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robustHD (version 0.5.0)

robustHD-package: Robust methods for high-dimensional data

Description

Robust methods for high-dimensional data, in particular linear model selection techniques based on least angle regression and sparse regression.

Arguments

Details

ll{ Package: robustHD Type: Package Version: 0.5.0 Date: 2014-04-30 Depends: R (>= 3.0.2), ggplot2 (>= 0.9.2), perry (>= 0.2.0), robustbase (>= 0.9-5) Imports: MASS, parallel, stats LinkingTo: Rcpp (>= 0.9.10), RcppArmadillo (>= 0.3.0) Suggests: mvtnorm License: GPL (>= 2) LazyLoad: yes }

Index: AIC.seqModel Information criteria for a sequence of regression models TopGear Top Gear car data coef.seqModel Extract coefficients from a sequence of regression models coefPlot Coefficient plot of 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 fortify.seqModel Convert a sequence of regression models into a data frame for plotting getScale Extract the residual scale of a robust regression model grplars (Robust) groupwise least angle regression lambda0 Penalty parameter for sparse LTS regression perry.seqModel Resampling-based prediction error for a sequential regression model 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-deprecated Deprecated functions in package 'robustHD' robustHD-package Robust methods for high-dimensional data sparseLTS Sparse least trimmed squares regression standardize Data standardization 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 fixed lag length winsorize Data cleaning by winsorization wt Extract outlier weights from sparse LTS regression models