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libjs-requirejs, libjs-mathjax\n Suggests: python3-statsmodels, python3-doc, python-numpy-doc, python-patsy-doc, python-pandas-doc, python-scipy-doc\n Breaks: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc, python-statsmodels (<< 0.9.0-3~)\n Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc, python-statsmodels (<< 0.9.0-3~)\n Section: doc\n Priority: optional\n Homepage: https://www.statsmodels.org\n"}, {"source1": "./md5sums", "source2": "./md5sums", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "comments": ["Files differ"], "unified_diff": null}]}]}]}, {"source1": "data.tar.xz", "source2": "data.tar.xz", "unified_diff": null, "details": [{"source1": "data.tar", "source2": "data.tar", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -1180,49 +1180,49 @@\n drwxr-xr-x 0 root (0) root (0) 0 2025-08-10 13:13:47.000000 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13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/discrete_choice_example.ipynb.gz\n+-rw-r--r-- 0 root (0) root (0) 235914 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/discrete_choice_example.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 39453 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/discrete_choice_overview.html\n -rw-r--r-- 0 root (0) root (0) 4387 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/discrete_choice_overview.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 20786 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/distributed_estimation.html\n -rw-r--r-- 0 root (0) root (0) 1363 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/distributed_estimation.ipynb.gz\n -rw-r--r-- 0 root (0) 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./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/kernel_density.ipynb.gz\n@@ -7730,18 +7730,18 @@\n -rw-r--r-- 0 root (0) root (0) 55096 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/markov_regression.html\n -rw-r--r-- 0 root (0) root (0) 413018 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/markov_regression.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 25030 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/mediation_survival.html\n -rw-r--r-- 0 root (0) root (0) 1803 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/mediation_survival.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 87397 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/metaanalysis1.html\n -rw-r--r-- 0 root (0) root (0) 171326 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/metaanalysis1.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 42981 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/mixed_lm_example.html\n--rw-r--r-- 0 root (0) root (0) 92979 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/mixed_lm_example.ipynb.gz\n+-rw-r--r-- 0 root (0) root (0) 92980 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/mixed_lm_example.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 44594 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/mstl_decomposition.html\n -rw-r--r-- 0 root (0) root (0) 60510 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/ols.html\n--rw-r--r-- 0 root (0) root (0) 95456 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/ols.ipynb.gz\n+-rw-r--r-- 0 root (0) root (0) 95449 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/ols.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 50631 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/ordinal_regression.html\n -rw-r--r-- 0 root (0) root (0) 39581 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/pca_fertility_factors.html\n -rw-r--r-- 0 root (0) root (0) 350167 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/pca_fertility_factors.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 46200 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/plots_boxplots.html\n -rw-r--r-- 0 root (0) root (0) 1281909 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/plots_boxplots.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 81065 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/postestimation_poisson.html\n -rw-r--r-- 0 root (0) root (0) 523728 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/postestimation_poisson.ipynb.gz\n@@ -7755,15 +7755,15 @@\n -rw-r--r-- 0 root (0) root (0) 25934 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/regression_diagnostics.html\n -rw-r--r-- 0 root (0) root (0) 32646 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/regression_diagnostics.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 58473 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/regression_plots.html\n -rw-r--r-- 0 root (0) root (0) 1170796 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/regression_plots.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 35954 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/robust_models_0.html\n -rw-r--r-- 0 root (0) root (0) 107466 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/robust_models_0.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 121850 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/robust_models_1.html\n--rw-r--r-- 0 root (0) root (0) 613174 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/robust_models_1.ipynb.gz\n+-rw-r--r-- 0 root (0) root (0) 613182 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/robust_models_1.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 25914 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/rolling_ls.html\n -rw-r--r-- 0 root (0) root (0) 36274 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_arma_0.html\n -rw-r--r-- 0 root (0) root (0) 283280 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_arma_0.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 36077 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_chandrasekhar.html\n -rw-r--r-- 0 root (0) root (0) 44414 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_concentrated_scale.html\n -rw-r--r-- 0 root (0) root (0) 4269 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_concentrated_scale.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 123185 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_custom_models.html\n@@ -7771,15 +7771,15 @@\n -rw-r--r-- 0 root (0) root (0) 87436 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_dfm_coincident.html\n -rw-r--r-- 0 root (0) root (0) 29973 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_fixed_params.html\n -rw-r--r-- 0 root (0) root (0) 79051 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_forecasting.html\n -rw-r--r-- 0 root (0) root (0) 106432 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_forecasting.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 35138 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_local_linear_trend.html\n -rw-r--r-- 0 root (0) root (0) 66879 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_news.html\n -rw-r--r-- 0 root (0) root (0) 107985 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_sarimax_faq.html\n--rw-r--r-- 0 root (0) root (0) 41358 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_sarimax_faq.ipynb.gz\n+-rw-r--r-- 0 root (0) root (0) 41353 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_sarimax_faq.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 27702 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_sarimax_internet.html\n -rw-r--r-- 0 root (0) root (0) 59589 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_sarimax_pymc3.html\n -rw-r--r-- 0 root (0) root (0) 72003 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_sarimax_stata.html\n -rw-r--r-- 0 root (0) root (0) 74954 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_seasonal.html\n -rw-r--r-- 0 root (0) root (0) 1313992 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_seasonal.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 45205 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_structural_harvey_jaeger.html\n -rw-r--r-- 0 root (0) root (0) 87047 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_tvpvar_mcmc_cfa.html\n@@ -7792,15 +7792,15 @@\n -rw-r--r-- 0 root (0) root (0) 6766 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/stats_rankcompare.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 62608 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/stl_decomposition.html\n -rw-r--r-- 0 root (0) root (0) 1531977 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/stl_decomposition.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 33287 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/theta-model.html\n -rw-r--r-- 0 root (0) root (0) 52442 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/treatment_effect.html\n -rw-r--r-- 0 root (0) root (0) 7250 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/treatment_effect.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 61202 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_arma_0.html\n--rw-r--r-- 0 root (0) root (0) 393421 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_arma_0.ipynb.gz\n+-rw-r--r-- 0 root (0) root (0) 393424 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_arma_0.