User-visible changes

  • Added documentation for plot.mboost function and moved documentation of plot.glmboost to the same help page. Resolves issue #14.
  • bbs and bmono no longer allow data outside of the boundary.knots during model fitting.
  • Predictions for bbs and bmono now use linear extrapolation (user request inspired by mgcv::Predict.matrix.pspline.smooth).
  • Better handling of errors in (single) folds of cvrisk: results of folds without errors are used and a warning is issued.
  • Parallel computing via mclapply: Set mc.preschedule = FALSE per default.
  • Added new option options(mboost_check_df2lambda = TRUE), which controls if a stability check in df2lambda is performed. If set to FALSE this might speed up the computation of df2lambda especially with large design matrices.
  • Prediction now also possible with newdata = list(). Resolves issue #15.

Miscellaneous

  • PropOdds(): Updated manual for proportional odds model.
  • Multinomial(): Updated manual for multinomial logit model. Predictions for new data are now possible (resolves issue #13, thanks to Sarah Brockhaus).
  • inst/CITATION: Added subheadings and tutorial paper.
  • Stopped computing the singular vectors in df2lambda as the singular values are sufficient and as “computing the singular vectors is the slow part for large matrices” (proposed by Fabian Scheipl).

Bug-fixes

  • Fixed bug in PropOdds() which occurred if offset was specified: nuisance parameters delta and sigma were not properly initialized (spotted by Madlene Nussbaum).
  • Bug in plot.mboost() fixed which occurred if a factor with equal effect estimates for different categories was plotted.
  • Bug in df2lambda fixed: Make sure that A is symmetric if it is Matrix-object (spotted by Souhaib Ben Taieb).
  • Bug in df2lambda fixed. Design matrices were always assumed to be of full rank.
  • Truncate output of complete data structure when model is printed. Resolves issue #11.
  • Adhere to CRAN policies regarding import of base packages (closes #9).