Changes in robustHD version 0.8.0

    + Removed deprecated functions.

    + Added rstandard() method for standardized residuals rather than
      residuals(standardize = TRUE).

    + Added DOI's to references in help files.

    + Minor change in behavior of diagnosticPlot() for the regression
      diagnostic plot of sparse LTS results when the number of selected
      variables is large compared to the number of observations. Added
      clear documentation why this plot may fail in such a situation.

    + Added metainformation on proteins, genes, and cancer cell lines to
      the NCI-60 cancer cell panel data.

    + Bugfix of inconsistent argument names of the internal generic function
      getComponent() one of its methods.



Changes in robustHD version 0.7.4

    + Bugfix in C++ code: deprecated argument of insert_rows() and
      insert_cols() no longer used.


Changes in robustHD version 0.7.3

    + Bugfix in sparseLTS(): function calls for fitting the model on training
      sets (when selecting lambda via prediction error estimation) are now
      evaluated in the correct environment.


Changes in robustHD version 0.7.2

    + Added CITATION file.

    + Added reference to JOSS paper in package help file.

    + Added URL and BugReports fields to DESCRIPTION file.


Changes in robustHD version 0.7.1

    + Unit tests are now run automatically with R CMD check.

    + Help file of sparseLTS() now correctly documents that arguments for
      selecting the optimal value of the penalty parameter are ignored if
      only one value of the penalty parameter is supplied.


Changes in robustHD version 0.7.0

    + Re-implemented plots and added functions setupCoefPlot(),
      setupCritPlot(), and setupDiagnosticPlot() to extract all
      the relevant information.

    + Removed deprecated functions.

    + Function partialOrder() for finding the order of the smallest or
      largest values of a vector is now exported.

    + Added NCI-60 cancer cell panel data.

    + Added coef(), fitted(), and residuals() methods for objects of class
      "perrySeqModel".

    + Updated broken links in documentation.



Changes in robustHD version 0.6.1

    + Internal C++ functions for testing are now properly registered.


Changes in robustHD version 0.6.0

    + Method for function weights() is now used to retrieve robustness weights
      from objects of class "sparseLTS" (with argument type = "robustness").

    + C++ functions are now properly registered.

    + The alternative back end for sparse least trimmed squares from package
      sparseLTSEigen is no longer supported and can no longer be used.



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.