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 17787 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_arma_1.html\n -rw-r--r-- 0 root (0) root (0) 61249 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_arma_1.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 15524 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_dates.html\n -rw-r--r-- 0 root (0) root (0) 68631 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_dates.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 36151 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_filters.html\n -rw-r--r-- 0 root (0) root (0) 268103 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_filters.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 42766 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/variance_components.html\n@@ -14003,15 +14003,15 @@\n -rw-r--r-- 0 root (0) root (0) 27594 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/release/version0.7.html\n -rw-r--r-- 0 root (0) root (0) 23354 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/release/version0.8.html\n -rw-r--r-- 0 root (0) root (0) 28739 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/release/version0.9.html\n -rw-r--r-- 0 root (0) root (0) 19857 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/rlm.html\n -rw-r--r-- 0 root (0) root (0) 7250 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/rlm_techn1.html\n -rw-r--r-- 0 root (0) root (0) 24328 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/sandbox.html\n -rw-r--r-- 0 root (0) root (0) 5526 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/search.html\n--rw-r--r-- 0 root (0) root (0) 5164852 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/searchindex.js\n+-rw-r--r-- 0 root (0) root (0) 5164870 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/searchindex.js\n -rw-r--r-- 0 root (0) root (0) 112815 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/statespace.html\n -rw-r--r-- 0 root (0) root (0) 129649 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/stats.html\n -rw-r--r-- 0 root (0) root (0) 23215 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/tools.html\n -rw-r--r-- 0 root (0) root (0) 9553 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/treatment.html\n -rw-r--r-- 0 root (0) root (0) 69564 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/tsa.html\n -rw-r--r-- 0 root (0) root (0) 13463 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/user-guide.html\n -rw-r--r-- 0 root (0) root (0) 60604 2025-08-10 13:13:47.000000 ./usr/share/doc/python-statsmodels-doc/html/vector_ar.html\n"}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/discrete_choice_example.ipynb.txt", "source2": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/discrete_choice_example.ipynb.txt", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -1216,15 +1216,22 @@\n \"execution\": {}\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n- \"-11.8863%\\n\"\n+ \"-11.8863%\"\n+ ]\n+ },\n+ {\n+ \"name\": \"stdout\",\n+ \"output_type\": \"stream\",\n+ \"text\": [\n+ \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"%2.4f%%\\\" % (diff[0] * 100))\"\n ]\n },\n"}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/glm_weights.ipynb.txt", "source2": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/glm_weights.ipynb.txt", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.999749177631579%", "Differences: {\"'cells'\": \"{21: {'outputs': {0: {'text': {insert: [(18, \"", " \"'=================================================================================')], \"", " \"delete: [18]}}, insert: [(1, OrderedDict({'name': 'stdout', 'output_type': 'stream', \"", " \"'text': ['\\\\n']}))]}}, 27: {'outputs': {0: {'text': {insert: [(18, \"", " \"'=================================================================================\\\\n')], \"", " \"delete: [18]}}, delete: [1]}}, 34: {'outputs': {0: [\u2026]"], "unified_diff": "@@ -911,15 +911,22 @@\n \"=================================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"---------------------------------------------------------------------------------\\n\",\n \"Intercept 2.7155 0.107 25.294 0.000 2.505 2.926\\n\",\n \"rate_marriage -0.4952 0.012 -41.702 0.000 -0.518 -0.472\\n\",\n \"age -0.0299 0.004 -6.691 0.000 -0.039 -0.021\\n\",\n \"yrs_married -0.0108 0.004 -2.507 0.012 -0.019 -0.002\\n\",\n- \"=================================================================================\\n\"\n+ \"=================================================================================\"\n+ ]\n+ },\n+ {\n+ \"name\": \"stdout\",\n+ \"output_type\": \"stream\",\n+ \"text\": [\n+ \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"glm = smf.glm(\\n\",\n \" \\\"affairs ~ rate_marriage + age + yrs_married\\\",\\n\",\n \" data=data,\\n\",\n@@ -1075,22 +1082,15 @@\n \"=================================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"---------------------------------------------------------------------------------\\n\",\n \"Intercept 2.7155 0.107 25.294 0.000 2.505 2.926\\n\",\n \"rate_marriage -0.4952 0.012 -41.702 0.000 -0.518 -0.472\\n\",\n \"age -0.0299 0.004 -6.691 0.000 -0.039 -0.021\\n\",\n \"yrs_married -0.0108 0.004 -2.507 0.012 -0.019 -0.002\\n\",\n- \"=================================================================================\"\n- ]\n- },\n- {\n- \"name\": \"stdout\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"\\n\"\n+ \"=================================================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"glm = smf.glm(\\n\",\n \" \\\"affairs ~ rate_marriage + age + yrs_married\\\",\\n\",\n \" data=dc,\\n\",\n@@ -1265,22 +1265,15 @@\n \"=================================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"---------------------------------------------------------------------------------\\n\",\n \"Intercept 2.7155 0.107 25.294 0.000 2.505 2.926\\n\",\n \"rate_marriage -0.4952 0.012 -41.702 0.000 -0.518 -0.472\\n\",\n \"age -0.0299 0.004 -6.691 0.000 -0.039 -0.021\\n\",\n \"yrs_married -0.0108 0.004 -2.507 0.012 -0.019 -0.002\\n\",\n- \"=================================================================================\"\n- ]\n- },\n- {\n- \"name\": \"stdout\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"\\n\"\n+ \"=================================================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"glm = smf.glm(\\n\",\n \" \\\"affairs_mean ~ rate_marriage + age + yrs_married\\\",\\n\",\n \" data=df_a,\\n\",\n"}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/mixed_lm_example.ipynb.txt", "source2": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/mixed_lm_example.ipynb.txt", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -577,14 +577,22 @@\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"execution\": {}\n },\n \"outputs\": [\n {\n+ \"name\": \"stderr\",\n+ \"output_type\": \"stream\",\n+ \"text\": [\n+ \"/usr/lib/python3/dist-packages/statsmodels/regression/mixed_linear_model.py:2237: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\\n\",\n+ \" warnings.warn(msg, ConvergenceWarning)\\n\"\n+ ]\n+ },\n+ {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" Mixed Linear Model Regression Results\\n\",\n \"===============================================================\\n\",\n \"Model: MixedLM Dependent Variable: size \\n\",\n \"No. Observations: 395 Method: REML \\n\",\n@@ -599,22 +607,14 @@\n \"Time 0.013 0.000 33.888 0.000 0.012 0.013\\n\",\n \"Intercept Var 0.646 0.914 \\n\",\n \"Intercept x Time Cov -0.001 0.003 \\n\",\n \"Time Var 0.000 0.000 \\n\",\n \"===============================================================\\n\",\n \"\\n\"\n ]\n- },\n- {\n- \"name\": \"stderr\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"/usr/lib/python3/dist-packages/statsmodels/regression/mixed_linear_model.py:2237: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\\n\",\n- \" warnings.warn(msg, ConvergenceWarning)\\n\"\n- ]\n }\n ],\n \"source\": [\n \"exog_re = exog.copy()\\n\",\n \"md = sm.MixedLM(endog, exog, data[\\\"tree\\\"], exog_re)\\n\",\n \"mdf = md.fit()\\n\",\n \"print(mdf.summary())\"\n"}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/ols.ipynb.txt", "source2": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/ols.ipynb.txt", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -870,22 +870,15 @@\n \"8 -0.004019 -0.045687 0.023708 0.018125 0.013683 -0.034770 0.005116\\n\",\n \"9 -1.018242 -0.282131 -0.412621 -0.663904 -0.715020 -0.229501 1.035723\\n\",\n \"10 0.030947 -0.024781 0.029480 0.035361 0.034508 -0.014194 -0.030805\\n\",\n \"11 0.005987 -0.079727 0.030276 -0.008883 -0.006854 -0.010693 -0.005323\\n\",\n \"12 -0.135883 0.092325 -0.253027 -0.211465 0.094720 0.331351 0.129120\\n\",\n \"13 0.032736 -0.024249 0.017510 0.033242 0.090655 0.007634 -0.033114\\n\",\n \"14 0.305868 0.148070 0.001428 0.169314 0.253431 0.342982 -0.318031\\n\",\n- \"15 -0.538323 0.432004 -0.261262 -0.143444 -0.360890 -0.467296 0.552421\"\n- ]\n- },\n- {\n- \"name\": \"stdout\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"\\n\"\n+ \"15 -0.538323 0.432004 -0.261262 -0.143444 -0.360890 -0.467296 0.552421\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(infl.summary_frame().filter(regex=\\\"dfb\\\"))\"\n ]\n }\n"}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/robust_models_1.ipynb.txt", "source2": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/robust_models_1.ipynb.txt", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -1531,15 +1531,22 @@\n \"truck.driver -0.129227 0.897810 0.999436\\n\",\n \"cook 0.127207 0.899399 0.999436\\n\",\n \"janitor -0.079890 0.936713 0.999436\\n\",\n \"policeman 0.078847 0.937538 0.999436\\n\",\n \"architect 0.072256 0.942750 0.999436\\n\",\n \"teacher 0.050510 0.959961 0.999436\\n\",\n \"taxi.driver 0.023322 0.981507 0.999436\\n\",\n- \"author 0.000711 0.999436 0.999436\\n\"\n+ \"author 0.000711 0.999436 0.999436\"\n+ ]\n+ },\n+ {\n+ \"name\": \"stdout\",\n+ \"output_type\": \"stream\",\n+ \"text\": [\n+ \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"fdr = ols_model.outlier_test(\\\"fdr_bh\\\")\\n\",\n \"fdr.sort_values(\\\"unadj_p\\\", inplace=True)\\n\",\n \"print(fdr)\"\n"}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/statespace_sarimax_faq.ipynb.txt", "source2": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/statespace_sarimax_faq.ipynb.txt", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -1683,22 +1683,15 @@\n \"Ljung-Box (L1) (Q): 0.33 Jarque-Bera (JB): 0.15\\n\",\n \"Prob(Q): 0.57 Prob(JB): 0.93\\n\",\n \"Heteroskedasticity (H): 0.97 Skew: -0.01\\n\",\n \"Prob(H) (two-sided): 0.53 Kurtosis: 3.00\\n\",\n \"===================================================================================\\n\",\n \"\\n\",\n \"Warnings:\\n\",\n- \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\"\n- ]\n- },\n- {\n- \"name\": \"stdout\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"\\n\"\n+ \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(arx_res.summary())\"\n ]\n },\n"}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/tsa_arma_0.ipynb.txt", "source2": "./usr/share/doc/python-statsmodels-doc/html/_sources/examples/notebooks/generated/tsa_arma_0.ipynb.txt", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -766,14 +766,24 @@\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"execution\": {}\n },\n \"outputs\": [\n {\n+ \"name\": \"stderr\",\n+ \"output_type\": \"stream\",\n+ \"text\": [\n+ \"/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\\n\",\n+ \" warn('Non-stationary starting autoregressive parameters'\\n\",\n+ \"/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\\n\",\n+ \" warn('Non-invertible starting MA parameters found.'\\n\"\n+ ]\n+ },\n+ {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" AC Q Prob(>Q)\\n\",\n \"lag \\n\",\n \"1.0 -0.001244 0.000778 9.777436e-01\\n\",\n \"2.0 0.052350 1.382049 5.010626e-01\\n\",\n@@ -798,24 +808,14 @@\n \"21.0 -0.016212 330.713971 1.708363e-57\\n\",\n \"22.0 0.054804 332.291098 3.279536e-57\\n\",\n \"23.0 -0.110592 338.726892 6.325402e-58\\n\",\n \"24.0 0.022742 338.999620 2.166837e-57\\n\",\n \"25.0 0.029459 339.458216 6.665490e-57\\n\",\n \"26.0 0.095294 344.266902 2.658405e-57\\n\"\n ]\n- },\n- {\n- \"name\": \"stderr\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\\n\",\n- \" warn('Non-stationary starting autoregressive parameters'\\n\",\n- \"/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\\n\",\n- \" warn('Non-invertible starting MA parameters found.'\\n\"\n- ]\n }\n ],\n \"source\": [\n \"lags = int(10 * np.log10(arma_rvs.shape[0]))\\n\",\n \"arma11 = ARIMA(arma_rvs, order=(1, 0, 1)).fit()\\n\",\n \"resid = arma11.resid\\n\",\n \"r, q, p = sm.tsa.acf(resid, nlags=lags, fft=True, qstat=True)\\n\",\n"}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/discrete_choice_example.ipynb.gz", "source2": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/discrete_choice_example.ipynb.gz", "unified_diff": null, "details": [{"source1": "discrete_choice_example.ipynb", "source2": "discrete_choice_example.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -1216,15 +1216,22 @@\n \"execution\": {}\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n- \"-11.8863%\\n\"\n+ \"-11.8863%\"\n+ ]\n+ },\n+ {\n+ \"name\": \"stdout\",\n+ \"output_type\": \"stream\",\n+ \"text\": [\n+ \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"%2.4f%%\\\" % (diff[0] * 100))\"\n ]\n },\n"}]}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/glm_weights.ipynb.gz", "source2": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/glm_weights.ipynb.gz", "unified_diff": null, "details": [{"source1": "glm_weights.ipynb", "source2": "glm_weights.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.999749177631579%", "Differences: {\"'cells'\": \"{21: {'outputs': {0: {'text': {insert: [(18, \"", " \"'=================================================================================')], \"", " \"delete: [18]}}, insert: [(1, OrderedDict({'name': 'stdout', 'output_type': 'stream', \"", " \"'text': ['\\\\n']}))]}}, 27: {'outputs': {0: {'text': {insert: [(18, \"", " \"'=================================================================================\\\\n')], \"", " \"delete: [18]}}, delete: [1]}}, 34: {'outputs': {0: [\u2026]"], "unified_diff": "@@ -911,15 +911,22 @@\n \"=================================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"---------------------------------------------------------------------------------\\n\",\n \"Intercept 2.7155 0.107 25.294 0.000 2.505 2.926\\n\",\n \"rate_marriage -0.4952 0.012 -41.702 0.000 -0.518 -0.472\\n\",\n \"age -0.0299 0.004 -6.691 0.000 -0.039 -0.021\\n\",\n \"yrs_married -0.0108 0.004 -2.507 0.012 -0.019 -0.002\\n\",\n- \"=================================================================================\\n\"\n+ \"=================================================================================\"\n+ ]\n+ },\n+ {\n+ \"name\": \"stdout\",\n+ \"output_type\": \"stream\",\n+ \"text\": [\n+ \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"glm = smf.glm(\\n\",\n \" \\\"affairs ~ rate_marriage + age + yrs_married\\\",\\n\",\n \" data=data,\\n\",\n@@ -1075,22 +1082,15 @@\n \"=================================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"---------------------------------------------------------------------------------\\n\",\n \"Intercept 2.7155 0.107 25.294 0.000 2.505 2.926\\n\",\n \"rate_marriage -0.4952 0.012 -41.702 0.000 -0.518 -0.472\\n\",\n \"age -0.0299 0.004 -6.691 0.000 -0.039 -0.021\\n\",\n \"yrs_married -0.0108 0.004 -2.507 0.012 -0.019 -0.002\\n\",\n- \"=================================================================================\"\n- ]\n- },\n- {\n- \"name\": \"stdout\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"\\n\"\n+ \"=================================================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"glm = smf.glm(\\n\",\n \" \\\"affairs ~ rate_marriage + age + yrs_married\\\",\\n\",\n \" data=dc,\\n\",\n@@ -1265,22 +1265,15 @@\n \"=================================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"---------------------------------------------------------------------------------\\n\",\n \"Intercept 2.7155 0.107 25.294 0.000 2.505 2.926\\n\",\n \"rate_marriage -0.4952 0.012 -41.702 0.000 -0.518 -0.472\\n\",\n \"age -0.0299 0.004 -6.691 0.000 -0.039 -0.021\\n\",\n \"yrs_married -0.0108 0.004 -2.507 0.012 -0.019 -0.002\\n\",\n- \"=================================================================================\"\n- ]\n- },\n- {\n- \"name\": \"stdout\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"\\n\"\n+ \"=================================================================================\\n\"\n ]\n }\n ],\n \"source\": [\n \"glm = smf.glm(\\n\",\n \" \\\"affairs_mean ~ rate_marriage + age + yrs_married\\\",\\n\",\n \" data=df_a,\\n\",\n"}]}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/mixed_lm_example.html", "source2": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/mixed_lm_example.html", "comments": ["Ordering differences only"], "unified_diff": "@@ -449,14 +449,23 @@\n print(mdf.summary())\n \n \n \n
\n
\n
\n+
\n+
\n+/usr/lib/python3/dist-packages/statsmodels/regression/mixed_linear_model.py:2237: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n+  warnings.warn(msg, ConvergenceWarning)\n+
\n+
\n+
\n+
\n+
\n
\n 
\n              Mixed Linear Model Regression Results\n ===============================================================\n Model:               MixedLM    Dependent Variable:    size\n No. Observations:    395        Method:                REML\n No. Groups:          79         Scale:                 0.0264\n@@ -471,23 +480,14 @@\n Intercept Var         0.646    0.914\n Intercept x Time Cov -0.001    0.003\n Time Var              0.000    0.000\n ===============================================================\n \n
\n
\n-
\n-
\n-
\n-
\n-
\n-/usr/lib/python3/dist-packages/statsmodels/regression/mixed_linear_model.py:2237: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n-  warnings.warn(msg, ConvergenceWarning)\n-
\n-
\n

We can further explore the random effects structure by constructing plots of the profile likelihoods. We start with the random intercept, generating a plot of the profile likelihood from 0.1 units below to 0.1 units above the MLE. Since each optimization inside the profile likelihood generates a warning (due to the random slope variance being close to zero), we turn off the warnings here.

