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
{
  "test_name": "DFFITS (Python - statsmodels)",
  "dataset": "synthetic_simple_linear",
  "formula": "x ~ y",
  "dffits": [
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    -0.041454646664723864,
    -0.20173676359098147,
    -0.3640092809760782,
    -0.016632220104946624,
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    -0.3527133807357307,
    -0.20259555175216748,
    0.03468745505196839,
    -0.15429179064973897,
    0.034114468151661796,
    0.034841006259349834,
    -0.0924792056207778,
    0.3305533034694081,
    0.28445121717516286,
    0.05303851590194556,
    0.13518724423263107,
    -0.09229322106232962,
    0.11316748650730112,
    0.20411227286665892,
    -0.2497250336699533,
    -0.0014557783384079102,
    -0.04489554009386772,
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    -0.046702832522921775,
    0.14218871519419354,
    -0.07926518749897322,
    0.05608491813132405,
    0.012715802744866663,
    0.05561206489021956,
    -0.24901186278418436,
    -0.02184118331503902,
    0.1171487665735426,
    -0.11988640492995521,
    0.13716889994115092,
    -0.04482062083410103,
    0.23972580100700727,
    0.14718766040280878,
    -0.04032475131653522,
    -0.10022241429895969,
    -0.03565838302601816,
    -0.0025769087751476858,
    0.018901265482536636,
    0.15841465062248072,
    0.06705631168963079,
    0.037861343504400874,
    -0.129771327097742,
    -0.049874838108586055,
    0.18870099748564273,
    -0.04681464316057501,
    0.03161073406146975,
    0.06423591597031879,
    -0.07847568130348652,
    -0.1280105440768769,
    -0.11660440838307586,
    0.08503639213385991,
    0.02703572387315262,
    -0.045958189900881644,
    -0.12549261228615755,
    0.04883909592414767,
    0.015859475207775076,
    0.12282426484474594,
    0.13492622430008266,
    -0.10955696136911942,
    -0.1887405760839126,
    0.005589795277969381,
    -0.14073898602489784,
    -0.05087356367649471,
    0.0814584913749088,
    -0.051298634846611124,
    -0.23571593518151893,
    0.004980526678673883,
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    0.3457344869146112,
    -0.1257642007547731,
    -0.010400426611555312,
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    -0.010076163856847262,
    0.29265693209194993,
    0.04085723648970929,
    -0.053043528229847724,
    -0.26455294087217385,
    0.09413190119148873,
    0.14341663953925288,
    0.09638122065214433,
    -0.1618496476953696,
    -0.048421312527538875,
    0.10905364952854267,
    -0.08488689929815431,
    -0.00278525221838344,
    -0.18310807686410346,
    0.15262387429090057,
    0.08448203532501625,
    0.09959549775374357,
    0.30777719634275397,
    -0.04023294061361456,
    -0.032100922960689046,
    0.025199070321619638,
    0.07932914890870058
  ],
  "n": 100,
  "p": 2,
  "threshold": 0.282842712474619,
  "influential_observations": [
    4,
    7,
    14,
    15,
    75,
    80,
    96
  ],
  "description": "Measures influence of each observation on its fitted value."
}