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": "prostate",
  "formula": "lcavol ~ lweight + age + lbph + svi + lcp + gleason + pgg45 + lpsa",
  "dffits": [
    0.13533953868002122,
    -0.21484025043260732,
    -0.2741286221867118,
    -0.3066324625617995,
    0.327625429621321,
    -0.3493507553336729,
    0.24838899667442074,
    0.36167815668559766,
    -0.32865902140247877,
    -0.018548911052905438,
    -0.06015637278451838,
    -0.45839702684156836,
    0.28952974322176533,
    0.3145247020202415,
    0.1264455113226541,
    0.34849034018755587,
    -0.3241474183126503,
    0.46322246997261446,
    -0.35481352987900305,
    -0.0880830747324891,
    0.16705656159887866,
    0.35086971903109276,
    -0.25727253683751394,
    0.25638017247813283,
    -0.0405981323982596,
    0.2935677342949313,
    -0.0824749992565893,
    -0.3094748682454973,
    0.12227588867575229,
    0.318083208904112,
    -0.14371200391180097,
    -0.11183283895921851,
    0.25682052029656555,
    -0.19395091777257176,
    -0.34730652179952265,
    0.25967860286858185,
    -0.815203445608095,
    -0.3898348759159493,
    0.5180253825161104,
    -0.19722037474069032,
    -0.302392715323982,
    0.08187230564245805,
    -0.14040978533109938,
    -0.0791041650280594,
    0.05970631762131048,
    0.20207198601854423,
    0.271625203618968,
    -0.013268581994957847,
    0.5607310013775685,
    -0.015118327971974856,
    -0.018694918609714548,
    0.28362334884303747,
    -0.31249850318524597,
    0.07005898545172448,
    0.7326697492805693,
    0.09236990918281925,
    -0.48899219135095146,
    -0.10866801032340254,
    -0.26979677267469687,
    0.064356195059,
    -0.31260748031591723,
    0.006358302243188914,
    0.20785875314688892,
    0.040298355697960184,
    0.27299015075154465,
    0.010426120085142994,
    0.0939888887164347,
    0.1907132827827713,
    -1.1425646352296253,
    -0.20997994924228552,
    -0.02910520613983811,
    -0.1485215706441164,
    -0.405072771281607,
    -0.07629824665808868,
    0.11143868716009589,
    0.2260977969326586,
    0.04521861024988546,
    0.41179029879500734,
    -0.06240562407655668,
    0.27987013355920604,
    -0.2943710910588971,
    -0.000846582348194938,
    0.07233251168977585,
    -0.052286042663949206,
    -0.23165922745563317,
    0.23106193140090966,
    0.29517479474868036,
    -0.21569781387017156,
    -0.124213999231827,
    -0.4463638770981085,
    0.7253253246816661,
    0.6815805644030067,
    -0.022205360984404406,
    0.603990687016847,
    -0.6243911223163177,
    -0.1921972059532901,
    -0.5167839627430427
  ],
  "n": 97,
  "p": 9,
  "threshold": 0.6092076990801715,
  "influential_observations": [
    37,
    55,
    69,
    91,
    92,
    95
  ],
  "description": "Measures influence of each observation on its fitted value."
}