Robust Methods for High-Dimensional Data
Information criteria for a sequence of regression models
Extract coefficients from a sequence of regression models
Coefficient plot of a sequence of regression models
Robust correlation based on winsorization
Optimality criterion plot of a sequence of regression models
Diagnostic plots for a sequence of regression models
Extract fitted values from a sequence of regression models
Extract the residual scale of a robust regression model
(Robust) groupwise least angle regression
Penalty parameter for sparse LTS regression
Find partial order of smallest or largest values
Resampling-based prediction error for a sequential regression model
Plot a sequence of regression models
Predict from a sequence of regression models
Extract residuals from a sequence of regression models
Robust least angle regression
tools:::Rd_package_title("robustHD")
Extract standardized residuals from a sequence of regression models
Set up a coefficient plot of a sequence of regression models
Set up an optimality criterion plot of a sequence of regression models
Set up a diagnostic plot for a sequence of regression models
Sparse least trimmed squares regression
Data standardization
Construct predictor blocks for time series models
(Robust) least angle regression for time series data
(Robust) least angle regression for time series data with fixed lag le...
Extract outlier weights from sparse LTS regression models
Data cleaning by winsorization
Robust methods for high-dimensional data, in particular linear model selection techniques based on least angle regression and sparse regression. Specifically, the package implements robust least angle regression (Khan, Van Aelst & Zamar, 2007; <doi:10.1198/016214507000000950>), (robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016; <doi:10.1016/j.csda.2015.02.007>), and sparse least trimmed squares regression (Alfons, Croux & Gelper, 2013; <doi:10.1214/12-AOAS575>).