\n
\n 
[9]:\n
\n
\n 
import warnings\n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -316,14 +316,18 @@\n being on the boundary of the parameter space. The regression slopes agree very\n well with R, but the likelihood value is much higher than that returned by R.\n [8]:\n exog_re = exog.copy()\n md = sm.MixedLM(endog, exog, data[\"tree\"], exog_re)\n mdf = md.fit()\n print(mdf.summary())\n+/usr/lib/python3/dist-packages/statsmodels/regression/mixed_linear_model.py:\n+2237: ConvergenceWarning: The MLE may be on the boundary of the parameter\n+space.\n+  warnings.warn(msg, ConvergenceWarning)\n              Mixed Linear Model Regression Results\n ===============================================================\n Model:               MixedLM    Dependent Variable:    size\n No. Observations:    395        Method:                REML\n No. Groups:          79         Scale:                 0.0264\n Min. group size:     5          Log-Likelihood:        -62.4834\n Max. group size:     5          Converged:             Yes\n@@ -333,18 +337,14 @@\n ---------------------------------------------------------------\n Intercept             2.273    0.101 22.513 0.000  2.075  2.471\n Time                  0.013    0.000 33.888 0.000  0.012  0.013\n Intercept Var         0.646    0.914\n Intercept x Time Cov -0.001    0.003\n Time Var              0.000    0.000\n ===============================================================\n-/usr/lib/python3/dist-packages/statsmodels/regression/mixed_linear_model.py:\n-2237: ConvergenceWarning: The MLE may be on the boundary of the parameter\n-space.\n-  warnings.warn(msg, ConvergenceWarning)\n We can further explore the random effects structure by constructing plots of\n the profile likelihoods. We start with the random intercept, generating a plot\n of the profile likelihood from 0.1 units below to 0.1 units above the MLE.\n Since each optimization inside the profile likelihood generates a warning (due\n to the random slope variance being close to zero), we turn off the warnings\n here.\n [9]:\n"}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/mixed_lm_example.ipynb.gz", "source2": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/mixed_lm_example.ipynb.gz", "unified_diff": null, "details": [{"source1": "mixed_lm_example.ipynb", "source2": "mixed_lm_example.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -577,14 +577,22 @@\n             \"cell_type\": \"code\",\n             \"execution_count\": 8,\n             \"metadata\": {\n                 \"execution\": {}\n             },\n             \"outputs\": [\n                 {\n+                    \"name\": \"stderr\",\n+                    \"output_type\": \"stream\",\n+                    \"text\": [\n+                        \"/usr/lib/python3/dist-packages/statsmodels/regression/mixed_linear_model.py:2237: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\\n\",\n+                        \"  warnings.warn(msg, ConvergenceWarning)\\n\"\n+                    ]\n+                },\n+                {\n                     \"name\": \"stdout\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n                         \"             Mixed Linear Model Regression Results\\n\",\n                         \"===============================================================\\n\",\n                         \"Model:               MixedLM    Dependent Variable:    size    \\n\",\n                         \"No. Observations:    395        Method:                REML    \\n\",\n@@ -599,22 +607,14 @@\n                         \"Time                  0.013    0.000 33.888 0.000  0.012  0.013\\n\",\n                         \"Intercept Var         0.646    0.914                           \\n\",\n                         \"Intercept x Time Cov -0.001    0.003                           \\n\",\n                         \"Time Var              0.000    0.000                           \\n\",\n                         \"===============================================================\\n\",\n                         \"\\n\"\n                     ]\n-                },\n-                {\n-                    \"name\": \"stderr\",\n-                    \"output_type\": \"stream\",\n-                    \"text\": [\n-                        \"/usr/lib/python3/dist-packages/statsmodels/regression/mixed_linear_model.py:2237: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\\n\",\n-                        \"  warnings.warn(msg, ConvergenceWarning)\\n\"\n-                    ]\n                 }\n             ],\n             \"source\": [\n                 \"exog_re = exog.copy()\\n\",\n                 \"md = sm.MixedLM(endog, exog, data[\\\"tree\\\"], exog_re)\\n\",\n                 \"mdf = md.fit()\\n\",\n                 \"print(mdf.summary())\"\n"}]}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/ols.ipynb.gz", "source2": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/ols.ipynb.gz", "unified_diff": null, "details": [{"source1": "ols.ipynb", "source2": "ols.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -870,22 +870,15 @@\n                         \"8   -0.004019    -0.045687  0.023708   0.018125   0.013683 -0.034770  0.005116\\n\",\n                         \"9   -1.018242    -0.282131 -0.412621  -0.663904  -0.715020 -0.229501  1.035723\\n\",\n                         \"10   0.030947    -0.024781  0.029480   0.035361   0.034508 -0.014194 -0.030805\\n\",\n                         \"11   0.005987    -0.079727  0.030276  -0.008883  -0.006854 -0.010693 -0.005323\\n\",\n                         \"12  -0.135883     0.092325 -0.253027  -0.211465   0.094720  0.331351  0.129120\\n\",\n                         \"13   0.032736    -0.024249  0.017510   0.033242   0.090655  0.007634 -0.033114\\n\",\n                         \"14   0.305868     0.148070  0.001428   0.169314   0.253431  0.342982 -0.318031\\n\",\n-                        \"15  -0.538323     0.432004 -0.261262  -0.143444  -0.360890 -0.467296  0.552421\"\n-                    ]\n-                },\n-                {\n-                    \"name\": \"stdout\",\n-                    \"output_type\": \"stream\",\n-                    \"text\": [\n-                        \"\\n\"\n+                        \"15  -0.538323     0.432004 -0.261262  -0.143444  -0.360890 -0.467296  0.552421\\n\"\n                     ]\n                 }\n             ],\n             \"source\": [\n                 \"print(infl.summary_frame().filter(regex=\\\"dfb\\\"))\"\n             ]\n         }\n"}]}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/robust_models_1.ipynb.gz", "source2": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/robust_models_1.ipynb.gz", "unified_diff": null, "details": [{"source1": "robust_models_1.ipynb", "source2": "robust_models_1.