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.Details
ll{
Package: robustHD
Type: Package
Version: 0.3.0
Date: 2013-03-13
Depends: R (>= 2.14.1), Rcpp (>= 0.9.10), ggplot2 (>= 0.9.2), MASS,
parallel, perry (>= 0.2.0), robustbase (>= 0.6-0)
Imports: RcppArmadillo (>= 0.3.0), pcaPP, stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: mvtnorm
License: GPL (>= 2)
LazyLoad: yes
}
Index:
AIC.seqModel Information criteria for a sequence of
regression models
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
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
winsorize Data cleaning by winsorization
wt Extract outlier weights from sparse LTS
regression models