linreg-core 0.8.1

Lightweight regression library (OLS, Ridge, Lasso, Elastic Net, WLS, LOESS, Polynomial) with 14 diagnostic tests, cross validation, and prediction intervals. Pure Rust - no external math dependencies. WASM, Python, FFI, and Excel XLL bindings.
Documentation 
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{
  "test_name": "Cook's Distance (Python - statsmodels)",
  "dataset": "prostate",
  "formula": "lcavol ~ lweight + age + lbph + svi + lcp + gleason + pgg45 + lpsa",
  "distances": [
    0.0020552983730474277,
    0.005157029462219868,
    0.008404348729187746,
    0.010439400959310589,
    0.011835344225904668,
    0.013448438634844491,
    0.0068311105343344305,
    0.014400561704473671,
    0.011988606644825152,
    3.866500842760251e-05,
    0.00040635627331123933,
    0.022333921407975,
    0.00922576572285748,
    0.010955505162608459,
    0.0017915698283331356,
    0.013340503562731351,
    0.011455207963484355,
    0.023446075787505752,
    0.013993034426380937,
    0.0008704232872740978,
    0.003107537730245602,
    0.0136572824837634,
    0.007243718114635241,
    0.007324060157608717,
    0.00018516317988315133,
    0.009533790566111906,
    0.0007640571148134618,
    0.01052825562422346,
    0.0016784581778771456,
    0.01129371116646261,
    0.002311084375212974,
    0.0014005032499504785,
    0.0073627504126652895,
    0.004187562805161193,
    0.013174657917586164,
    0.007508732457766227,
    0.07240548079465793,
    0.016907445781541356,
    0.029504093474845318,
    0.004351033328242149,
    0.010242806758950604,
    0.0007524536398748692,
    0.0022048302375425105,
    0.0007027798904438226,
    0.00040029056481261804,
    0.004539229926691506,
    0.008264148202630458,
    1.9785489735498988e-05,
    0.03434786880384171,
    2.5686742637648704e-05,
    3.92783803590615e-05,
    0.008905964883721024,
    0.010850798033491895,
    0.0005512533684020152,
    0.05515045432057453,
    0.0009576169216175917,
    0.026399456169148384,
    0.0013254499816746574,
    0.00806691463186952,
    0.00046512950149248605,
    0.010852511492094844,
    4.543612508091602e-06,
    0.004844940185405533,
    0.00018248486503332344,
    0.008263210159427364,
    1.2216609623834086e-05,
    0.000991940834561177,
    0.004060635676121936,
    0.1324823442428551,
    0.004937365661421921,
    9.519419406986755e-05,
    0.0024739111040701063,
    0.018125166291179544,
    0.0006540820824535399,
    0.001393888116662898,
    0.0057104553456610195,
    0.0002297502356442764,
    0.01872921701042776,
    0.00043750777404741125,
    0.008715487139026153,
    0.009647197507915359,
    8.054884300902875e-08,
    0.0005876942737373845,
    0.00030720165337734724,
    0.005980222720211342,
    0.005953301737455237,
    0.009686020115046741,
    0.005197821780717073,
    0.0017325550708604605,
    0.022043817098226174,
    0.05690270827570004,
    0.05105983731940326,
    5.541284524351261e-05,
    0.0401729495953096,
    0.04295232405764093,
    0.004142171234042094,
    0.02951757309738325
  ],
  "p": 9,
  "mse": 0.49058749077511726,
  "threshold_4_over_n": 0.041237113402061855,
  "threshold_4_over_df": 0.045454545454545456,
  "threshold_1": 1.0,
  "influential_4_over_n": [
    37,
    55,
    69,
    91,
    92,
    95
  ],
  "influential_4_over_df": [
    37,
    55,
    69,
    91,
    92
  ],
  "influential_1": [],
  "max_distance": 0.1324823442428551,
  "max_index": 69,
  "description": "Measures influence of each observation on regression coefficients. Uses statsmodels.stats.outliers_influence.OLSInfluence.cooks_distance."
}