Robust Methods for High-Dimensional Data

Robust methods for high-dimensional data, in particular linear model selection techniques based on least angle regression and sparse regression.


News

Changes in robustHD version 0.5.1

+ Explicitly calling C++ function std::abs() rather than abs() to avoid
  clang warning.

+ Correctly importing functions head() and tail() from package 'utils' and
  function devAskNewPage from package 'grDevices'.

Changes in robustHD version 0.5.0

+ Added functionality for (robust) groupwise least angle regression.

+ Added TopGear car data.

+ Diagnostic plots now allow to pass arguments to covMcd().

+ Removed PCA step from data cleaning RLARS to consolidate code.

+ Updated package dependencies.

Changes in robustHD version 0.4.0

+ sparseLTS() no longer uses subsampling algorithm in the special case of
  alpha = 1.

+ sparseLTS() now has argument 'normalize' to specify whether the predictor
  variables should be normalized.

+ sparseLTS() now computes objective function with coefficients for
  normalized data (if applicable).

+ Most required packages are now imports rather than depends.

Changes in robustHD version 0.3.2

+ Bugfixes in sparseLTS() preventing errors for high-dimensional data.

Changes in robustHD version 0.3.1

+ rlars now uses perryFit() instead of perryTuning() for prediction error
  estimation.

+ Bugfix in rlars() allowing the number of variables to be sequenced to be
  larger than half the number of observations.

+ Bugfix in sparseLTS() in case of only one predictor variable.

+ Added tests for C++ implementation of the lasso.

Changes in robustHD version 0.3.0

+ Redesign of the class structure.

+ Redesign of how C++ back end is called.

+ Functionality of sparseLTSGrid() now included in sparseLTS();
  sparseLTSGrid() is now a deprecated wrapper function.

+ Restructured internal code for computing initial subsets for sparse LTS.

+ rlars() now supports data cleaning RLARS, with an extra PCA step for
  high-dimensional data.

+ New argument 's' in rlars() to select the steps along the sequence for
  which to compute submodels

+ fortify() and diagnosticPlot() methods for class "seqModel".

+ Bugfix in predict() method for "sparseLTS" if object was computed without
  intercept.

Changes in robustHD version 0.2.2

+ Bugfix in sparseLTS() for more stability of the results.

+ Bugfix in winsorize(): weights are now correctly returned as vector for
  a matrix with only one column.

+ Bugfix in diagnosticPlot(): previous setting of devAskNewPage() is now
  retained on exit.

Changes in robustHD version 0.2.1

+ Bugfix in rlars(): formula method now only adds function call and model
  terms if the default method returns an "rlars" object, not if only the
  sequence is returned.

+ Bugfix in rlars(): argument cl is now preferred over argument ncores for
  parallel computing, as stated in the help file.

+ Plots are no longer using the opts() function from package ggplot2, which
  is deprecated since ggplot2 version 0.9.2.

Changes in robustHD version 0.2.0

+ Graphics are now based on package ggplot2 instead of lattice.

+ Prediction error estimation is now based on package perry instead of
  cvTools.

+ Parallel computing for sparseLTS() now available via OpenMP.

+ rlars() is now using C++ code for variable sequencing, including
  parallelization of certain tasks via OpenMP.  Further parallel
  computing is implemented on the R level via package parallel.

+ sparseLTSGrid() and rlars() now allow model selection based on the
  prediction error.

+ coef(), fitted(), residuals() and wt() methods now have argument
  'drop' to control whether to reduce the dimension if possible.

+ Renamed components 'weight' and 'raw.weights' of sparse LTS models to
  'wt' and 'raw.wt', and renamed the accessor function accordingly to wt().

+ Print methods for "sparseLTS" and "sparseLTSGrid" now only show non-zero
  coefficients by default; also added argument to print method for "rlars".

+ sparseLTS() and sparseLTSGrid() now store the raw fitted values.

+ Bugfixes in C++ code for sparseLTS() and fastLasso() to prevent memory
  related errors.

Reference manual

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install.packages("robustHD")

0.5.1 by Andreas Alfons, 2 years ago


Browse source code at https://github.com/cran/robustHD


Authors: Andreas Alfons [aut, cre]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports MASS, grDevices, parallel, stats, utils

Depends on ggplot2, perry, robustbase

Suggests mvtnorm

Linking to Rcpp, RcppArmadillo


Imported by gamreg, rrcovHD.

Depended on by sparseLTSEigen.

Suggested by cellWise.


See at CRAN