docs/conf.py,sha256=pkMro-zA3RZJGvh9-ogJ7wQ5wXk3BVh-WLtdb0rdHfo,3468
surpyval/__init__.py,sha256=sGuCjS1F_Ut_l7Wvg6NdKpsvDHVvwR0dwJ6qdUu67Uw,2508
surpyval/distribution.py,sha256=toWnQ8L94w1dHpB8KsE2LrXqA_ERqzNC-jETGM1XzTU,3468
surpyval/fit_best.py,sha256=AQ1I3kJtX4W4dk6lsYuHuFdDq3iJf47ppn3d-0d90-M,2103
surpyval/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/serialisation.py,sha256=xbmEPv7SL-9CMquAJnaldy5yHIpIRQc7vju8RWUuguI,7530
surpyval/alpha/__init__.py,sha256=E5Tt2dlm1obzskKKVEeWTXSnzphIuRpWuZblo4WfBUs,277
surpyval/alpha/parallel.py,sha256=4k61YswUyvM82TfGzLkLI7yYNEcZjIHWmwg66tDfoTM,1406
surpyval/alpha/series.py,sha256=pr7UlfR8oOpQxQvAEzmSJj09SDTvhL9tMQiSbacgTG8,1446
surpyval/beta/__init__.py,sha256=K-Fv90zuCS8so4ufOu6WVLHUWy3UwgXXwiQ5PZ8ONOQ,283
surpyval/beta/ml/__init__.py,sha256=ZUfYpcCkJqIzYqRpPQ8_d3r2rtcRZ0bURRx3np5wCjU,242
surpyval/beta/ml/forest/__init__.py,sha256=2NqnqA1IaUUM3x5Qs7Xln8nEmqYEe6uSZ7vledbktrI,72
surpyval/beta/ml/forest/deviance_split.py,sha256=7Kd-Eb6ehHcBQTaeoYoIFsxk6lU_Py8FUewnhuNWdSk,15336
surpyval/beta/ml/forest/forest.py,sha256=Syzl3qTCSJo5aVlRq85v-BekOVVY6adRGpwVZ997ZIY,6110
surpyval/beta/ml/forest/log_rank_split.py,sha256=LY0D9EfpZtqgwwb4dsXQGuM4WSo2pGi-APpDiULsSwo,5924
surpyval/beta/ml/forest/node.py,sha256=acZ2Da9HyQ-IV8RJ-ycsFn2py-sk0LmM5qhogPW3JZ0,7585
surpyval/beta/ml/forest/tree.py,sha256=_gJh4rlNsNrNRdpX1cBH9zqb2OyC5hXTGuPOJPCRs_Y,6899
surpyval/datasets/__init__.py,sha256=P97S8HN9BNSW_HcNZyVO4J7UEJYmgFhU-KBhSd9nZ-8,8981
surpyval/datasets/bofors_steel.csv,sha256=4ZZxX6auyGygHFiMT7qgcR2iq7lFnH6zGV4hv_vgkhU,102
surpyval/datasets/boston.csv,sha256=hmSuziyMgiJFqIq1MyWNo2TLFYLkMFpMLES3PbNp3Fo,35734
surpyval/datasets/framingham.csv,sha256=kP2Yh-HVIQjFY9h-Ns2xXO2IsUwzj0Z3Me3F44T_ndo,1395919
surpyval/datasets/heart.csv,sha256=mo6Pv-zxs4EemxQceL4ELCW8AismCMHBRmf5FCFoJP0,9785
surpyval/datasets/lung.csv,sha256=OJ9XOP5TqhQ_I4PB43E4l9nTRJzUBr0IMXn_F8x6-_M,9844
surpyval/datasets/pbc2.csv,sha256=gaZM2CSSXyaO257nUwhLXKRX3gEEnmUMzqXK0rTkagU,296085
surpyval/datasets/rossi.csv,sha256=m4ybuQbBvAQD5dS0agJArMpjnKtTlhCqNntNI3LKUa8,12805
surpyval/datasets/rossi_tv.csv,sha256=fOlx4MUs6WqzjgNNxz__1Zl2fRmHWdXF2vj3Uev2UXo,1918864
surpyval/datasets/support2.csv,sha256=T338-w1wJAGt_Q-kdwLtGP3U3-N6zQvPEFWst98Aehs,3141735
surpyval/datasets/synthetic_dataset.csv,sha256=OsqAD-NqTpkVmYjpGrCQGiy1ffozkm4handkoK7wbR8,1170567
surpyval/datasets/tires.csv,sha256=-Zmub6aCo3xdglTVTNsUjUH4QDAn3nJ73Cg4qzzXS5o,1508
surpyval/degradation/__init__.py,sha256=EUa1tNuKiX7MaHa0EDTc8dBFRjnzj9i9t7xD1aFLOmc,1008
surpyval/degradation/_bounds.py,sha256=b-OtSDMW9sByNdM45Q9H2OOAas8HsZU3IU-BDzWsjL4,14943
surpyval/degradation/degradation_analysis.py,sha256=w20jNVhx5Q6Rzl1A5KuAaEG5RUiC3RkZ9mEaNI3LoBA,67079
surpyval/degradation/path_models.py,sha256=T9p3mWv9JftkkY9A3tnZhPHAAzj4SrQjCFZS2o3dct4,18030
surpyval/degradation/population.py,sha256=WdZN-b5Pmiyh9ympmAmLJoyTkfKty4V1vjCxlfNu6qY,11527
surpyval/degradation/process_models.py,sha256=SJMzr63_V-tYzN6TlCMcN9siQgqQWQFsAgD1jIYdaXI,20787
surpyval/experimental/__init__.py,sha256=YYapY7bFQ14UW1O-aqiuiIOVEaFC8BX3icX33hz4LwA,660
surpyval/multivariate/__init__.py,sha256=gt0pGo3GMKvbghmXMsw9QaavIrgttPjI6NfEgIFYtZw,971
surpyval/multivariate/parametric/__init__.py,sha256=R7QQ4EPU7CzZAAtKyBz6g7tE6-SrFTpVUqQzota2GbI,322
surpyval/multivariate/parametric/data.py,sha256=5TWXX5QrEcZwz3xaDKADUWEpXFSQb5tbeYBgABd29gA,3995
surpyval/multivariate/parametric/copula/__init__.py,sha256=CpVPHIDaDeHa_ip5KuEUtnLVpVqcxurrvZ-QiVmWExk,307
