- 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
- 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
- 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).
- 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
- Truncate output of complete data structure when model is printed.
Resolves issue #11.
- Adhere to CRAN policies regarding import of base packages (closes