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robustHD (version 0.6.1)

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

2,037

Version

0.6.1

License

GPL (>= 2)

Maintainer

Andreas Alfons

Last Published

December 14th, 2019

Functions in robustHD (0.6.1)

fortify.seqModel

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

Top Gear car data
AIC.seqModel

Information criteria for a sequence of regression models
diagnosticPlot

Diagnostic plots for a sequence of regression models
critPlot

Optimality criterion plot of a sequence of regression models
corHuber

Robust correlation based on winsorization.
getScale

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

Extract fitted values from a sequence of regression models
grplars

(Robust) groupwise least angle regression
robustHD-deprecated

Deprecated functions in package robustHD
tslarsP

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

(Robust) least angle regression for time series data
plot.seqModel

Plot a sequence of regression models
standardize

Data standardization
rlars

Robust least angle regression
perry.seqModel

Resampling-based prediction error for a sequential regression model
lambda0

Penalty parameter for sparse LTS regression
tsBlocks

Construct predictor blocks for time series models
robustHD-package

robustHD
sparseLTS

Sparse least trimmed squares regression
weights.sparseLTS

Extract outlier weights from sparse LTS regression models
winsorize

Data cleaning by winsorization
coef.seqModel

Extract coefficients from a sequence of regression models
residuals.seqModel

Extract residuals from a sequence of regression models
predict.seqModel

Predict from a sequence of regression models
coefPlot

Coefficient plot of a sequence of regression models