aurora/__init__.py,sha256=F8P7q5b5Kw2hn1MAjT0zWt5Qq_Ko9pMVWoOkHYmFfE8,4354
aurora/helpers.py,sha256=RgqCMuyUY418hvDDPO9KJ7nqkLlLGKseSc9EMal0kys,19471
aurora/core/__init__.py,sha256=6C3MN0GDMIvDVPms7uU9MltKbgAK5nsQILXK4DfUCIM,3999
aurora/core/types.py,sha256=zwhxd9zNFf1xjoimuSxa7OQHHzxGYXDEk_HVcvul9cw,3324
aurora/core/autodiff/__init__.py,sha256=VT7z7yT3YKbuPQTXNhPWPIZb6GL6fSdUMkXslq1bzJ0,3341
aurora/core/autodiff/backends.py,sha256=vxJ0pdkp9G6Lety8c3FgwOQ4LSZE4Zzy1Hkq2UailGg,1131
aurora/core/autodiff/gradient.py,sha256=6-SVdzY6BDiuMUC5MYrYn-AjD3h9iUGKLPXeISD9qa0,4086
aurora/core/autodiff/hessian.py,sha256=e_Xf_PTruUMuV24dTYtydKcdek70_LVNhJb5SZCp77c,3344
aurora/core/autodiff/jacobian.py,sha256=BL1_14e5xH5IK6XUaPFQL0-72qqn3GqL9H37oRu03ZA,3280
aurora/core/autodiff/products.py,sha256=d6H8cgN1IIhgAFE3oy4fMqTftlAX4Ftfb3RCnhYDs_U,5009
aurora/core/autodiff/utils.py,sha256=Nyk4G9kGT56jvretHyAc4iSKmcLxIx_QQXbUNc8vtIs,1779
aurora/core/backends/__init__.py,sha256=gnYurCKbIUE3yzhZwnsOQdAZqE9h2FYuhBvhP8rSmfg,2360
aurora/core/backends/_protocol.py,sha256=WcXltcL6AWi1epmPCnDJN0ts-0xXUcH1d9zDJGhtg7s,3863
aurora/core/backends/_registry.py,sha256=befg10xWlNtwlL6pCXyH90kdUPA052H0MpJQybN0MKs,5122
aurora/core/backends/jax_backend.py,sha256=-2isyUqeBJkap1-dtxYYexGo5giKVx15yLlgv8DR6_E,1683
aurora/core/backends/operations.py,sha256=Kaer192muVyJymkz0g7Ucw3n3097auAOetSQ1oughEU,18583
aurora/core/backends/pytorch_backend.py,sha256=2hEcRE9mlue40AIypo8QWagKfEy-U08AdFo4t8PQPho,4400
aurora/core/linalg/__init__.py,sha256=a39OLlweCPsFMHFhgwtLZk5uh2qVABFClZPcN14PuqE,14775
aurora/core/optimization/__init__.py,sha256=H522bFpQp-QfRcSd9YpXXRLbx8O0_ROw1GmaLOuM5XA,1344
aurora/core/optimization/base.py,sha256=Qe-7bThuhDkqXDNjTDcnRXiP_Z2Mo02ybidQsq9Xapw,740
aurora/core/optimization/irls.py,sha256=AHuswfDZTnyxizkYkCGa69Ft2PWK5R34jsccB4309fQ,22066
aurora/core/optimization/lbfgs.py,sha256=o13P9NDX_sLvkkDt6CY-TVaTf5jZFJ69_bnsCxlmj7M,24305
aurora/core/optimization/newton.py,sha256=BSPBCZ94ZseqbCWgxolmbGAqIh0pA3fID_c4OOL-Fxo,24109
aurora/core/optimization/result.py,sha256=qdE8vCCiFRL2RT78-3j0ts2_rIJ4_N90Ly_SCSJ-sBg,983
aurora/core/optimization/sparse_solvers.py,sha256=7CwRuipg2n4sOIReJ6X3fg7i4gu9FOBRwni5MM37rRE,13943