surpyval/multivariate/parametric/copula/archimedean.py,sha256=s7AmqHstMOU4AEYpx34UnnF7wEOSb1SAycXB--I8ts8,4777
surpyval/multivariate/parametric/copula/copula.py,sha256=qcRK67s7ipCyYkoRBOpcL1B14EwHpAUnOavZSd6rnlo,14740
surpyval/multivariate/parametric/copula/copula_model.py,sha256=lHzH5uiDrY6lSRypaw_arU6Z0Gm830jKUGZVpuWUIeM,4194
surpyval/multivariate/parametric/copula/elliptical.py,sha256=O5A8ZSYO1bs-8z12s79BzthqX9pjfAzKnOfpkzz_wHY,2951
surpyval/recurrent/__init__.py,sha256=bALk7m-U7Awxb7_oIElbQBjd3TAKJwjUi-478SxllvI,479
surpyval/recurrent/diagnostics.py,sha256=0Xy5j1ZHUaUYDiDrltgtX4CVbLEZ9NM2Rp4zBxlgKP4,21405
surpyval/recurrent/inference.py,sha256=PZPZU6A47FmfkvphvAq6pyIXZYLVJYxYAYBcTKLYXoU,10141
surpyval/recurrent/serialisation.py,sha256=W5N0gAxiL53RTcOYGBEFc5rD7eYK6aDj-CibXAp8puU,1145
surpyval/recurrent/simulation.py,sha256=QohT_NHeH_SI8koJqGoHbrpJ2mmruk_BZBKwkDyKcAM,14443
surpyval/recurrent/tests.py,sha256=HQJH_epu7X5OyC05KspZWMiBPSD__6KE1WDF2rme9Lg,14654
surpyval/recurrent/competing_risks/__init__.py,sha256=oTNNnCXlKjzsRS5qeb_SGS_MOjAiiP3Sb6iYRVwSa-4,139
surpyval/recurrent/competing_risks/nonparametric/__init__.py,sha256=m-mGrXyPtpWXjO4dB4Gi9zidsvr-_PiIaYgGbGX4LHM,49
surpyval/recurrent/competing_risks/nonparametric/cause_specific_mcf.py,sha256=ENNQD2VfMYAH_GaNbbEPIpNAYfRL3EPSsSpe9AiWl98,7946
surpyval/recurrent/competing_risks/parametric/__init__.py,sha256=oq37TRfcRdZWm1dJ-yd3uRvLnM3m7n_IUSDajxK7HRM,84
surpyval/recurrent/competing_risks/parametric/cause_specific_nhpp.py,sha256=-xiks_Txylf8YOCV9s1zOVrVbzzvNm3_W9nEvPhpH6M,12230
surpyval/recurrent/nonparametric/__init__.py,sha256=kRqNJix-CTEJhJwXN-7_7rMVk128MofjyAG-sk1pR-M,39
surpyval/recurrent/nonparametric/mcf.py,sha256=1iAzDJPb7cZIJROVfRH_RyybKIEOfDaYWz01RApsPWE,8661
surpyval/recurrent/parametric/__init__.py,sha256=tAXI-rBvVFaGzRgkNRwFLPB4fTOdTqgTSqFdwRpK_Bs,235
surpyval/recurrent/parametric/counting_process.py,sha256=QifVAliX_ELeJbhjgpgjM6gWowwcBUZcuYhZ1UQe4gM,3136
surpyval/recurrent/parametric/cox_lewis.py,sha256=ZrxC-HHt4SHgMR6LzjDNjdORn7z2VJMTVNVBxLxZtmU,2649
surpyval/recurrent/parametric/crow_amsaa.py,sha256=8UxjNAk7pOjFDtf48QRO3Q67d6ylB5fqCUk_PoWlExk,2187
surpyval/recurrent/parametric/duane.py,sha256=Ng1Z6iLSvV2CChE3Fj-RCV1oqWl2C86nnrc8XK6CNM0,2029
surpyval/recurrent/parametric/hpp.py,sha256=4tCJMakOj50nsrmB7UqIsyy5L5qFkRiQgxLQYH4u2HA,11045
surpyval/recurrent/parametric/nhpp_fitter.py,sha256=4Jt3imS-Q7GE9PO8kWpA6TfJGnCaYXOLi707VWO0VCw,9120
surpyval/recurrent/parametric/parametric_recurrence.py,sha256=N1p2o_-1enphrb8W9ar7nbwUZfwXVJm8kut1Td3SYcY,13198
surpyval/recurrent/regression/__init__.py,sha256=XYsxa1w_DoO2fc9pFkdL-RAisKoJp92UUEQruG6rYGA,195
surpyval/recurrent/regression/hpp_proportional_intensity.py,sha256=a_ano34ymCeeAnj--Z2cJ8MZAkNAzNN3PRG2J_Gflik,11137
surpyval/recurrent/regression/nhpp_proportional_intensity.py,sha256=6AdBZ8Wbgz9l3IW3B92BOvCPsRCpF5LWOxsXQNyc6lM,10529
surpyval/recurrent/regression/proportional_intensity.py,sha256=DjEN2te4f8U_EwcKUUZiW084WZOC4zGHKapivIMfyXc,18723
surpyval/recurrent/renewal/__init__.py,sha256=F6JEry6TK8dWhb3-FUg7HQxU33h8KaSzUfeQJ2VaU7g,303
surpyval/recurrent/renewal/ara.py,sha256=8z2D8xnZx3O2N57MaWOUaJF9ykUMMnoax7OnVyUG1sg,9670
surpyval/recurrent/renewal/ari.py,sha256=O_UilAqGtt4pJ1oAeDff8hiGRBzvU6ixfdTa5_45RdY,11524
surpyval/recurrent/renewal/fit_mixin.py,sha256=v4lL2PntQ739ZHSQs-BydGPTJ6dzddV77nKRMnKC7Oo,4461
surpyval/recurrent/renewal/generalized_one_renewal.py,sha256=9jvmpRNGwMiPoEEldZq5_N_BTgeotOKcqeYsb11V8eI,12507
surpyval/recurrent/renewal/generalized_renewal.py,sha256=pMwtoBHlV8eywCOA1D3UGWvsWluEdSR_vr5hNC9D_7Y,13455
