raiseR 0.1.0

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.