aurora/distributions/__init__.py,sha256=0nFWB7H5Ptv_wJ-_5QwOkOKcMhJA3FNlyGFWpEFtTWQ,6651
aurora/distributions/_utils.py,sha256=KLg2j_rcqek1AHdnQFvqmc3uUnpwRImJf5kabMwgxDM,7391
aurora/distributions/base.py,sha256=W0KNr2OguuQAaIOtQ63q6XlFCna-U-JE_ZEHOPFctNM,7998
aurora/distributions/custom/__init__.py,sha256=zeNT8OzJkOhoAQTT7BRjHX8U5CLhioQivwEjai1qC-0,120
aurora/distributions/families/__init__.py,sha256=_kP7ESZ7Jy2pgSHlqfZ67EZHD7cXZUrORfEcKeSiGRw,1836
aurora/distributions/families/beta.py,sha256=EP8UmTUbyGc6q9t8Kegef5UsC3qwEfM5ExUTL0r41Qc,12916
aurora/distributions/families/binomial.py,sha256=EaEIMlHZ1Yef2_nVdncksIEqV1q8n5t_-jw1dHFFETA,4585
aurora/distributions/families/gamma.py,sha256=7mn2FSoXIuXcDbsNtLRRfHnpRwlZGLNbRvjLTkbapng,2898
aurora/distributions/families/gaussian.py,sha256=aSlg1BRXBd0ZC6FUjigI0q6Rra_RU0mzhlhb5UPl00M,2248
aurora/distributions/families/inverse_gaussian.py,sha256=UEsuZuoEWXvMxzVIbV8JftdIxaXEN1nB1hJzL-dfOPk,12466
aurora/distributions/families/negative_binomial.py,sha256=AesTvGL3d6ZHff84wrzVb3hzN9cYxQApdT2fN2sfVco,15436
aurora/distributions/families/poisson.py,sha256=RQVSRkREucLL3VhshBh0dVgXVGMBYKal8nuNL5s1L7o,2541
aurora/distributions/families/student_t.py,sha256=bxMBRsk1XhUxx5KsPwfEhp_GpSfCKj4H2TC7OpstxA0,10832
aurora/distributions/families/tweedie.py,sha256=QlDgULXLixj4JbY-KMxnrgU9dinSoaK6PkZazekkNkI,18740
aurora/distributions/links/__init__.py,sha256=TNWYK2k6WevdZmW40REjIchxB6GNwgxrMs2A4F_07SI,1055
aurora/distributions/links/common.py,sha256=Bk0MzkJzXZVY1SQyaqA05gr3tyaOzLLhzzWPgSu7vfo,12844
aurora/estimation/__init__.py,sha256=hican8qsD-V-K4lgvudT3fkWim9B5ElwPCNYGNpMzhc,5267
aurora/estimation/laplace/__init__.py,sha256=SkO-V-8oFAjm1dkCdZfjc700BtCRtSun0VK8RydN4gk,111
aurora/estimation/ml/__init__.py,sha256=7NRb9tWtGSokaEcGRWbH80A0cli8zwwgBwJ0nEnu9bc,118
aurora/estimation/reml/__init__.py,sha256=fCkDOOKpZeX61ei_iwrIaO4_yg1Di9cK0Zo4PhPfXdg,129
aurora/inference/__init__.py,sha256=d1a4_AprFEEIBns82NmOxGOgdHZtvFce93u-jb_NWCw,7002
aurora/inference/robust.py,sha256=I7rC9RkAk_-CF7DM_Dh9XLSdiAPuQNYkZ_TBiPL1Mf4,12599
aurora/inference/anova/__init__.py,sha256=v4TRuydxmfwFqSKd23h13hbVMeTqAPegvIJJQR_CcH0,13993