surpyval/recurrent/renewal/renewal_model.py,sha256=53vSUpT4ZT5t4rwQxC6FIpuJc-7MyKG5FW273smUSK8,14314
surpyval/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/test_datasets.py,sha256=iRiYa--Vc435hjuYLokPro1vUutZdAi32vnh7OTaca0,618
surpyval/tests/test_distribution_abc.py,sha256=kYBvAl6oZTJ2oIK-UHkOhfS07BHICLP6DSQkq_AbkE0,2718
surpyval/tests/test_mongodb_serialisation.py,sha256=ej0kVLQMSVE7sHjnpNIXDMiag_zWg9KWdWPDPPKZP7E,12152
surpyval/tests/test_package_serialisation.py,sha256=N98Qdr5U5va3b1B1NldJwEVz0ZsV9KwSAMNwHMX_l-U,8474
surpyval/tests/alpha/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/beta/ml/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/beta/ml/forest/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/beta/ml/forest/test_forest.py,sha256=r62xQWggZdVywtlG4C38JiajA2rT_jM2R0sfLFw-zRk,3808
surpyval/tests/beta/ml/forest/test_log_rank_split.py,sha256=rVex0H0xAQnItVWjM3d266lTzlTREEh9CsV9MFCgTbI,3140
surpyval/tests/beta/ml/forest/test_tree.py,sha256=otzyjhYCjYsOZwoy5UCr6jGcZnLpj7RRzksn8CaJAvs,8274
surpyval/tests/beta/ml/forest/test_tree_forest_behaviour.py,sha256=3dQMDp7rbyynlAwFCT6IMk4-tvUhK5UFbH_kar5q2o0,12075
surpyval/tests/beta/ml/forest/test_tree_full_data_model.py,sha256=QiJHpHDBF-RIDb1HW4YSnHX1b-wokVASyngP1U33yeg,13378
surpyval/tests/degradation/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/degradation/test_degradation_analysis.py,sha256=VJasYdxKS7OIpGwmuJU-1WMJXWG62_SPdvOxWSehQMA,35414
surpyval/tests/degradation/test_degradation_bounds.py,sha256=QyXSiJvclrQdP0VOapgIVqOiTe5nyIwXJ9sgM3sHVqA,7826
surpyval/tests/degradation/test_induced_life.py,sha256=6DCzwLEZ-M-09my8WreElWxCzza_rSCIHtAp2i5Lh7E,5675
surpyval/tests/degradation/test_path_models.py,sha256=EhqU5q8kNUwSHIFThDBFUwmTMBYnU3nxpnyzl5uR_YE,4345
surpyval/tests/degradation/test_process_models.py,sha256=zVtNbfbsBgRobZMp9WIoGFF3r6hGAQg704hJqAbJvRY,6663
surpyval/tests/degradation/test_serialisation.py,sha256=Hw3wOxzfdLNxaB746LXXkNzUsua7umxuDlzSS_o3lvM,7318
surpyval/tests/multivariate/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/multivariate/test_archimedean.py,sha256=KC89Xvfw8BPsPvPT4Ru7z9iBJ5Wg8DqSDxuNnL1I_l8,2430
surpyval/tests/multivariate/test_copula_censoring.py,sha256=4F6dUg_6NgaSEn3XzIijcaat37ES0xWWifJ1D6_9qhc,3934
surpyval/tests/multivariate/test_dependence.py,sha256=FCEX2-efiagr6GxeoaiOd1eOgbqwjjUABjsFkUChX5k,1563
surpyval/tests/multivariate/test_sampling.py,sha256=iCDtyKDsOKMK2HzE3Si5P6crH4OH3Y1L4yKPphhoVsI,1577
surpyval/tests/recurrent/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/recurrent/test_ara.py,sha256=hqooZ7l3bWQcP7nU9SMykIyaTO1rXOzC138xcd39THI,2539
surpyval/tests/recurrent/test_ari.py,sha256=byihGvgGHVzrUVpFdzvE5oyF9yyYNMTqyWbRo7wdol8,2817
surpyval/tests/recurrent/test_competing_risks.py,sha256=RO7YoRkkeJQ6TsLqS9ZogCd-gXKffM98B9YUluurJW8,2567
surpyval/tests/recurrent/test_counting.py,sha256=m5-9X5_KqDLPJxpBlmsHR-uTvYNb4ERJZ2peuNwMmaY,13094
surpyval/tests/recurrent/test_counting_process.py,sha256=i87ALkbZRtXzfDggbU6PnqcK3I9aTwU0huLFrTERaz0,2852
surpyval/tests/recurrent/test_cox_lewis.py,sha256=09eaycHFNRiNbreq6v-NAYwIN7xfeKy5Jy4qDttkZKQ,3519
surpyval/tests/recurrent/test_diagnostics.py,sha256=2X5o6ysROBtDM6P4e5I7bFu3ZgcgZOl-tQMU0jTFPx0,6464
surpyval/tests/recurrent/test_event_marks.py,sha256=vr3W9hCGlUMUXHRHOYhmWEEwuRGUbBtGl4fGfsrWK3k,7391
surpyval/tests/recurrent/test_gapped_observation.py,sha256=-1LLcHnPeX_d-ybVvhm8q0riXKdpv22YplU3a-ewOZ8,8727
surpyval/tests/recurrent/test_hpp_proportional_intensity.py,sha256=gyi6zg_t6zERg1UinrwnyywG6fXzik6L98S7OgntgIA,2621
