Learn R Programming
⚠️There's a newer version (0.8.4) of this package.Take me there.

robustHD (version 0.3.2)

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.

Copy Link

Version

Install

install.packages('robustHD')

Monthly Downloads

5,077

Version

0.3.2

License

GPL (>= 2)

Maintainer

Andreas Alfons

Last Published

May 2nd, 2013

Functions in robustHD (0.3.2)

robustHD-package

Robust methods for high-dimensional data
robustHD-deprecated

Deprecated functions in package robustHD
fortify.seqModel

Convert a sequence of regression models into a data frame for plotting
standardize

Data standardization
sparseLTS

Sparse least trimmed squares regression
rlars

Robust least angle regression
lambda0

Penalty parameter for sparse LTS regression
AIC.seqModel

Information criteria for a sequence of regression models
wt

Extract outlier weights from sparse LTS regression models
coef.seqModel

Extract coefficients from a sequence of regression models
coefPlot

Coefficient plot of a sequence of regression models
plot.seqModel

Plot a sequence of regression models
critPlot

Optimality criterion plot of a sequence of regression models
winsorize

Data cleaning by winsorization
diagnosticPlot

Diagnostic plots for a sequence of regression models
fitted.seqModel

Extract fitted values from a sequence of regression models
perry.seqModel

Resampling-based prediction error for a sequential regression model
residuals.seqModel

Extract residuals from a sequence of regression models
getScale

Extract the residual scale of a robust regression model
predict.seqModel

Predict from a sequence of regression models
corHuber

Robust correlation based on winsorization.