aurora/inference/diagnostics/__init__.py,sha256=nbbPOqS9GQqXFJMWLBVSh7cXtpMkIRhVLflm2T2bCqs,215
aurora/inference/diagnostics/glm.py,sha256=mgdo6GfX9hLxVZ9YIfNdH4i44zrzivw8U86QN1XB_90,7016
aurora/inference/hypothesis/__init__.py,sha256=xfLIAukCzH3Nu7xo7D2SAGqW9n6jgSS7I7r7BYo_mjc,161
aurora/inference/hypothesis/wald.py,sha256=R61h096IaioA-TVfQWCnrPqUptZbKBnp7xZgsztG230,5696
aurora/inference/intervals/__init__.py,sha256=2WtPxH5eRBiuwobfvfOhkAj-m1hO8koCNqwK1x0rv5M,257
aurora/inference/intervals/confidence.py,sha256=v4fDDA8jdG4rKZh8_8ArvwgsEriVXCvKR_BmJeY-oxk,1850
aurora/io/__init__.py,sha256=rfIG3V514o3jncK9hAhsLyQ5IG-irXuRf-odgqAj21o,1482
aurora/io/converters/__init__.py,sha256=KtiDH7HP7QzxieVSH0V1g0pTRiWc0YCGDYoD4Rwz7aw,103
aurora/io/readers/__init__.py,sha256=cVVb2wB5iqbSyrjE598x3201uCdN0Yz2kULioy9XzMk,11007
aurora/io/writers/__init__.py,sha256=vuXUZ0qXW0ScgcDlWU_ewjR9kemChurnZYp61rvvUoo,9444
aurora/models/__init__.py,sha256=1-hILUO55AloKR56AjzAx-bsve6O-QkSILXd2xdYzSc,6008
aurora/models/base/__init__.py,sha256=pRKEr9laWP1sHNVSu-pPQoRsEAk7DcK42vYrVq7q62A,927
aurora/models/base/base_result.py,sha256=EMewg0Xw4mLGinQQ95p-espHQTH9mvIIqzWXltKbvWU,17551
aurora/models/base/result.py,sha256=MDvQn8sSKzuz95Oej-S5aP1TsTHIa_iNkRKOBCj5e8M,25487
aurora/models/bayes/__init__.py,sha256=xTxvnkN1OT5GSsAq-t71MYSlEk_S0YeEYaef-_pgD7g,1755
aurora/models/bayes/glm_bayes.py,sha256=mTK1DYop565q-ldZn_a0vANXhUXGAKxxBGFkqXJMbiU,7759
aurora/models/bayes/priors.py,sha256=VXQJsdgknhUeW5XzkUUesTIGhw_cfNmuW1vt-c4sf9o,11131
aurora/models/bayes/result.py,sha256=F_Cr2vb0WJ6L68zVqZMPxOrEuuo1Ed9SpBQ6AvFCD3Y,16568
aurora/models/bayes/backends/__init__.py,sha256=T4FVNpTkySsW1lJ54c1BIsrxLH4x3zbWd2Qc-LQqEJQ,1167
aurora/models/bayes/backends/numpyro_backend.py,sha256=lAFXKVSeW1YCIA5ERBq7ung-tK3B9r7pyH0FYcPOOUM,6544
aurora/models/bayes/backends/pymc_backend.py,sha256=0E27qcipcamEkp_vcRASxHhysISstFxbho3sXCSOaQM,6961
aurora/models/distributed/__init__.py,sha256=jz_RTNnl-vpRymzcRmif9hsdh2IEBzikJyniifJJY1g,1757
aurora/models/distributed/chunked.py,sha256=cEvnGi7V_qkSQUp5KKZJebJfaqpjxU0zuMPp5hlYHEA,8316
