Initial release.
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.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".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.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().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.