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