | Line | Snippet | Context |
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| MEDIUM | statsmodels/tools/tests/test_numdiff.py | 333 | CODE | |
| MEDIUM | statsmodels/tools/tests/test_numdiff.py | 334 | CODE | |
| MEDIUM | statsmodels/discrete/count_model.py | 659 | CODE | |
| MEDIUM | statsmodels/discrete/count_model.py | 660 | CODE | |
| MEDIUM | statsmodels/discrete/count_model.py | 662 | CODE | |
| MEDIUM | statsmodels/discrete/count_model.py | 672 | CODE | |
| MEDIUM | statsmodels/discrete/count_model.py | 674 | CODE | |
| MEDIUM | statsmodels/discrete/tests/test_truncated_model.py | 137 | CODE | |
| MEDIUM | statsmodels/discrete/tests/test_truncated_model.py | 178 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 862 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 863 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 865 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 866 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 868 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 869 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 870 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 874 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 875 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 876 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 877 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 878 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 879 | CODE | |
| MEDIUM | …dels/tsa/statespace/tests/test_simulation_smoothing.py | 880 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | examples/python/statespace_news.py | 105 | # #### Step 1: fitting the model on the available dataset | COMMENT |
| LOW | examples/python/statespace_news.py | 168 | # #### Step 2: computing the "news" from a new observation | COMMENT |
| LOW | examples/python/statespace_tvpvar_mcmc_cfa.py | 422 | # Step 3: Initialize the state vector as alpha_1 ~ N(0, 5I) | COMMENT |
| LOW | examples/python/statespace_forecasting.py | 190 | # Step 1: fit model parameters w/ training sample | COMMENT |
| LOW⚡ | examples/python/statespace_forecasting.py | 201 | # Step 2: produce one-step-ahead forecasts | COMMENT |
| LOW⚡ | examples/python/statespace_forecasting.py | 204 | # Step 3: compute root mean square forecasting error | COMMENT |
| LOW | examples/python/statespace_forecasting.py | 243 | # Step 1: append a new observation to the sample and refit the parameters | COMMENT |
| LOW⚡ | examples/python/statespace_forecasting.py | 256 | # Step 2: produce one-step-ahead forecasts | COMMENT |
| LOW⚡ | examples/python/statespace_forecasting.py | 259 | # Step 3: compute root mean square forecasting error | COMMENT |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 897 | # Step 1: convert from arbitrary matrices to those with singular values | STRING |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 902 | # Step 2: convert matrices from our "partial autocorrelation matrix" space | STRING |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 940 | # Step 1: convert from arbitrary matrices to those with singular values | STRING |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 946 | # Step 2: convert matrices from our "partial autocorrelation matrix" | STRING |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 1476 | # Step 1: convert matrices from the space of stationary | STRING |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 1482 | # Step 2: convert from arbitrary matrices to those with singular values | STRING |
| LOW⚡ | statsmodels/tsa/arima/estimators/hannan_rissanen.py | 191 | # Step 1: Compute long AR model via Yule-Walker, get residuals | COMMENT |
| LOW⚡ | statsmodels/tsa/arima/estimators/hannan_rissanen.py | 201 | # Step 2: estimate ARMA model via least squares | COMMENT |
| LOW | statsmodels/tsa/arima/estimators/hannan_rissanen.py | 247 | # Step 3: bias correction (if requested) | COMMENT |
| LOW | statsmodels/tsa/arima/estimators/gls.py | 186 | # Step 1: OLS | COMMENT |
| LOW | statsmodels/tsa/arima/estimators/gls.py | 211 | # Step 2 - 4: iterate feasible GLS to convergence | COMMENT |
| LOW⚡ | statsmodels/tsa/arima/estimators/gls.py | 232 | # Step 2: ARMA | COMMENT |
| LOW | statsmodels/tsa/arima/estimators/gls.py | 304 | # Step 3: GLS | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | examples/python/statespace_news.py | 105 | # #### Step 1: fitting the model on the available dataset | COMMENT |
| LOW | examples/python/statespace_news.py | 168 | # #### Step 2: computing the "news" from a new observation | COMMENT |
| LOW | examples/python/statespace_tvpvar_mcmc_cfa.py | 422 | # Step 3: Initialize the state vector as alpha_1 ~ N(0, 5I) | COMMENT |
| LOW | examples/python/statespace_forecasting.py | 190 | # Step 1: fit model parameters w/ training sample | COMMENT |
| LOW⚡ | examples/python/statespace_forecasting.py | 201 | # Step 2: produce one-step-ahead forecasts | COMMENT |
| LOW⚡ | examples/python/statespace_forecasting.py | 204 | # Step 3: compute root mean square forecasting error | COMMENT |
| LOW | examples/python/statespace_forecasting.py | 243 | # Step 1: append a new observation to the sample and refit the parameters | COMMENT |
| LOW⚡ | examples/python/statespace_forecasting.py | 256 | # Step 2: produce one-step-ahead forecasts | COMMENT |