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -1531,15 +1531,22 @@\n                         \"truck.driver            -0.129227  0.897810   0.999436\\n\",\n                         \"cook                     0.127207  0.899399   0.999436\\n\",\n                         \"janitor                 -0.079890  0.936713   0.999436\\n\",\n                         \"policeman                0.078847  0.937538   0.999436\\n\",\n                         \"architect                0.072256  0.942750   0.999436\\n\",\n                         \"teacher                  0.050510  0.959961   0.999436\\n\",\n                         \"taxi.driver              0.023322  0.981507   0.999436\\n\",\n-                        \"author                   0.000711  0.999436   0.999436\\n\"\n+                        \"author                   0.000711  0.999436   0.999436\"\n+                    ]\n+                },\n+                {\n+                    \"name\": \"stdout\",\n+                    \"output_type\": \"stream\",\n+                    \"text\": [\n+                        \"\\n\"\n                     ]\n                 }\n             ],\n             \"source\": [\n                 \"fdr = ols_model.outlier_test(\\\"fdr_bh\\\")\\n\",\n                 \"fdr.sort_values(\\\"unadj_p\\\", inplace=True)\\n\",\n                 \"print(fdr)\"\n"}]}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_sarimax_faq.ipynb.gz", "source2": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/statespace_sarimax_faq.ipynb.gz", "unified_diff": null, "details": [{"source1": "statespace_sarimax_faq.ipynb", "source2": "statespace_sarimax_faq.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -1683,22 +1683,15 @@\n                         \"Ljung-Box (L1) (Q):                   0.33   Jarque-Bera (JB):                 0.15\\n\",\n                         \"Prob(Q):                              0.57   Prob(JB):                         0.93\\n\",\n                         \"Heteroskedasticity (H):               0.97   Skew:                            -0.01\\n\",\n                         \"Prob(H) (two-sided):                  0.53   Kurtosis:                         3.00\\n\",\n                         \"===================================================================================\\n\",\n                         \"\\n\",\n                         \"Warnings:\\n\",\n-                        \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\"\n-                    ]\n-                },\n-                {\n-                    \"name\": \"stdout\",\n-                    \"output_type\": \"stream\",\n-                    \"text\": [\n-                        \"\\n\"\n+                        \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\\n\"\n                     ]\n                 }\n             ],\n             \"source\": [\n                 \"print(arx_res.summary())\"\n             ]\n         },\n"}]}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_arma_0.html", "source2": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_arma_0.html", "comments": ["Ordering differences only"], "unified_diff": "@@ -648,14 +648,25 @@\n print(table.set_index("lag"))\n
\n
\n
\n
\n
\n
\n+
\n+
\n+/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n+  warn('Non-stationary starting autoregressive parameters'\n+/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n+  warn('Non-invertible starting MA parameters found.'\n+
\n+
\n+
\n+
\n+
\n
\n 
\n             AC           Q      Prob(>Q)\n lag\n 1.0  -0.001244    0.000778  9.777436e-01\n 2.0   0.052350    1.382049  5.010626e-01\n 3.0  -0.522181  139.090106  5.938063e-30\n@@ -680,25 +691,14 @@\n 22.0  0.054804  332.291098  3.279536e-57\n 23.0 -0.110592  338.726892  6.325402e-58\n 24.0  0.022742  338.999620  2.166837e-57\n 25.0  0.029459  339.458216  6.665490e-57\n 26.0  0.095294  344.266902  2.658405e-57\n
\n
\n-
\n-
\n-
\n-
\n-
\n-/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n-  warn('Non-stationary starting autoregressive parameters'\n-/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n-  warn('Non-invertible starting MA parameters found.'\n-
\n-
\n
\n 
[35]:\n
\n
\n 
arma41 = ARIMA(arma_rvs, order=(4, 0, 1)).fit()\n resid = arma41.resid\n r, q, p = sm.tsa.acf(resid, nlags=lags, fft=True, qstat=True)\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -223,14 +223,22 @@\n lags = int(10 * np.log10(arma_rvs.shape[0]))\n arma11 = ARIMA(arma_rvs, order=(1, 0, 1)).fit()\n resid = arma11.resid\n r, q, p = sm.tsa.acf(resid, nlags=lags, fft=True, qstat=True)\n data = np.c_[range(1, lags + 1), r[1:], q, p]\n table = pd.DataFrame(data, columns=[\"lag\", \"AC\", \"Q\", \"Prob(>Q)\"])\n print(table.set_index(\"lag\"))\n+/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:966:\n+UserWarning: Non-stationary starting autoregressive parameters found. Using\n+zeros as starting parameters.\n+  warn('Non-stationary starting autoregressive parameters'\n+/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:978:\n+UserWarning: Non-invertible starting MA parameters found. Using zeros as\n+starting parameters.\n+  warn('Non-invertible starting MA parameters found.'\n             AC           Q      Prob(>Q)\n lag\n 1.0  -0.001244    0.000778  9.777436e-01\n 2.0   0.052350    1.382049  5.010626e-01\n 3.0  -0.522181  139.090106  5.938063e-30\n 4.0   0.146506  149.951983  2.084573e-31\n 5.0  -0.091171  154.166872  1.731083e-31\n@@ -251,22 +259,14 @@\n 20.0  0.093960  330.576238  4.414194e-58\n 21.0 -0.016212  330.713971  1.708363e-57\n 22.0  0.054804  332.291098  3.279536e-57\n 23.0 -0.110592  338.726892  6.325402e-58\n 24.0  0.022742  338.999620  2.166837e-57\n 25.0  0.029459  339.458216  6.665490e-57\n 26.0  0.095294  344.266902  2.658405e-57\n-/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:966:\n-UserWarning: Non-stationary starting autoregressive parameters found. Using\n-zeros as starting parameters.\n-  warn('Non-stationary starting autoregressive parameters'\n-/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:978:\n-UserWarning: Non-invertible starting MA parameters found. Using zeros as\n-starting parameters.\n-  warn('Non-invertible starting MA parameters found.'