# enetLTS v0.1.0

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## Robust and Sparse Methods for High Dimensional Linear and Logistic Regression

Fully robust versions of the elastic net estimator are introduced for linear and logistic regression, in particular high dimensional data by Kurnaz, Hoffmann and Filzmoser (2017) <DOI:10.1016/j.chemolab.2017.11.017>. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied.

## Functions in enetLTS

 Name Description plot.enetLTS plots from the "enetLTS" object plotCoef.enetLTS coefficients plots from the "enetLTS" object coef.enetLTS coefficients from the enetLTS object cv.enetLTS Cross-validation for the enetLTS object enetLTS Robust and sparse estimation for linear and logistic regression fitted.enetLTS the fitted values from the "enetLTS" object. lambda00 Upper limit of the penalty parameter for family="binomial" nonzeroCoef.enetLTS nonzero coefficients indices from the "enetLTS" object plotDiagnostic.enetLTS diagnostics plots from the "enetLTS" object plotResid.enetLTS residuals plots from the "enetLTS" object predict.enetLTS make predictions from the "enetLTS" object. print.enetLTS print from the "enetLTS" object residuals.enetLTS the residuals from the "enetLTS" object weights.enetLTS binary weights from the "enetLTS" object No Results!