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robustHD (version 0.5.0)

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.

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Version

Install

install.packages('robustHD')

Monthly Downloads

5,077

Version

0.5.0

License

GPL (>= 2)

Maintainer

Andreas Alfons

Last Published

April 30th, 2014

Functions in robustHD (0.5.0)

fortify.seqModel

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

Data standardization
getScale

Extract the residual scale of a robust regression model
lambda0

Penalty parameter for sparse LTS regression
perry.seqModel

Resampling-based prediction error for a sequential regression model
TopGear

Top Gear car data
tsBlocks

Construct predictor blocks for time series models
robustHD-deprecated

Deprecated functions in package robustHD
coef.seqModel

Extract coefficients from a sequence of regression models
wt

Extract outlier weights from sparse LTS regression models
coefPlot

Coefficient plot of a sequence of regression models
tslarsP

(Robust) least angle regression for time series data with fixed lag length
corHuber

Robust correlation based on winsorization.
robustHD-package

Robust methods for high-dimensional data
plot.seqModel

Plot a sequence of regression models
predict.seqModel

Predict from a sequence of regression models
rlars

Robust least angle regression
tslars

(Robust) least angle regression for time series data
critPlot

Optimality criterion plot of a sequence of regression models
diagnosticPlot

Diagnostic plots for a sequence of regression models
residuals.seqModel

Extract residuals from a sequence of regression models
sparseLTS

Sparse least trimmed squares regression
fitted.seqModel

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

Information criteria for a sequence of regression models
grplars

(Robust) groupwise least angle regression
winsorize

Data cleaning by winsorization