\n [35]:\n arma41 = ARIMA(arma_rvs, order=(4, 0, 1)).fit()\n resid = arma41.resid\n r, q, p = sm.tsa.acf(resid, nlags=lags, fft=True, qstat=True)\n data = np.c_[range(1, lags + 1), r[1:], q, p]\n table = pd.DataFrame(data, columns=[\"lag\", \"AC\", \"Q\", \"Prob(>Q)\"])\n print(table.set_index(\"lag\"))\n"}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_arma_0.ipynb.gz", "source2": "./usr/share/doc/python-statsmodels-doc/html/examples/notebooks/generated/tsa_arma_0.ipynb.gz", "unified_diff": null, "details": [{"source1": "tsa_arma_0.ipynb", "source2": "tsa_arma_0.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "unified_diff": "@@ -766,14 +766,24 @@\n             \"cell_type\": \"code\",\n             \"execution_count\": 34,\n             \"metadata\": {\n                 \"execution\": {}\n             },\n             \"outputs\": [\n                 {\n+                    \"name\": \"stderr\",\n+                    \"output_type\": \"stream\",\n+                    \"text\": [\n+                        \"/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\\n\",\n+                        \"  warn('Non-stationary starting autoregressive parameters'\\n\",\n+                        \"/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\\n\",\n+                        \"  warn('Non-invertible starting MA parameters found.'\\n\"\n+                    ]\n+                },\n+                {\n                     \"name\": \"stdout\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n                         \"            AC           Q      Prob(>Q)\\n\",\n                         \"lag                                     \\n\",\n                         \"1.0  -0.001244    0.000778  9.777436e-01\\n\",\n                         \"2.0   0.052350    1.382049  5.010626e-01\\n\",\n@@ -798,24 +808,14 @@\n                         \"21.0 -0.016212  330.713971  1.708363e-57\\n\",\n                         \"22.0  0.054804  332.291098  3.279536e-57\\n\",\n                         \"23.0 -0.110592  338.726892  6.325402e-58\\n\",\n                         \"24.0  0.022742  338.999620  2.166837e-57\\n\",\n                         \"25.0  0.029459  339.458216  6.665490e-57\\n\",\n                         \"26.0  0.095294  344.266902  2.658405e-57\\n\"\n                     ]\n-                },\n-                {\n-                    \"name\": \"stderr\",\n-                    \"output_type\": \"stream\",\n-                    \"text\": [\n-                        \"/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\\n\",\n-                        \"  warn('Non-stationary starting autoregressive parameters'\\n\",\n-                        \"/usr/lib/python3/dist-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\\n\",\n-                        \"  warn('Non-invertible starting MA parameters found.'\\n\"\n-                    ]\n                 }\n             ],\n             \"source\": [\n                 \"lags = int(10 * np.log10(arma_rvs.shape[0]))\\n\",\n                 \"arma11 = ARIMA(arma_rvs, order=(1, 0, 1)).fit()\\n\",\n                 \"resid = arma11.resid\\n\",\n                 \"r, q, p = sm.tsa.acf(resid, nlags=lags, fft=True, qstat=True)\\n\",\n"}]}]}, {"source1": "./usr/share/doc/python-statsmodels-doc/html/searchindex.js", "source2": "./usr/share/doc/python-statsmodels-doc/html/searchindex.js", "unified_diff": null, "details": [{"source1": "js-beautify {}", "source2": "js-beautify {}", "unified_diff": "@@ -51580,15 +51580,15 @@\n         \"5\": \"py:property\",\n         \"6\": \"py:data\",\n         \"7\": \"py:exception\"\n     },\n     \"terms\": {\n         \"\": [4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 23, 24, 27, 28, 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6033, 6034, 6045, 6414, 6427, 6452, 6453, 6456],\n         \"200000\": 219,\n         \"20004001\": 196,\n         \"200096\": 196,\n         \"2000q1\": 243,\n         \"2000q2\": 243,\n@@ -54497,17 +54497,17 @@\n         \"2021\": [197, 201, 221, 262, 4956],\n         \"20210801\": 195,\n         \"20210819\": 246,\n         \"2022\": 262,\n         \"2023\": 262,\n         \"202363\": 211,\n         \"2024\": [262, 6453],\n-        \"2025\": [34, 183, 188, 193, 196, 197, 198, 199, 201, 203, 206, 207, 208, 209, 210, 211, 217, 219, 222, 226, 227, 228, 229, 231, 232, 233, 234, 238, 243, 246, 250, 257, 259, 261, 262, 265, 267, 268, 6413, 6414, 6418, 6431, 6441, 6453, 6466],\n+        \"2025\": [193, 196, 197, 198, 199, 201, 203, 206, 207, 208, 209, 210, 211, 217, 219, 222, 226, 227, 228, 229, 231, 232, 233, 234, 238, 243, 246, 250, 257, 259, 261, 262, 265, 6453],\n         \"2025687192481\": [85, 116, 287, 359, 434, 492, 550, 607, 662, 715, 777, 841, 919, 999, 1084, 1139, 1213, 1253, 1447, 1581, 2011, 2141, 2181, 2409, 2607, 2740, 2804, 2931, 3002, 3115, 3243, 3519, 3583, 3638, 4333, 4412, 4498, 4615, 4791, 5060, 5168, 5268, 5578, 5746, 5965, 6074],\n-        \"2026\": [85, 116, 197, 262, 287, 359, 434, 492, 550, 607, 662, 715, 777, 841, 919, 999, 1084, 1139, 1213, 1253, 1447, 1581, 2011, 2141, 2181, 2409, 2607, 2740, 2804, 2931, 3002, 3115, 3243, 3519, 3583, 3638, 4333, 4412, 4498, 4615, 4791, 5060, 5168, 5268, 5578, 5746, 5965, 6074],\n+        \"2026\": [34, 85, 116, 183, 188, 197, 262, 267, 268, 287, 359, 434, 492, 550, 607, 662, 715, 777, 841, 919, 999, 1084, 1139, 1213, 1253, 1447, 1581, 2011, 2141, 2181, 2409, 2607, 2740, 2804, 2931, 3002, 3115, 3243, 3519, 3583, 3638, 4333, 4412, 4498, 4615, 4791, 5060, 5168, 5268, 5578, 5746, 5965, 6074, 6413, 6414, 6418, 6431, 6441, 6466],\n         \"2027\": 262,\n         \"2028\": 6453,\n         \"2029\": 6453,\n         \"203\": [23, 193, 197, 208, 238, 243, 263, 267, 4145],\n         \"203008\": 198,\n         \"2031\": 6453,\n         \"2032\": [6452, 6453],\n@@ -54566,15 +54566,15 @@\n         \"2099\": [34, 210],\n         \"209999\": 234,\n         \"20cc\": 2433,\n         \"20e\": 34,\n         \"20effect\": 6425,\n         \"20implement\": 6425,\n         \"20lectur\": 2433,\n-        \"21\": [4, 5, 6, 9, 29, 34, 185, 188, 193, 197, 198, 199, 201, 202, 206, 207, 209, 219, 222, 226, 229, 232, 233, 234, 236, 237, 243, 246, 255, 256, 257, 259, 260, 262, 263, 267, 268, 2274, 2525, 2526, 2528, 2529, 2535, 2536, 2538, 2539, 2653, 2671, 3736, 3755, 3983, 3984, 4141, 4218, 5070, 6367, 6466],\n+        \"21\": [4, 5, 6, 9, 29, 34, 185, 188, 193, 197, 198, 199, 201, 202, 206, 207, 209, 219, 222, 226, 229, 232, 233, 234, 236, 237, 243, 246, 255, 256, 257, 259, 260, 262, 263, 267, 268, 2274, 2525, 2526, 2528, 2529, 2535, 2536, 2538, 2539, 2653, 2671, 3736, 3755, 3983, 3984, 