aurora/models/distributed/data_parallel.py,sha256=z68gTXJqwgV2jpWxenPJkOK0pdFGSMDiI01i05Cha0I,9959
aurora/models/distributed/minibatch.py,sha256=MjV6_h0Dc9Ih_PTfpFd3jfxcxRrgAJvaLXJUvDmfMiM,12239
aurora/models/distributed/optimizers.py,sha256=Ryv4mUwsClc9Xj1Tt-8vIbxQHR-DOoZMtigdmC0nqoU,5721
aurora/models/distributed/backends/__init__.py,sha256=CZyrGHlRgMlLc5JFD4ENTWkA_BvB8clDrzhfGlnIXWE,761
aurora/models/gam/__init__.py,sha256=hNESyFwrHYfTUs38FKVlxSold8zL3VcGvAmNUc-mV20,823
aurora/models/gam/additive.py,sha256=FrHbYffwB9P834kwiQJlVsNB6o00Ug8erbuielbryJo,25677
aurora/models/gam/fitting.py,sha256=5dynqytixd9qg0FX4KLIqrzYk9swbtyTwAe45alMHWk,16635
aurora/models/gam/formula.py,sha256=toMyF34QLYpLvfgWzrlgMvySfmfLjfgIGLXmqieUegw,13732
aurora/models/gam/plotting.py,sha256=pES7pEdleVRweLPR0AjWThiHOc_3qHCESoLRJmiAExI,11844
aurora/models/gam/result.py,sha256=NyWgfSj4m5YYHABf9H4USG7M0khcMCn2vzHfzH_qxFU,5538
aurora/models/gam/terms.py,sha256=vZAC--AOAUUCB7SOBeFvNjV52qAVSGrNoLPeLcE0TQ8,6812
aurora/models/gamm/__init__.py,sha256=MXcs8r5Md5iG9q6vWiC4hHEI4Sm8wjE9cwqukWvqCRE,3091
aurora/models/gamm/covariance.py,sha256=QFuRcutDoDkydjYjUq64wrpXk4GIWA0L16gvcpXO2l8,34602
aurora/models/gamm/design.py,sha256=a9UMcXRyCpYNYB0k6j-FmhHEQTouSvC1YJ6TAg73Lew,9675
aurora/models/gamm/diagnostics.py,sha256=jxdr9kiLEROJB0GHry4abaK7OkFqu2gQiM4ZoeueYqA,15268
aurora/models/gamm/estimation.py,sha256=DVipp2mHb2C9bU6prHPGbUQItdQdAyZwnCgtrs3qpR0,18903
aurora/models/gamm/fitting.py,sha256=qGKf_Ig5Yiw1ZmVo6-msaepda6ro0kIacq3nYWmvhuI,41565
aurora/models/gamm/interface.py,sha256=EZ0HfTwU7ibqorWbVHyUCjtzuOjc10Ngkm0yJ-NTpf8,32565
aurora/models/gamm/laplace.py,sha256=Xz8RxFWwNcr3wu5JtANLI1iviBNTXz-kDQNhOZQwJHk,16627
aurora/models/gamm/plotting.py,sha256=oSV6UvSBzIO6XhakSaD3YMvev3nR5VxJH4n89z6bAA8,35317
aurora/models/gamm/pql.py,sha256=fMUFeD_PcHr-HdcBVrnZlgDOn2X3-zjfnZbIBRrgzM4,27493
aurora/models/gamm/pql_smooth.py,sha256=0xEdLyV4k49SWdgWw4PoyBS6z7Lw_RhSdnuZmbQTyrg,17983
aurora/models/gamm/random_effects.py,sha256=2ukFeAupzc8cD96xRewHgZki7MNbD5q4WNqGqkw4MeI,9535
aurora/models/gamm/smoothing_selection.py,sha256=I8qjToU_yhn4FuFZieDymvaS4J3QPzJTD2QWoSJZ1KY,14846
aurora/models/glm/__init__.py,sha256=48xY3k_IiWVE5HKJylCyUNlepOXF9vcsDQa-keiL5dg,310
