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