4141, 4218, 5070, 6367, 6431, 6466],\n         \"210\": [4, 14, 24, 188, 191, 193, 197, 199, 203, 6413],\n         \"2100\": 219,\n         \"210000\": 219,\n         \"21015893\": 4126,\n         \"21044934264957588\": 3927,\n         \"210565\": 259,\n         \"2107\": [221, 4956],\n@@ -54632,15 +54632,15 @@\n         \"219144465985076\": [234, 3255],\n         \"219200e\": 209,\n         \"219351\": 222,\n         \"21950568e\": 233,\n         \"219512\": [4300, 4379, 4456],\n         \"21955051\": [1929, 2027, 2056],\n         \"219866\": 209,\n-        \"22\": [4, 5, 6, 34, 90, 121, 183, 185, 188, 193, 197, 198, 199, 203, 206, 207, 209, 217, 219, 220, 222, 226, 231, 232, 234, 236, 238, 243, 246, 255, 256, 257, 259, 260, 262, 268, 292, 364, 439, 497, 555, 612, 667, 720, 782, 846, 924, 1004, 1089, 1144, 1218, 1258, 1452, 1473, 1587, 2016, 2146, 2186, 2274, 2414, 2612, 2642, 2659, 2677, 2746, 2810, 2818, 2886, 2936, 3008, 3124, 3248, 3524, 3588, 3644, 3736, 3755, 3974, 3981, 4341, 4420, 4506, 4623, 4799, 5068, 5176, 5276, 5586, 5754, 5974, 6082, 6413, 6418, 6419, 6447, 6448, 6449, 6460, 6466],\n+        \"22\": [4, 5, 6, 34, 90, 121, 183, 185, 188, 193, 197, 198, 199, 203, 206, 207, 209, 217, 219, 220, 222, 226, 231, 232, 234, 236, 238, 243, 246, 255, 256, 257, 259, 260, 262, 268, 292, 364, 439, 497, 555, 612, 667, 720, 782, 846, 924, 1004, 1089, 1144, 1218, 1258, 1452, 1473, 1587, 2016, 2146, 2186, 2274, 2414, 2612, 2642, 2659, 2677, 2746, 2810, 2818, 2886, 2936, 3008, 3124, 3248, 3524, 3588, 3644, 3736, 3755, 3974, 3981, 4341, 4420, 4506, 4623, 4799, 5068, 5176, 5276, 5586, 5754, 5974, 6082, 6413, 6418, 6419, 6441, 6447, 6448, 6449, 6460, 6466],\n         \"220\": [19, 193, 224, 232],\n         \"220276e\": 259,\n         \"2204\": 250,\n         \"220446049250313e\": 219,\n         \"220460\": 219,\n         \"2206\": 250,\n         \"2208\": 250,\n@@ -55945,15 +55945,15 @@\n         \"409\": [34, 206, 6452],\n         \"4096\": [201, 3779, 4635, 4648],\n         \"409646\": 226,\n         \"40976931\": 2504,\n         \"409830\": 219,\n         \"409877\": 198,\n         \"40e\": [188, 203],\n-        \"41\": [4, 5, 22, 185, 188, 197, 198, 201, 202, 203, 209, 219, 227, 233, 234, 246, 250, 257, 260, 1353, 3210, 3736, 6413, 6414, 6447, 6457],\n+        \"41\": [4, 5, 22, 185, 188, 197, 198, 201, 202, 203, 209, 219, 227, 233, 234, 246, 250, 257, 260, 1353, 3210, 3736, 6413, 6447, 6457],\n         \"410\": [208, 224, 265, 6452],\n         \"4104\": 6456,\n         \"4107\": 6439,\n         \"411\": [4, 202],\n         \"4111\": 185,\n         \"411120\": [198, 207],\n         \"4112\": [185, 6456],\n@@ -56009,15 +56009,15 @@\n         \"418180\": 201,\n         \"4187\": 267,\n         \"4189\": 6456,\n         \"419\": [188, 203, 6413, 6466],\n         \"419180\": 34,\n         \"4193\": 6456,\n         \"4195\": 6456,\n-        \"42\": [4, 5, 185, 193, 198, 201, 202, 207, 209, 219, 228, 229, 232, 234, 246, 257, 260, 268, 3736, 3824, 4001, 6418, 6460],\n+        \"42\": [4, 5, 34, 183, 185, 193, 198, 201, 202, 207, 209, 219, 228, 229, 232, 234, 246, 257, 260, 268, 3736, 3824, 4001, 6418, 6460],\n         \"420\": [207, 222, 228, 232, 234, 255, 2617],\n         \"4200\": 6456,\n         \"420045\": 199,\n         \"4202\": 202,\n         \"420261\": 211,\n         \"421\": [199, 210, 217, 6452],\n         \"42129e\": 6466,\n@@ -56081,15 +56081,15 @@\n         \"4291\": 201,\n         \"429357\": 234,\n         \"42940306\": 209,\n         \"4295\": 219,\n         \"429520\": 219,\n         \"429750\": 201,\n         \"42e\": 232,\n-        \"43\": [4, 5, 16, 185, 198, 201, 202, 207, 209, 219, 222, 229, 231, 233, 234, 237, 243, 246, 247, 256, 257, 260, 2671, 3736, 5070, 6418, 6431],\n+        \"43\": [4, 5, 16, 185, 188, 198, 201, 202, 207, 209, 219, 222, 229, 231, 233, 234, 237, 243, 246, 247, 256, 257, 260, 2671, 3736, 5070, 6418],\n         \"430\": [224, 232],\n         \"430369e\": 202,\n         \"430375\": 201,\n         \"430420\": 198,\n         \"43062\": 6441,\n         \"430626\": 222,\n         \"430686\": 234,\n@@ -56149,15 +56149,15 @@\n         \"43924704e\": 206,\n         \"4394\": 6456,\n         \"4395\": 6460,\n         \"439575\": 259,\n         \"43960374424e\": [90, 121, 292, 364, 439, 497, 555, 612, 667, 720, 782, 846, 924, 1004, 1089, 1144, 1218, 1258, 1452, 1587, 2016, 2146, 2186, 2414, 2612, 2746, 2810, 2886, 2936, 3008, 3124, 3248, 3524, 3588, 3644, 4341, 4420, 4506, 4623, 4799, 5068, 5176, 5276, 5586, 5754, 5974, 6082],\n         \"4399\": 229,\n         \"439900\": 228,\n-        \"44\": [4, 5, 6, 10, 185, 197, 198, 201, 202, 209, 219, 232, 234, 246, 247, 256, 257, 2405, 3184, 3736, 3781, 4197],\n+        \"44\": [4, 5, 6, 10, 185, 197, 198, 201, 202, 209, 219, 232, 234, 246, 247, 256, 257, 267, 268, 2405, 3184, 3736, 3781, 4197],\n         \"440\": [201, 202, 3932, 4748, 6452],\n         \"4404\": 219,\n         \"441\": [188, 203, 229, 234, 267],\n         \"44117491\": 222,\n         \"441618\": 259,\n         \"441669\": 234,\n         \"441717\": 259,\n@@ -56209,15 +56209,15 @@\n         \"4492\": [265, 6456],\n         \"449294e\": 197,\n         \"4494\": 6434,\n         \"449639\": 207,\n         \"4498\": 6434,\n         \"44997408\": 227,\n         \"44e\": 265,\n-        \"45\": [4, 5, 6, 17, 34, 185, 198, 201, 202, 209, 214, 219, 220, 228, 232, 233, 234, 237, 243, 250, 252, 256, 257, 260, 267, 2195, 2196, 2198, 2199, 2200, 2201, 2207, 2208, 2209, 2515, 2522, 2525, 2526, 2535, 2536, 3736, 6441, 6452],\n+        \"45\": [4, 5, 6, 17, 34, 185, 198, 201, 202, 209, 214, 219, 220, 228, 232, 233, 234, 237, 243, 250, 252, 256, 257, 260, 267, 2195, 2196, 2198, 2199, 2200, 2201, 2207, 2208, 2209, 2515, 2522, 2525, 2526, 2535, 2536, 3736, 6452],\n         \"450\": [40, 196, 198, 207, 255, 267, 6452],\n         \"450000\": 219,\n         \"450069\": 259,\n         \"4501\": 6434,\n         \"450418\": 234,\n         \"45050393\": 227,\n         \"45064714\": 227,\n@@ -56355,15 +56355,15 @@\n         \"469\": [198, 206, 247, 6434],\n         \"4691\": 6434,\n         \"4692\": 6434,\n         \"4696\": 6434,\n         \"4698\": 6434,\n         \"4699\": 6434,\n         \"46e\": [210, 6414],\n-        \"47\": [4, 5, 185, 193, 196, 197, 198, 199, 201, 202, 203, 206, 207, 208, 209, 210, 211, 217, 219, 220, 222, 226, 227, 228, 229, 231, 232, 233, 234, 238, 243, 246, 247, 250, 257, 259, 261, 262, 263, 265, 321, 3184, 3736, 3910, 6413],\n+        \"47\": [4, 5, 185, 193, 196, 197, 198, 199, 201, 202, 203, 206, 207, 208, 209, 