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": "DFFITS (Python - statsmodels)",
  "dataset": "synthetic_nonlinear",
  "formula": "x ~ y",
  "dffits": [
    -0.28034381971061545,
    -0.2678034109568708,
    -0.2701140465774827,
    -0.23901691960968655,
    -0.23558215438290817,
    -0.22004225729916171,
    -0.21089337275406872,
    -0.20275286867307984,
    -0.1985070094231071,
    -0.18278851211087443,
    -0.190617052681754,
    -0.16000054424155355,
    -0.1578729149831471,
    -0.14576468294020484,
    -0.1570102579059941,
    -0.13421346514148522,
    -0.13481717071652705,
    -0.09118468034688522,
    -0.10710671842791927,
    -0.10629113895512053,
    -0.08554094068063571,
    -0.09090824220747706,
    -0.05598092043667251,
    -0.07858361944288854,
    -0.08328063596864205,
    -0.02965293687780557,
    -0.018405969609943278,
    -0.04604899195773047,
    -0.04028353940750534,
    0.01106571018973452,
    0.017413319858067607,
    0.022540480078696152,
    0.059096992012036,
    0.0554926748946103,
    0.06582626272205513,
    0.06667169609106256,
    0.0537730824074048,
    0.05324201904399363,
    0.027977083393237387,
    0.09975761483610308,
    0.05130133369326018,
    0.08728969506989345,
    0.0872577369522189,
    0.07217910216688063,
    0.12717310810887852,
    0.08678047205987655,
    0.09549563657724662,
    0.08945190169498216,
    0.09696720424905476,
    0.04935578798058038,
    0.12582481710197818,
    0.12360217476414313,
    0.12672677433950205,
    0.12495475404618776,
    0.12702890231537792,
    0.08253651498084352,
    0.0779431901226311,
    0.12287476291631458,
    0.12828190914311385,
    0.09656440052231328,
    0.11810376586636923,
    0.09157072674650298,
    0.09889480913370506,
    0.13442351488240195,
    0.0735419871446573,
    0.06922182301799133,
    0.10831053639839071,
    0.10262487612989386,
    0.0904460569822557,
    0.08878714925100877,
    0.08327692448615205,
    0.06298308260361321,
    0.09356200462796428,
    0.10130700308798213,
    0.10830181596229287,
    0.09864734417761714,
    0.08836045313783662,
    0.05809433154050621,
    0.09316948119841122,
    0.08070781483969491,
    0.08269436952787933,
    0.05777061323477296,
    0.02134434101059009,
    0.08411113466297557,
    0.08000061986377593,
    0.04024569968136351,
    0.042677469958754384,
    -0.0004671431026807315,
    0.028937556351145508,
    0.04709854396660168,
    0.007486730021958869,
    -0.0245306556555167,
    -0.028540634958730285,
    -0.03221058392045856,
    -0.07476156107949124,
    -0.10096535860352095,
    -0.13589120612522299,
    -0.12258569729706968,
    -0.10558727635595405,
    -0.14994024562667543
  ],
  "n": 100,
  "p": 2,
  "threshold": 0.282842712474619,
  "influential_observations": [],
  "description": "Measures influence of each observation on its fitted value."
}