surpyval/tests/recurrent/test_mcf_truncation.py,sha256=zpMAr_bUAlqwlz_y8cXFUx-RaVLU66tY6lNkojLFlcc,6753
surpyval/tests/recurrent/test_nhpp_censoring.py,sha256=GPFky9GfBKJ6YWR2P9qzDlf_ldb2oinA2tmQ4ah7bv0,1781
surpyval/tests/recurrent/test_parametric_inference.py,sha256=4I4BlstcOtNfzjVFfDLDO3c29JmY4GwstRkRM9rv_aE,10539
surpyval/tests/recurrent/test_recurrent_roundtrips.py,sha256=g-gh-wtacml3V91J7I1b3K6NPzBG9vMWRg7DnLZp75M,8032
surpyval/tests/recurrent/test_regression_diagnostics.py,sha256=cqpJWtqBaiwt3T0y3owVxH04wyPfErBeCziNG7rKmLw,8741
surpyval/tests/recurrent/test_regression_simulation.py,sha256=VeqDY48y6TBOQ6YdBolpso5g5P3eO0JOAQJHQ3bxeUQ,2540
surpyval/tests/recurrent/test_renewal_diagnostics.py,sha256=52T3qGIt5udcbWNbTj4NmJLsidh8fJDfxmp6T6acGnM,7466
surpyval/tests/recurrent/test_renewal_serialisation.py,sha256=oBoBu6UPps6-Fci69PhtgAFFf9ZRQ7E68CMh3LaEAqg,3952
surpyval/tests/recurrent/test_serialisation.py,sha256=uDZcx7pmuWV5AZ4uQXJI5Di9r0bU0bH5P5JNhf4DubQ,7753
surpyval/tests/recurrent/test_trend_tests.py,sha256=g0wRlhc1uzs1d5YHEKU6ty6ZmSHAZ5nKQytpwDqt2SM,8979
surpyval/tests/recurrent/test_truncation.py,sha256=JWUQOuQbEP1X8BMpv9pRBotMaJLqMawCK02kjvTUcrs,6244
surpyval/tests/recurrent/test_xicn_validation.py,sha256=qNCegjdxBgmvD_nftykvadXy-HFNqTVsVGL2o6dhJko,6068
surpyval/tests/univariate/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/univariate/competing_risks/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/univariate/competing_risks/test_cause_specific_cox.py,sha256=evh3P3it5pmVIQ1x4bvfUihGt_8kCW9CBsaBma0S6ho,5890
surpyval/tests/univariate/competing_risks/test_fine_gray.py,sha256=tTO2ahMnC4Cld50yKKYFdER59iRDBXj_qM4GNzVU9Zo,6538
surpyval/tests/univariate/competing_risks/test_parametric_competing_risks.py,sha256=gt53Ov6DJQDb2cizh9UVp96jbAO8HqROq1pz4HIXIiA,13215
surpyval/tests/univariate/competing_risks/test_serialisation.py,sha256=RfacYXyzqY67qy4tNj0IAMVQtTyTOv1PNreLo4Y9FCs,5425
surpyval/tests/univariate/nonparametric/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/univariate/nonparametric/test_bands_and_hazard.py,sha256=zSzE6Qtt41QwR3gkIqhbtfvxrZCzh6VU5ds-s3jlozI,3747
surpyval/tests/univariate/nonparametric/test_confidence_bounds.py,sha256=BGmwmAClCrPkYZlo_dE03DjgoNwL3w2-_GSYoRzM18Q,9125
surpyval/tests/univariate/nonparametric/test_fleming_harrington_ladder.py,sha256=fWjojJm3Vg7lM4TGaduc2KjAFKGO1ajM3INHpWM4p88,4399
surpyval/tests/univariate/nonparametric/test_logrank.py,sha256=1AnSiYPDGRQwe_AURrJiJ6ShcvBjFf60IJNDzwYlL7M,3419
surpyval/tests/univariate/nonparametric/test_model_methods.py,sha256=zMwPJjSLui47b1rT9ekoCYNCbiPKBVCkV9a7Q-F64y8,6377
surpyval/tests/univariate/nonparametric/test_np.py,sha256=CRcUQVYSZejB2X3jf0DEjSNOBH_yiFelbFisZTizpKs,10522
surpyval/tests/univariate/nonparametric/test_np_coverage.py,sha256=GvEU483neBHNcWWnoJN5pivyDQiF5llmFZp1iThF2HA,3431
surpyval/tests/univariate/nonparametric/test_np_improvements.py,sha256=0o5Tx3Muve98aobMvZwof0RFpIm6U032Hhp_1lKCXx8,5945
surpyval/tests/univariate/nonparametric/test_plotting_positions.py,sha256=Tns7vH8vfyDswSZ3o4S3FLYQ1zbmEsX4YZiEfMxdI7A,1270
surpyval/tests/univariate/nonparametric/test_turnbull.py,sha256=FpPpjf5jnJHgaJ99e39dydrb_Hg90S-eqb55FNfxqOw,8697
surpyval/tests/univariate/parametric/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/univariate/parametric/test_binomial.py,sha256=GNloLG7H7mfaubAzK8baCmZWQPQqd8PVLEowPqE2njQ,4460
surpyval/tests/univariate/parametric/test_confidence_bounds.py,sha256=dxCs3TJhMae9GbWJzJG_N_80Yn0BKNqhQyryqzZUhGs,10409
surpyval/tests/univariate/parametric/test_discrete.py,sha256=SzKEoDfViQOWrOpYuLvdxkC30S48AT-T7k5hND8i1Kg,10270