aurora/models/glm/fitting.py,sha256=gn5yfhjeE8TyU0sVaX9BhF8YYsWDR-CXoDJYSmU5gOQ,20721
aurora/models/glm/prediction.py,sha256=1zijLwRKS8Wz9vkCbUMEYELTrUY5EIahZZemJYEwoow,2420
aurora/models/hurdle/__init__.py,sha256=zxDM5qzNqkYcdsUJ5U3JakVe3TFmKHgATwC43LWv-vo,2122
aurora/models/hurdle/hurdle_negbin.py,sha256=1R4x7G-YbvLbKS6pbBWLxeKM4YF5Gi7YBBD0Wvt-pqk,10910
aurora/models/hurdle/hurdle_poisson.py,sha256=aNIYlpg7RT-2UmZRzVfpHmc3LIusWmWIcwehy0OpvLo,11048
aurora/models/hurdle/truncated.py,sha256=liQAeL6hGOQUXwuxFDgwOjcMdEWkiROVILSouF0OAfI,5840
aurora/models/zero_inflated/__init__.py,sha256=x4QMBcg54jGDumSlsInfeHkQWvLf4VZZPg4sCionyl8,2014
aurora/models/zero_inflated/diagnostics.py,sha256=GMrlxabaXwfxrRwiHRAMEJFlTW4Ve9YkyXljHiOqUCM,14609
aurora/models/zero_inflated/zinb.py,sha256=QQKMAeir_kDXlZH34GAXoX-eOIgRwSZmogofEgWXFD4,16922
aurora/models/zero_inflated/zip.py,sha256=RePp4TK8A4r-OccexbLJQsxf5bDQ4Ws9jsQHGsTzAvg,16362
aurora/smoothing/__init__.py,sha256=S7uCaYD7LHHMKtazyuDLlWn4P1DNleFyOmdmaIQIAIQ,6121
aurora/smoothing/tensor.py,sha256=nSN9bHGCB9klX0cFa23vNruOJ_dV6vZ12T1wKYnDcdw,7994
aurora/smoothing/thinplate.py,sha256=7Q9KovP_-CUqXeBc6kEwNuAkyH-Xa5IXX2XRiXywe6U,9401
aurora/smoothing/local/__init__.py,sha256=h3lSfumEdtJX40YWWWXu4Q5GmLf4jkkZvhg9Schq6L8,401
aurora/smoothing/local/loess.py,sha256=PX4roqg_fUg-smrO8GLKR4dIu7PzVZbQIY99AJamEmE,15138
aurora/smoothing/penalties/__init__.py,sha256=tmWNn6Dxp9GAnfvtEDsEbvwg7HTYJ-od6hno5oY2Nqk,124
aurora/smoothing/penalties/difference.py,sha256=Q9vIMBt2Keza-eGK4UVuVABfiKHb-8LpySo_ILy4tkk,7877
aurora/smoothing/selection/__init__.py,sha256=RT-5CajhopiYyQMiLS2MxjQN0djr8fKDE0EyO2vGn40,558
aurora/smoothing/selection/gcv.py,sha256=dQu5PwXDAxVFfDwaxxbw-pcu72wvkVujitmcz80Tp4E,6851
aurora/smoothing/selection/reml.py,sha256=iZ__4iUEWdaxbUCfDVgJIz6-xPwfM5tX3e_x4wALXiI,13768
aurora/smoothing/splines/__init__.py,sha256=n3P1gdMNxen-QLrNThTcAydaNiHzEnja7RFmMPRXBqE,393
aurora/smoothing/splines/bspline.py,sha256=rsc6XYCaXXZLSp0Nm63DXgu_aczaozFbKDkUQb498J8,29531
aurora/smoothing/splines/cubic.py,sha256=5WLRz4DGUDQwGNjusq6BOOXH1ks2kPAawvs2WEEMC8U,9862
