| AIC.seqModel | Information criteria for a sequence of regression models |
| coefPlot | Coefficient plot of a sequence of regression models |
| coef.seqModel | Extract coefficients from a sequence of regression models |
| corHuber | Robust correlation based on winsorization |
| critPlot | Optimality criterion plot of a sequence of regression models |
| diagnosticPlot | Diagnostic plots for a sequence of regression models |
| fitted.seqModel | Extract fitted values from a sequence of regression models |
| getScale | Extract the residual scale of a robust regression model |
| grplars | (Robust) groupwise least angle regression |
| lambda0 | Penalty parameter for sparse LTS regression |
| nci60 | NCI-60 cancer cell panel |
| partialOrder | Find partial order of smallest or largest values |
| perry.seqModel | Resampling-based prediction error for a sequential regression... |
| plot.seqModel | Plot a sequence of regression models |
| predict.seqModel | Predict from a sequence of regression models |
| residuals.seqModel | Extract residuals from a sequence of regression models |
| rlars | Robust least angle regression |
| robustHD-package | Robust Methods for High-Dimensional Data |
| rstandard.seqModel | Extract standardized residuals from a sequence of regression... |
| setupCoefPlot | Set up a coefficient plot of a sequence of regression models |
| setupCritPlot | Set up an optimality criterion plot of a sequence of... |
| setupDiagnosticPlot | Set up a diagnostic plot for a sequence of regression models |
| sparseLTS | Sparse least trimmed squares regression |
| standardize | Data standardization |
| TopGear | Top Gear car data |
| tsBlocks | Construct predictor blocks for time series models |
| tslars | (Robust) least angle regression for time series data |
| tslarsP | (Robust) least angle regression for time series data with... |
| weights.sparseLTS | Extract outlier weights from sparse LTS regression models |
| winsorize | Data cleaning by winsorization |
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