surpyval/tests/univariate/parametric/test_distributions_math.py,sha256=4gLpjjNU7R8OyZqK1SpQecSUPRbAJaIaIur7sbBHXxw,10427
surpyval/tests/univariate/parametric/test_fit.py,sha256=Pcpl341rHu1HRTrP8Uxfo-x2BNvxGCCcUdA0Ok-jnPc,13396
surpyval/tests/univariate/parametric/test_fit_helpers.py,sha256=IKJhCnvY4ES4jie990KyX97-cRKfsGG2KZ7O-B8_e3w,6403
surpyval/tests/univariate/parametric/test_fixed.py,sha256=CUMZnmE3mLKUnCM8HS5T1WIdOWpD47qzbDG0gGkbwpo,2451
surpyval/tests/univariate/parametric/test_lfp_zi.py,sha256=URi9vwxj0JwrPeojkbk60OrWYzXW16gq4Nt8ng5VnHg,8344
surpyval/tests/univariate/parametric/test_mixture_model.py,sha256=IxVhQ6Luq1xlkeeCGRv5z0JQ8qK12sEE9HLHuMfZQWk,2137
surpyval/tests/univariate/parametric/test_offset_divergence.py,sha256=TaX3Dq5k4xIQIsUN8fkRBHJob91hrbhHHgLnm9LmABE,4899
surpyval/tests/univariate/parametric/test_real_data.py,sha256=NDs7jq4GnTx7eJJCYBGhG8UpHRDN9ScsVeRKosChu2g,5515
surpyval/tests/univariate/parametric/test_regressions.py,sha256=T5I1osdIQA1cbA7uI8vE7SHN5IjF718gl7q18q7a-Lk,3588
surpyval/tests/univariate/parametric/test_to_dict.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/univariate/parametric/test_uniform.py,sha256=UX9-FHUbNsIvOukJhZR4LkBqHAKilrLxkpCUhc3SDW4,1839
surpyval/tests/univariate/regression/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/univariate/regression/test_additive_hazards.py,sha256=pC3BrkWmnPFpSOAOxbMVzQq6E8HrSz3EgEallK_gcqw,5641
surpyval/tests/univariate/regression/test_additive_hazards_parametric.py,sha256=dw9vf1oM5cSAR7o2HLH3sPSzRn7iOxHhuzTxU8Pjvtg,5701
surpyval/tests/univariate/regression/test_buckley_james.py,sha256=880JjGjj3EmeXbre6DDCKRIs0CRAB_GX63ECnZVLQUQ,5366
surpyval/tests/univariate/regression/test_confidence_bounds.py,sha256=cZeMlCUtmZtSGPBE8xjjv0jIncR1JS1vC9hmSfviZU4,6649
surpyval/tests/univariate/regression/test_dataframe_fit.py,sha256=7v93aoxoEYyZGfLpwXndXgDxJaplA3u4aJ9rDWBrpeQ,4675
surpyval/tests/univariate/regression/test_proportional_hazards.py,sha256=pUjWX-UV8XxW2x9yHHKZJQ7L59lzDTTqpgG9iewU_f4,11264
surpyval/tests/univariate/regression/test_regression.py,sha256=A0FsF4SKqL9b0etAFTcF4TYDNgRTEvxpuZHRBO6kd9Y,4151
surpyval/tests/univariate/regression/test_semiparametric_serialisation.py,sha256=cNaDimFj6yB6eG3t0jC7u-zIhKj9zh7vI3mP6wGXMug,6935
surpyval/tests/univariate/regression/test_serialisation.py,sha256=dkcMkleIrlkpM5ZLpw_Wru997mjo47xZe1H7-ErA2gg,6297
surpyval/tests/univariate/regression/test_truncation.py,sha256=27tyrQzFxUPxbM6HvX3PC7zaWTOzY_z_g-MDDQE-v30,7324
surpyval/tests/univariate/regression/test_tvc.py,sha256=HAvb-pbSY-LIIX4v1NYoMfY3iaT5AtUdynIqGtusEvc,6652
surpyval/tests/utils/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/tests/utils/test_score.py,sha256=4Xo8nY6nWBlyXstK-bokS9N8Lrv8PHGLGW6WieK2qbU,2416
surpyval/tests/utils/test_surpyval_data.py,sha256=BDGDfytYseuEUg43eFCCOZVHQLO3FuKaSDgr-mihhb0,6732
surpyval/tests/utils/test_utils.py,sha256=SE2-gR44YAJQVnzrhF-ufCgPHuBgMB_MdPs3xE5XZW4,10920
surpyval/univariate/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
surpyval/univariate/competing_risks/__init__.py,sha256=Fp0IS3QP1aOoQUykAZWfRj65_1XGPDPLSvyMUdvwXKU,285
surpyval/univariate/competing_risks/nonparametric/__init__.py,sha256=fwKlk61_igh1VQqX-EPQY9GQmXilYOjhUSUVnxhvj-0,44
surpyval/univariate/competing_risks/nonparametric/competing_risks.py,sha256=kfWS32LBIAAP9vXNcrgCbVPhMPC4EzvhChE-aBNgaq0,7035
surpyval/univariate/competing_risks/parametric/__init__.py,sha256=PQlGBj4VuSMWYSP9T3oQgsdR6XIJANxLH5r8JgBU_mM,105
surpyval/univariate/competing_risks/parametric/parametric_competing_risks.py,sha256=rLuIhqhcEFEYLGmayJ_R9-Fh3pLSuBdG2BWFO6wvaVU,17026
