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_nonnormal",
  "formula": "x ~ y",
  "dffits": [
    0.105784251485542,
    -0.07323920703841268,
    0.1491998239373619,
    -0.12774960379865138,
    -0.13830792518849766,
    0.04544481893529834,
    0.1735487073293617,
    0.11003731972031656,
    0.0025443518108384127,
    -0.4226084061546112,
    -0.002986689225163497,
    0.11083800006232808,
    -0.0029814726208673495,
    0.032275658517613624,
    -0.07496714586638646,
    0.13504171875688825,
    -0.06050389611827473,
    0.031167915194020047,
    -0.28194464082539733,
    0.0820372733612245,
    0.0007420057830707237,
    -0.20051316361605268,
    0.12241373741071573,
    -0.20160201220783297,
    -0.01794705828132964,
    0.1221812465395535,
    0.08241085182206254,
    -0.023807544759591067,
    0.11685476563144195,
    0.01803459281513662,
    0.07009323211731931,
    0.007263721223988043,
    0.11300371532796978,
    -0.10232811687545151,
    -0.25072818121054813,
    -0.23318447506464254,
    -0.033469549235011144,
    0.09544531965513992,
    -0.0355124266016745,
    0.10558304996838407,
    0.10469661146698787,
    0.06575675448140651,
    0.037803241629557026,
    -0.03748205832936911,
    -0.0073326640200475825,
    0.04519523935402546,
    0.070818794509642,
    -0.53554428414452,
    0.04843820982884145,
    0.044374016459996814,
    0.08359464139706754,
    -0.04548611972187478,
    -0.008351054390665852,
    0.07751147580957685,
    0.09291772390480485,
    -0.0038030346987952563,
    0.0037502026845035175,
    0.07312043438929408,
    -0.19938023286396195,
    0.08905960939318873,
    0.05238336099388196,
    -0.19091812953141182,
    0.05615026169072228,
    0.010472364279996142,
    0.06174836446785038,
    0.08115386947150371,
    -0.39155937840239524,
    0.06322565233999776,
    -0.12969262015358624,
    0.09320113509570772,
    0.09720688524277911,
    0.12077235922135189,
    0.010568282693162488,
    0.0789188817292798,
    -0.10817565257183893,
    0.06140563648241806,
    -0.3374730792913095,
    0.11571455779174418,
    -0.1194243844336593,
    -0.04528863028408098,
    -0.04222242650643246,
    -0.09956326452446235,
    -0.03687926362189815,
    0.02688169250062566,
    0.1421949514243702,
    0.1593269826535163,
    -0.22203074724749294,
    0.034261835269059425,
    -0.06455538204424331,
    0.08224894820001036,
    0.16306047369594925,
    0.1650580891557055,
    0.006962682813282952,
    0.10499075064809105,
    0.16118996568305158,
    0.09071804088753005,
    0.19308344679746706,
    0.06073949625117715,
    0.1593391798150666,
    -0.07298061660300932
  ],
  "n": 100,
  "p": 2,
  "threshold": 0.282842712474619,
  "influential_observations": [
    10,
    48,
    67,
    77
  ],
  "description": "Measures influence of each observation on its fitted value."
}