aurora/smoothing/splines/pspline.py,sha256=ClSIW0FdkJf3484Bvl9Y6MExj6tyFhVUbA4iGiJeypk,23035
aurora/utils/__init__.py,sha256=2UtC80i5KtwoqA5pDnMQWaVeJj_uhA7wgitYcJd_D-Y,321
aurora/utils/exceptions/__init__.py,sha256=cKHPkl5A82yi76DQJMr_ICMJRqSlnajrSueueqcABbQ,582
aurora/utils/validation/__init__.py,sha256=X2nCeVOwbvw_j2KhHo40H4mZb8i1-7mWZ5MHacxj-Ig,1920
aurora/utils/validation/decorators.py,sha256=G0EKJbeC_0b99J6rgUdW8FeiW7y00DZk4aAkhLy4VAo,23051
aurora/validation/__init__.py,sha256=KW4piWahBe3Vt80aFXZealSE8qjlClNkyRZvSwA69UM,1294
aurora/validation/cross_val/__init__.py,sha256=U0U7YQdNCdlgrc8Snv11RHSk_JVaL7Rk7jFy6we3Ay0,280
aurora/validation/cross_val/evaluate.py,sha256=KtWIsiewbrGoLd7ifQtVP1VCTNrfCjkcaSOxj1x-YRM,4173
aurora/validation/cross_val/split.py,sha256=4cWagkQ8VmL7DVJT2E1nHXXOMkxtOz6q-h-3xs4vkFk,4198
aurora/validation/metrics/__init__.py,sha256=Jr1LClh-O78kHZYoQxh2WfQhdsODL8htnryGoLbh6wo,843
aurora/validation/metrics/classification.py,sha256=o9DGo_2_MmevW83ATghsBxFKOaCM0Re3PrFb1Vx9GgU,12722
aurora/validation/metrics/glm.py,sha256=5dzWYqQmr-4DEZYI5Smitb6WpHWdzYlm1XuZYGrL8H4,3816
aurora/validation/metrics/regression.py,sha256=pEACcypXk2x4bHM-6rlaGkGP6Xlf-x1Vp0UuZfo6LhI,4097
aurora/validation/sensitivity/__init__.py,sha256=FYBuk3iAqsxgji1ry546ZASdNvaDUu8qpWJNmT7W0yg,15161
aurora/visualization/__init__.py,sha256=vcbs3GEOftwWWb5IB9mcrIvXGwe5gwkw3J-qBz0MT5g,2024
aurora/visualization/model_plots/__init__.py,sha256=NF7vAlZTyaYZDA-wBjgU3PfQ1jO_wwqZkXevnyeIMhQ,109
aurora/visualization/predictions/__init__.py,sha256=l00OHoVzK3BQfEPg_xw2XBBw8x9scZzorNIu3D15iTg,114
aurora/visualization/residuals/__init__.py,sha256=4qrCO-tiLgWor-1siji6meI08TDGvgjGgAFSn3oo4N8,105
aurora_glm-0.7.0.dist-info/licenses/AUTHORS.txt,sha256=8JkoL3TVpr0lnaz8khmLhbkhsBLtioAQaBgkyrucrv8,458
aurora_glm-0.7.0.dist-info/licenses/LICENSE,sha256=uYFZHjof0NfqwxkH3bIijLgU4sH91Mfkr5hYFqVPWv8,1096
aurora_glm-0.7.0.dist-info/METADATA,sha256=qnqITaeTl6R82ASoW9BudqsQg9VMJx_x4tSn_ucORlM,42385
aurora_glm-0.7.0.dist-info/WHEEL,sha256=_zCd3N1l69ArxyTb8rzEoP9TpbYXkqRFSNOD5OuxnTs,91
aurora_glm-0.7.0.dist-info/top_level.txt,sha256=_VPZyby2U0daT4zxBQyOTduleUKBlGa_eQkjEGBjNyE,7
aurora_glm-0.7.0.dist-info/RECORD,,