surpyval/univariate/competing_risks/regression/__init__.py,sha256=7AklrzLowgORlWL_tNplnQZt-HwG1H3obHvFq8EBtM0,185
surpyval/univariate/competing_risks/regression/competing_risks_proportional_hazard.py,sha256=S9E411NsAWIRVg1sNGLy45-juqdXZryKOGyHMBgSLmU,11399
surpyval/univariate/competing_risks/regression/fine_gray.py,sha256=NV3_l0ZhCVJ56-O7lF9CTXNFxsK_CoCrfGvgbOxiWuI,12197
surpyval/univariate/nonparametric/__init__.py,sha256=xSQXfAP9mYb_zMSRXXAFrPob3WWdw50MKGp_Dk23VVI,1293
surpyval/univariate/nonparametric/filliben.py,sha256=dkDT7qWGaqqthSe4rVPQcvvYFeZHKN14y7megpdOfms,916
surpyval/univariate/nonparametric/fleming_harrington.py,sha256=tvOuDKFRz9sBzwpml8NOs0CbTSSKO5Ltig8W-7k9TPw,4077
surpyval/univariate/nonparametric/kaplan_meier.py,sha256=sEd_dQOf3dKMua6H7qsieCqbsHjScfYcGfwodAAqeOM,1743
surpyval/univariate/nonparametric/logrank.py,sha256=tdRLEt8SeqSXoLwdaPIbkqwuCc7ckwQ12YjFlmFx8Hk,7081
surpyval/univariate/nonparametric/nelson_aalen.py,sha256=8N378W5OyGLOEriH4Mz-SQ_WrbJM3CJKVjfpuQk9XJE,1716
surpyval/univariate/nonparametric/nonparametric.py,sha256=cCgQuiZq4QE26J50Bk86NMZ4YIA3bNCI8u2wJoBumto,48215
surpyval/univariate/nonparametric/nonparametric_fitter.py,sha256=UCjEg7nIJD8N1HAT1dBce8Rh-QJL2SVKL5ROxJNZ1XM,8711
surpyval/univariate/nonparametric/plotting_positions.py,sha256=jgw2Oi7Qm2TBdIBiFNUgqPldxKlZs6I43U0WjdFfbqk,6316
surpyval/univariate/nonparametric/rank_adjust.py,sha256=WZQksodvbOeA7n7C8R8bv69j_MllqrD1zuV5CmWIYC4,1129
surpyval/univariate/nonparametric/success_run.py,sha256=XKuNLk2ldK_4jTT-fWgJSdPs_UwkOs0b2s3QJSr7p90,1341
surpyval/univariate/nonparametric/turnbull.py,sha256=gjBXs8OPNIRJqOMXNrMWQRoYSnkmi1V1FIpWC2iZLq0,10914
surpyval/univariate/parametric/__init__.py,sha256=umSbdlBYTDUw0-V7r2ih4BKm0YiQD_7Qjo3oM0StbrE,1545
surpyval/univariate/parametric/mixture_model.py,sha256=BNsNUf0PeQ5q2IVbTpHejbUuKpOLVWF6I761uEt1zcM,14042
surpyval/univariate/parametric/parametric.py,sha256=cwHT3ItIm4XicZcsKqjprl2WU909k5zLZTK2XHon520,41802
surpyval/univariate/parametric/parametric_fitter.py,sha256=we9Nx6UMDSYgdwPTtaS9PiOe7_1tLmSMxm4GyiGSZ1k,41082
surpyval/univariate/parametric/probability_plotting.py,sha256=_3U9kUliKZxu9HipIizuKyOAfqoU_OnXg4rhfexsiDw,6177
surpyval/univariate/parametric/distributions/__init__.py,sha256=r50gAw1Na-4NS1SGxiL51uZFtY-qa5ox5Fz7J6-YWk8,877
surpyval/univariate/parametric/distributions/bernoulli.py,sha256=TW_L2XLyZgaVjoHYx5x1YZZXOWjEEKv695zItoLrftA,4121
surpyval/univariate/parametric/distributions/beta.py,sha256=UJOmD903weqU4GBVZOXeTsFoRtL2GDdhKFawgO7a3Ac,12325
surpyval/univariate/parametric/distributions/beta4.py,sha256=FCClVox7fQebOHNhmhRmz4rY1Z4oHq2cajD5iU53_b0,14888
surpyval/univariate/parametric/distributions/beta_geometric.py,sha256=lLTm5QH_1GDr_g_gi07E5z-OnsXjXO6Ww4SiqaD3A5g,4684
surpyval/univariate/parametric/distributions/binomial.py,sha256=qiFFG4ZyRVYbWmKKXxMtPSe1xIfvrta_HRqaAFwuZ4I,11848
surpyval/univariate/parametric/distributions/custom_distribution.py,sha256=iki4J190WzFTsAZxE6CAGoeJaPJKPqXKFgIf3AImrGo,3816
surpyval/univariate/parametric/distributions/discrete_weibull.py,sha256=_uIHrmZVqFBDYr0JxRKpyNtS_gKVkZ5h761TW5sNRi4,3984
surpyval/univariate/parametric/distributions/discretize.py,sha256=wPr7LTbCjebZR-2qmxHsRZt2FeyJaZsgqxr0LZypSWg,4761
surpyval/univariate/parametric/distributions/exact_event_time.py,sha256=S-TgP9Ch54zsFaUmG8f1wD-l5XNZBPVoygj3PvKugc8,2057
surpyval/univariate/parametric/distributions/expo_weibull.py,sha256=s1DNsyLYE7ydBaPxihdCsCjoX0yTZ5ycjlCOI0tCn-w,12236