210, 211, 217, 219, 220, 222, 226, 227, 228, 229, 231, 232, 233, 234, 238, 243, 246, 247, 250, 257, 259, 261, 262, 263, 265, 267, 321, 3184, 3736, 3910, 6413],\n         \"470\": [196, 198, 256, 263, 6452, 6460],\n         \"4700471\": 233,\n         \"470062982770637\": 256,\n         \"4702\": 6434,\n         \"4704\": 6434,\n         \"4707\": 211,\n         \"470842\": 220,\n@@ -56425,15 +56425,15 @@\n         \"4789\": 6434,\n         \"47894334\": [3733, 3734],\n         \"479\": [193, 217, 222, 246, 267],\n         \"4790\": 6434,\n         \"479706e\": 202,\n         \"479804\": 201,\n         \"47e\": 222,\n-        \"48\": [4, 5, 34, 185, 197, 201, 202, 208, 209, 221, 233, 234, 246, 247, 257, 262, 265, 267, 2213, 3736, 3923, 4040, 4055, 4075],\n+        \"48\": [4, 5, 34, 185, 197, 201, 202, 208, 209, 221, 233, 234, 246, 247, 257, 262, 265, 267, 2213, 3736, 3923, 4040, 4055, 4075, 6413, 6414, 6418, 6431, 6441, 6466],\n         \"480\": [206, 217, 229, 246, 263, 2405, 6452],\n         \"4800\": 6434,\n         \"480302\": 243,\n         \"4805\": 6434,\n         \"480545\": 210,\n         \"4806\": 34,\n         \"480632e\": 202,\n@@ -56708,15 +56708,15 @@\n         \"50918122\": 227,\n         \"5092\": 267,\n         \"50927459\": 233,\n         \"5093\": 6434,\n         \"5096\": 6434,\n         \"5099\": 6434,\n         \"50k\": 225,\n-        \"51\": [4, 5, 25, 31, 90, 121, 188, 201, 202, 203, 209, 219, 222, 224, 228, 232, 234, 246, 256, 260, 265, 267, 292, 364, 439, 497, 555, 612, 667, 720, 782, 846, 924, 1004, 1089, 1144, 1218, 1258, 1452, 1587, 2016, 2146, 2186, 2414, 2612, 2746, 2810, 2886, 2936, 3008, 3124, 3248, 3524, 3588, 3644, 3736, 4001, 4140, 4148, 4341, 4420, 4506, 4623, 4799, 5068, 5176, 5276, 5586, 5754, 5974, 6082, 6413, 6466],\n+        \"51\": [4, 5, 25, 31, 90, 121, 188, 201, 202, 203, 209, 219, 222, 224, 228, 232, 234, 246, 256, 260, 265, 267, 292, 364, 439, 497, 555, 612, 667, 720, 782, 846, 924, 1004, 1089, 1144, 1218, 1258, 1452, 1587, 2016, 2146, 2186, 2414, 2612, 2746, 2810, 2886, 2936, 3008, 3124, 3248, 3524, 3588, 3644, 3736, 4001, 4140, 4148, 4341, 4420, 4506, 4623, 4799, 5068, 5176, 5276, 5586, 5754, 5974, 6082, 6413],\n         \"510\": [193, 217, 231, 246, 6418, 6452],\n         \"5103\": 6434,\n         \"5105\": 6434,\n         \"5109\": 226,\n         \"511\": [246, 247],\n         \"511024\": 263,\n         \"51136093\": 227,\n@@ -57087,15 +57087,15 @@\n         \"5592\": 6434,\n         \"5593\": 6434,\n         \"5594\": 6434,\n         \"5595\": 6434,\n         \"5596\": 6434,\n         \"5597\": 6434,\n         \"5599\": 6434,\n-        \"56\": [5, 34, 197, 201, 206, 219, 234, 236, 247, 257, 260, 3099, 3736, 4137, 4183, 4184, 6413],\n+        \"56\": [5, 34, 197, 201, 206, 219, 234, 236, 247, 257, 260, 268, 3099, 3736, 4137, 4183, 4184, 6413],\n         \"5600\": 6434,\n         \"5601\": 6434,\n         \"5602\": [228, 6434],\n         \"560379\": [188, 203],\n         \"5604\": 6434,\n         \"5605\": [207, 6434],\n         \"5606\": 6434,\n@@ -57304,15 +57304,15 @@\n         \"5792\": 6434,\n         \"5793\": 6434,\n         \"5796\": 6434,\n         \"5797\": [202, 207, 6437],\n         \"5798\": 6434,\n         \"579835\": 228,\n         \"5799\": 6437,\n-        \"58\": [4, 5, 18, 193, 197, 198, 201, 207, 224, 234, 246, 247, 257, 260, 2195, 3736],\n+        \"58\": [4, 5, 18, 183, 193, 197, 198, 201, 207, 224, 234, 246, 247, 257, 260, 2195, 3736],\n         \"580\": [237, 6452],\n         \"5801\": 6434,\n         \"580245\": 219,\n         \"5803\": 6434,\n         \"580351e\": 260,\n         \"580455\": 197,\n         \"5805\": 6434,\n@@ -62593,15 +62593,15 @@\n         \"debian\": 6453,\n         \"debias\": 200,\n         \"debiased_glm_fit\": 200,\n         \"debiased_glm_mod\": 200,\n         \"debiased_ols_fit\": 200,\n         \"debiased_ols_mod\": 200,\n         \"debug\": [86, 117, 288, 360, 435, 493, 551, 608, 663, 716, 778, 842, 920, 1000, 1085, 1140, 1214, 1254, 1448, 1582, 2012, 2142, 2182, 2410, 2608, 2741, 2805, 2882, 2932, 3003, 3116, 3244, 3520, 3584, 3639, 4109, 4113, 4334, 4413, 4499, 4616, 4792, 5061, 5169, 5269, 5579, 5747, 5966, 6075, 6411, 6439],\n-        \"dec\": [19, 236, 243, 260, 262, 2195, 2196, 2197, 3058, 3059, 3074, 4299, 4300, 4378, 4379, 4455, 4456, 4566, 4567, 4581, 5016, 5017, 5029, 5227, 5228, 5239, 5537, 5538, 5549, 5697, 5698, 5712, 5917, 5918, 5931, 6033, 6034, 6045, 6434],\n+        \"dec\": [19, 34, 183, 188, 236, 243, 260, 262, 267, 268, 2195, 2196, 2197, 3058, 3059, 3074, 4299, 4300, 4378, 4379, 4455, 4456, 4566, 4567, 4581, 5016, 5017, 5029, 5227, 5228, 5239, 5537, 5538, 5549, 5697, 5698, 5712, 5917, 5918, 5931, 6033, 6034, 6045, 6413, 6414, 6418, 6431, 6434, 6441, 6466],\n         \"dec_below\": 2289,\n         \"decemb\": [10, 23, 257],\n         \"dechert\": 6455,\n         \"decid\": [177, 187, 6446],\n         \"decim\": [5, 206, 209, 2253, 2452, 4168, 6452],\n         \"decimal_tvalu\": 179,\n         \"decis\": [86, 117, 187, 288, 360, 435, 493, 551, 608, 663, 716, 778, 842, 920, 1000, 1085, 1140, 1214, 1254, 1448, 1582, 2012, 2142, 2182, 2410, 2608, 2741, 2805, 2882, 2932, 3003, 3116, 3244, 3520, 3584, 3639, 3716, 3980, 4334, 4413, 4499, 4616, 4792, 5061, 5169, 5269, 5579, 5747, 5966, 6075],\n@@ -67022,15 +67022,14 @@\n         \"notebook\": [40, 185, 189, 190, 191, 200, 201, 202, 204, 205, 208, 209, 212, 215, 216, 217, 218, 219, 220, 221, 223, 224, 225, 226, 229, 234, 235, 236, 237, 239, 240, 242, 243, 244, 245, 246, 248, 249, 250, 252, 253, 255, 256, 259, 263, 264, 268, 2219, 4266, 4278, 4343, 4508, 4692, 4801, 4802, 4808, 4809, 4836, 4849, 4884, 4898, 4923, 4958, 5639, 6086, 6096, 6419, 6425, 6434, 6436, 6437, 6439, 6441, 6446, 6447, 6452, 6453, 6454, 6460, 6461, 6463, 6464],\n         \"noth\": [243, 245, 1563, 1856, 1984, 2124, 2216, 2456, 2687, 2951, 3262, 3287, 3312, 3337, 3368, 3393, 3424, 3449, 4004, 5078, 6411, 6412, 6423],\n         \"notic\": [5, 179, 188, 194, 203, 206, 217, 237, 239, 241, 243, 244, 245, 249, 251, 252, 253, 6413],\n         \"notimpl\": [863, 6439],\n         \"notimplementederror\": 4121,\n         \"notmarri\": 259,\n         \"nottest\": 6439,\n-        \"nov\": [34, 183, 188, 267, 268, 6413, 6414, 6418, 6431, 6441, 6466],\n         \"novel\": 247,\n         \"novemb\": [2530, 2540],\n         \"novikova\": 6452,\n         \"now\": [89, 90, 120, 121, 173, 174, 177, 191, 195, 196, 212, 213, 214, 215, 216, 217, 220, 221, 223, 226, 227, 230, 237, 238, 241, 242, 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