| LOW⚡ | examples/python/statespace_forecasting.py | 259 | # Step 3: compute root mean square forecasting error | COMMENT |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 897 | # Step 1: convert from arbitrary matrices to those with singular values | STRING |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 902 | # Step 2: convert matrices from our "partial autocorrelation matrix" space | STRING |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 940 | # Step 1: convert from arbitrary matrices to those with singular values | STRING |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 946 | # Step 2: convert matrices from our "partial autocorrelation matrix" | STRING |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 1476 | # Step 1: convert matrices from the space of stationary | STRING |
| LOW⚡ | statsmodels/tsa/statespace/tools.py | 1482 | # Step 2: convert from arbitrary matrices to those with singular values | STRING |
| LOW⚡ | statsmodels/tsa/arima/estimators/hannan_rissanen.py | 191 | # Step 1: Compute long AR model via Yule-Walker, get residuals | COMMENT |
| LOW⚡ | statsmodels/tsa/arima/estimators/hannan_rissanen.py | 201 | # Step 2: estimate ARMA model via least squares | COMMENT |
| LOW | statsmodels/tsa/arima/estimators/hannan_rissanen.py | 247 | # Step 3: bias correction (if requested) | COMMENT |
| LOW | statsmodels/tsa/arima/estimators/gls.py | 186 | # Step 1: OLS | COMMENT |
| LOW⚡ | statsmodels/tsa/arima/estimators/gls.py | 232 | # Step 2: ARMA | COMMENT |
| LOW | statsmodels/tsa/arima/estimators/gls.py | 304 | # Step 3: GLS | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| CRITICAL | archive/mcevaluate/mcresuts_arma1.txt | 7 | scikits.statsmodels.tsa.arma_mle.Arma.fit((2,0,2)) | CODE |
| CRITICAL | statsmodels/stats/outliers_influence.py | 553 | # self.results.model.family.link.deriv(self.results.fittedvalues) | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | archive/examples/example_discrete_mnl.py | 21 | # However, you can use other solvers | COMMENT |
| MEDIUM | examples/python/formulas.py | 46 | # Or you can use the following convention | COMMENT |
| MEDIUM | examples/python/statespace_forecasting.py | 483 | # you can use a Pandas series with an associated frequency, you'll have more | COMMENT |
| MEDIUM | examples/python/regression_plots.py | 136 | # For a quick check of all the regressors, you can use | COMMENT |
| MEDIUM | examples/python/regression_plots.py | 138 | # points, but you can use them to identify problems and then use | COMMENT |
| LOW | examples/python/statespace_custom_models.py | 752 | # .html), so make sure to check that for more details. | COMMENT |
| MEDIUM | examples/python/glm_formula.py | 16 | # This notebook illustrates how you can use R-style formulas to fit | COMMENT |
| LOW | …smodels/regression/tests/results/results_regression.py | 184 | # from Stata, make sure you force double to replicate | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | archive/examples/l1_demo/short_demo.py | 48 | # Print results | COMMENT |
| LOW | archive/examples/l1_demo/short_demo.py | 75 | # Print results | COMMENT |
| LOW | …odels/genmod/tests/gee_categorical_simulation_check.py | 198 | # Loop over data generating models | COMMENT |
| LOW | statsmodels/genmod/tests/gee_simulation_check.py | 255 | # Loop over data generating models | COMMENT |
| LOW | …tsmodels/genmod/tests/gee_gaussian_simulation_check.py | 260 | # Loop over data generating models | COMMENT |
| LOW | …atsmodels/genmod/tests/gee_poisson_simulation_check.py | 186 | # Loop over data generating models | COMMENT |
| LOW | statsmodels/tsa/statespace/structural.py | 433 | # Check if any of the trend/level components have been set, and | STRING |
| LOW | statsmodels/tsa/statespace/tools.py | 234 | # Check if we just have a scalar polynomial | STRING |
| LOW | statsmodels/tsa/statespace/tools.py | 237 | # Check if 1 was passed as the first argument (indicating an | STRING |
| LOW | statsmodels/tsa/statespace/representation.py | 264 | # Check if k_endog is actually the endog array | STRING |
| LOW | statsmodels/tsa/statespace/dynamic_factor_mq.py | 2707 | # Print output every `disp` observations | STRING |
| LOW | statsmodels/tsa/regime_switching/markov_switching.py | 813 | # Check if we need to do weighted averaging | COMMENT |
| LOW | statsmodels/tsa/base/tsa_model.py | 212 | # Check if we now have a modified index | COMMENT |
| LOW | statsmodels/regression/process_regression.py | 973 | # Check if the group exists, since _groups_ix is a | COMMENT |
| LOW | statsmodels/duration/tests/results/phreg_gentests.py | 26 | # Set this to get a reasonable amount of censoring | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | statsmodels/imputation/mice.py | 611 | # Order the columns as requested | COMMENT |
| HIGH | statsmodels/imputation/mice.py | 627 | # Order the rows as requested | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | statsmodels/tsa/statespace/exponential_smoothing.py | 173 | # TODO: add validation for bounds (e.g., have all bounds, upper > lower) | COMMENT |
| LOW | statsmodels/regression/tests/test_lme.py | 34 | # TODO: add tests with unequal group sizes | COMMENT |
| LOW | statsmodels/regression/tests/test_robustcov.py | 32 | # TODO: implement test_hac_simple | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …s/genmod/tests/results/test_get_R_tweedie_var_weight.R | 4 | # Usage: | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | .github/workflows/ai-policy-check.yml | 63 | problem = "placeholder"; | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | statsmodels/tools/tests/test_web.py | 26 | def test_function(self): | CODE |