surpyval/univariate/parametric/distributions/exponential.py,sha256=srYmWkjryIJIcaFOcrkG0CLIEYCh0p4oByLAOLdS7X4,12227
surpyval/univariate/parametric/distributions/gamma.py,sha256=TT2eZl_IZj2QBSa4MYclursfqWcAoi2Xj21XsqSgI1g,15961
surpyval/univariate/parametric/distributions/geometric.py,sha256=0AFxCPjBXZXt6QlUY4inGYr-XRYiOozS2SmluSkZmGg,3457
surpyval/univariate/parametric/distributions/gumbel.py,sha256=L47_HabzFCtDT82A9I_3CQvklxgws_TSJiYEX8lNqK0,10553
surpyval/univariate/parametric/distributions/gumbel_lev.py,sha256=i5nrayVvAB9TfkNQXem9nmuVHfS-Ea7hfhFJllnqAcE,10345
surpyval/univariate/parametric/distributions/logistic.py,sha256=naa-6QSTMSxm3d_x0f35r_mrRLxjx0s54HTFVGtCXkc,9565
surpyval/univariate/parametric/distributions/loglogistic.py,sha256=kKXNbL9eVBYoSSwpcdxUNg3mn5LY82ORhSU7jTN3kFY,11040
surpyval/univariate/parametric/distributions/lognormal.py,sha256=dmEw_jm0nPory_UXqBeGxn2DW9t476rakEInw-wJQ0g,11934
surpyval/univariate/parametric/distributions/negative_binomial.py,sha256=j-YxC8bcbSmXDUo-ujpTiSSKEDZ0Twfty8GjPfWUtVw,3847
surpyval/univariate/parametric/distributions/normal.py,sha256=f-3DC1rUB01fdSFvnHmarlFWGFKnRI2cqoLOEpi3NV8,11350
surpyval/univariate/parametric/distributions/poisson.py,sha256=VwXR3kG1fX9HPQOOmUTRZuhj08B1lb6whiw5RCpT2s4,3465
surpyval/univariate/parametric/distributions/rayleigh.py,sha256=88ve8DUJRLp6YMxy2RTdOq1vgRFwNThh7LY4fsQy3cQ,11091
surpyval/univariate/parametric/distributions/uniform.py,sha256=4q21cNNt7wkcUDqIUWAfUFn-l81zVAURyJAvtRNo2Lk,12042
surpyval/univariate/parametric/distributions/weibull.py,sha256=Ii6VTPS5CVrnTX564gnjlesdy5GvFo5fJwV-1F-5dYY,10975
surpyval/univariate/parametric/fitters/__init__.py,sha256=EiJE4Hw8SVBlL3U75-Cxf6tHD_cvpeITJ6P695nfzQI,4311
surpyval/univariate/parametric/fitters/mle.py,sha256=eX7YfUfxu40jd8zocz7vZPkNCz7TM-vzQVp2784WRzU,8373
surpyval/univariate/parametric/fitters/mom.py,sha256=Jf_dcSn1lRwYjCfAAU40T42HaoWZUMYMvehTgRUzOqo,1562
surpyval/univariate/parametric/fitters/mpp.py,sha256=7s-NqWWAhXSv4NEU0jjJ4okbJEdpH8UamECZVXU5peI,2838
surpyval/univariate/parametric/fitters/mps.py,sha256=s62NNqcyewwbW18Fz_aMWJsiNVsEnhXkfNkXUYQKYbQ,1642
surpyval/univariate/parametric/fitters/mse.py,sha256=cOiqfdyuekAjuY5wXif2ptEGJRhMqDdrBqxSP-pifdI,1859
surpyval/univariate/regression/__init__.py,sha256=rEHuaXgRZvhlb8Xc38WfwSR7BRYJ3TGhzLaTpifgOl0,2649
surpyval/univariate/regression/_bounds.py,sha256=w2XG30o-8uUlCuZCT44KghB_jD5-nuNUqJ3bDbHHXM8,4304
surpyval/univariate/regression/_likelihood.py,sha256=LC83FVlfzL8z_ZRGwPoHqn2TS9lFsNbmncNMccdFaC0,3802
surpyval/univariate/regression/parametric_regression_model.py,sha256=rWCYnatGy0mmTvUQTgELf0LuEiCs3DT_qPUo4YX_Qnc,35182
surpyval/univariate/regression/regression_data.py,sha256=GxBSoeVp6Ue_-v32aoo2TyFGL69SnsP8h8VPQvbneWA,8351
surpyval/univariate/regression/semi_parametric_regression_model.py,sha256=kWwFVpyNyA_NbYp_cRmMpew9kfHPvmk0Hn3E-0pHbLA,10593
surpyval/univariate/regression/accelerated_failure_time/__init__.py,sha256=u5ETnJyDulyOlWfwMJhsrD9T8bkOLOhF5ZjKWGHPhdo,601
surpyval/univariate/regression/accelerated_failure_time/accelerated_failure_time.py,sha256=AsAIopUNs6bu8evnRGC-dsGUbo7IoIl8QYTrTinCSvQ,7372
surpyval/univariate/regression/accelerated_failure_time/aft_fitter.py,sha256=SOtFDEBe1sBI14l0Yt9KZahVhwCaMMLxk1AxnRvS-0k,6143
surpyval/univariate/regression/accelerated_life/__init__.py,sha256=Ws7HuifKq7L49JRNh_5SD5DznYtQ5TiQsCOsSJBzdr4,735
surpyval/univariate/regression/accelerated_life/accelerated_life.py,sha256=_PcXvdwuM8kfKEJ4ZOwzeL5Qg6MP1S9cr8bW9C3soU0,1970
surpyval/univariate/regression/accelerated_life/dual_exponential.py,sha256=AxvW-55AlupI0P2G1ZaSouq10sssyAqq71qHPDEOwz0,2931
