Changes in version 0.1.0 (2026-09-15)                  

Initial release.

Collinearity diagnostics

  - vif() -- Variance Inflation Factor, with type = "O" (ordinary,
    correlation-matrix based) and type = "R" (robust, the RVIF of Jacob
    & Varadharajan, 2024). Accepts a formula, a fitted model object from
    this package (or an lm), or a numeric predictor matrix. rvif() is a
    convenience alias for vif(type = "R").
  - cn() -- Condition Number and full vector of Condition Indices, with
    ordinary (type = "O") and robust weighted (type = "R") versions.

Regression estimators

  - raiseReg() -- ordinary Raise Regression, with a sequential
    single-variable method (the default) and a simultaneous SVIF/QR
    method. Because raising leaves the column space of the design
    unchanged, its fitted values, residual standard error, R-squared and
    F-statistic are numerically identical to OLS, and its coefficient
    t-tests stay exactly valid.
  - robRaise() -- Robust Raise Regression with exact finite-sample
    inference (sandwich covariance, effective degrees of freedom,
    Satterthwaite-Welch correction), downweighting outliers via
    Stahel-Donoho projection outlyingness and Tukey's biweight function.
  - ridgeReg() / robRidge() -- ordinary and robust Ridge Regression.
    robRidge() offers type = "MM" (IRLS with an MM seed) and type =
    "SDO" (Stahel-Donoho weighting, then ordinary ridge).
  - liuReg() / robLiu() -- ordinary and robust Liu Regression. The
    biasing parameter d defaults to the MSE-optimal dopt, with dmm and a
    manual value also available, and out-of-range estimates clipped to
    [0, 1] by default. robLiu() offers type = "MM" (following the MM-Liu
    estimator of Filzmoser & Kurnaz, with a closed-form biasing
    parameter) and type = "SDO".

Utilities and methods

  - scaleDat() -- scale a dataset by one of four conventions: classical
    (mean/sd), robust weighted (Stahel-Donoho/Tukey biweight),
    median/MADN, or min-max to [0, 1].
  - print(), summary(), coef(), fitted(), residuals(), predict(newdata =
    ) and plot() methods for every fitted object, modelled on the
    corresponding lm methods.
  - For raiseReg() (the exact, unbiased fit), the standard influence
    diagnostics hatvalues(), cooks.distance(), dfbetas() and covRatio(),
    plus lmtest::bptest() and car::ncvTest(), are supported and return
    values numerically identical to an equivalent lm() fit. robRaise()
    supports bptest() and ncvTest().

Dependencies

  - The robust methods (robRaise(), robRidge(type = "SDO"), robLiu(type
    = "SDO"), vif(type = "R"), cn(type = "R"), scaleDat(type =
    "weighted")) require the mrfDepth package, which provides the
    projection outlyingness measure used in the published methodology.