1.2-3 - added support for package 'parallel' (removing support for 'multicore' and older R versions) - removed '\synopis' from documentation, resulting in some 'hidden' arguments to be visible now 1.2-2 - fixed bug that prevented subsetting (and cv.GAMBoost) from working - fixed bug where wrong name would be printed for selected smooth components 1.2-1 - speed improvements for continuous response models - implemented "criterion='score'" for all types of response - fixed bug where 'estimPVal' would with only one boosting step - 'estimPVal' now also works for zero boosting steps - added auomatic conversion of 'x' and 'x.linear' to class 'matrix' - creating a copy 'x.linear' is now avoided, if possible - improved output of 'print' and 'summary' methods - 'trace=TRUE' now shows the covariate names 1.2 - added function 'estimPVal' for permutation-based p-value estimation 1.1-1 - removed storage of covariate values in return object - implemented use of score function as selection criterion for linear components - added support for 'multicore' package for cross-validation - reduced memory consumption, speed-up for large number of linear components - added an option for fitting on subsets of observations - The coefficient matrix 'beta.linear' is for the linear components is now stored as a sparse matrix, employing package 'Matrix' 1.1 - implemented penalty modification factors and penalty change distribution via a connection matrix 1.0 - added wrappers (GLMBoost and predict.GLMBoost) for conveniently fitting generalized linear models, i.e., without smooth components - fixed "zero boosting steps" corner case: - GAMBoost/GLMBoost can now fit with "stepno=0", the trace, deviance, AIC, and BIC result vectors have an additional element for boosting step zero, and the elements of 'beta'/'beta.linear' for the latter are no longer equal to zero, but contain the results from one estimation step for the mandatory covariates, i.e., in the simplest case, an intercept-only model is fitted at zero boosting steps. - cv.GAMBoost/cv.GLMBoost can return an optimum at zero steps, and alos the 'criterion' and 'se' elements of the results have an additonal element for boosting step zero - implemented parallel evaluation on a compute cluster for cross-validation 0.9-4 - general performance improvements, especially for componentwise ridge boosting, i.e. boosting for covariates with linear influence, and there espacially for binary response models - fixed bug in formula for traditional AIC in the Gaussian response case 0.9-3 - fixed use of weights in cv.GAMBoost - added flexible p value cutoff for prediction and calculation of prediction error in cv.GAMBoost 0.9-2 - fixed a problem where predict.GAMBoost would not work with only linear predictors (thanks to Ravi Varadhan for pointing this out) - implemented penalty of difference '0' as absolute penalty on coefficients - 'pdiff, specifying the penalty difference can be a vector now, thus allowing for enforcement of several types of smoothness simultanoeusly' - fixed bug that prevented criterion from being save for AIC-optimGAMBoostPenalty - optimGAMBoostPenalty now also stores the selection criterion in the GAMBoost object returned 0.9-1 * initial public release