surpyval/univariate/regression/accelerated_life/dual_power.py,sha256=ctOqnzy3_RffYrYCyGc-JccaWb135E4JmQHENBEwJ44,881
surpyval/univariate/regression/accelerated_life/exponential.py,sha256=z7MHrsokFQQFLmVrp4qLi7MgaPSyxjchX05Cbn1k-Ug,1263
surpyval/univariate/regression/accelerated_life/eyring.py,sha256=igsgwClgHvqtlaxQfZOaPJchAqyyKrWwxyZy6xIvskA,1242
surpyval/univariate/regression/accelerated_life/general_log_linear.py,sha256=pYpU0J2PcP4Jcgq0mzFw0kAGBffswztlDaBSn2vuDm4,659
surpyval/univariate/regression/accelerated_life/lifemodel.py,sha256=NBZyAze5mk3AI7vDtcamZsX-OEvD_QrTIVE76_qz8xU,519
surpyval/univariate/regression/accelerated_life/linear.py,sha256=NvNn7fSYAmwSMtTE0mq3H1fUSYntB1eiVMQKVq-7c3c,620
surpyval/univariate/regression/accelerated_life/parameter_substitution.py,sha256=CAIEEkCl37TH-3VEqTz2uy67gYdqu9CCJuLndiD1VgI,11312
surpyval/univariate/regression/accelerated_life/power.py,sha256=SWMLYb39xw6j-3conzjW0KiVdsuMctBaSlTfj-xlB-U,1122
surpyval/univariate/regression/accelerated_life/power_exponential.py,sha256=3mme3mNKelYmdSM_GiQCj0t7AUQ1xuGADtVhGtle2EM,976
surpyval/univariate/regression/additive_hazards/__init__.py,sha256=m-bFc-OVvLQXE8ze0ljZg7xAIdYOApYVcgmsAo02Q6I,1565
surpyval/univariate/regression/additive_hazards/additive_hazards.py,sha256=ydtBhDr9PqQFD3W5bMEzhw7fZqarqoe59y8Z3wqgwv8,14190
surpyval/univariate/regression/additive_hazards/additive_hazards_fitter.py,sha256=0x_i3M_KpXtM4UKmRA5817bEMjOo5HMJWAPhYMXx_YY,10725
surpyval/univariate/regression/buckley_james/__init__.py,sha256=D5HhjatvkpjlirDWv7BPzpwCbj4POCvP7qc_B94kulE,108
surpyval/univariate/regression/buckley_james/buckley_james.py,sha256=KCDEblnOBO0FjJHdB8iVEfptpyqilWI4atfPmvJdkbg,15442
surpyval/univariate/regression/proportional_hazards/__init__.py,sha256=ZAKGoDZ-pTcsXF9Cj1EUfn4U9TQ_lPi7ntF0MYqszGE,1530
surpyval/univariate/regression/proportional_hazards/cox_ph.py,sha256=hPKQX-z9MEudp6pAcDclpEEdzhFiQdgiLAVpsJ96cv0,21976
surpyval/univariate/regression/proportional_hazards/proportional_hazards_fitter.py,sha256=qsKPExfqidi6QQlRe940Ljb8C5sccxibFoXiJOgGxO0,10539
surpyval/univariate/regression/proportional_hazards/tvc.py,sha256=yHuIa2kFstncsi0L3VmoiynhOtpHUt76gVdWRV99nCk,5424
surpyval/univariate/regression/proportional_odds/__init__.py,sha256=NkEbhe1_KTRtBc7n135bkqnXtmUL-yb7V-uHODTd9FE,609
surpyval/univariate/regression/proportional_odds/proportional_odds_fitter.py,sha256=nyFqtK8TZr-yzjvJbOWb2eSdlFKUHILWUWWC-SEEwAw,8135
surpyval/utils/__init__.py,sha256=evQQHyc3QmvGDw83GAdD9h1JtFNY1EYRayDKmoAYvfk,43265
surpyval/utils/autograd_gamma_compat.py,sha256=_LBkegKp-duNmzZzq7aVIpMCafXDhPVkHEcTJ3PyUHo,5859
surpyval/utils/fitter.py,sha256=ZKvs_WRiA3IkAxqfwzVTNi1iZEk16WqtFOiN9nJOxQA,1571
surpyval/utils/recurrent_event_data.py,sha256=siH3v-Ds8ToZ06IEMNDcDZfy6lNZ4IFStIf427HKJqg,14005
surpyval/utils/recurrent_utils.py,sha256=7HNPk_-NTjQwz7gCbI_tq3miRy2-vNe83eXjdFswkcU,19431
surpyval/utils/score.py,sha256=5ZOQ-Rd83_aAEkE5_t--xvN2E1e_tE2C10x7WemQPz4,2967
surpyval/utils/surpyval_data.py,sha256=ewpBt8TLdebJYg8QzwkhFIEtHQMBzcnQ4cRNPlihaxY,9767
surpyval/utils/surv_sksurv_transformations.py,sha256=q2sz44hN9SuLjUIji6b8widokNyqukAafRG2vo9WnLI,657
surpyval-0.15.2.dist-info/licenses/LICENSE,sha256=mMg_2fuwRTO7USfXHAdWWxTvdFPrC4zd9NszJBgA1PQ,1055
surpyval-0.15.2.dist-info/METADATA,sha256=ytrJBXn4UFB3NE1VNAjMFPr-6v2A8AuJLNGjj3F85vQ,7954
surpyval-0.15.2.dist-info/WHEEL,sha256=K260EYznzXsJYBQGqmI8VTxEdiZYNvDZwW9cBh9-_MA,91
surpyval-0.15.2.dist-info/top_level.txt,sha256=ZKe-oyHKoUx665hqakVMJnzB_N4r8JODTtodkxkA7WU,14
surpyval-0.15.2.dist-info/RECORD,,
