User-visible changes
- Models with zero steps (i.e., models containing only the
offset) can now be fitted. Furthermore, cross-validation can
now also select a model without base-learners. Fixes #64, #66,
and #69.
- Binomial now uses link functions by making use of make.link.
Furthermore, an alternative implementation of Binomial models
along the lines of the glm implementation can be used via
Binomial(type = "glm"). Additionally, it works not only with a
two-level factor but also with a two-column matrix containing
the number of successes and number of failures. Fixes #34, #63
and #65.
- Added new base-learner bkernel for kernel boosting as described
in
S. Friedrichs, J. Manitz, P. Burger, C.I. Amos, A. Risch, J.C.
Chang-Claude, H.E. Wichmann, T. Kneib, H. Bickeboeller, and B.
Hofner (2017), Pathway-Based Kernel Boosting for the Analysis
of Genome-Wide Association Studies. _Computational and
Mathematical Methods in Medicine_. 2017(6742763), 1-17.
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.1155/2017/6742763")}.
- Removed check if df2lambda is stable. Hence,
options(mboost_check_df2lambda) (introduced in mboost 2.5-0) is
no longer used. Closes #26.
Miscellaneous
- Added Andreas Mayr as contributor.
- Updated references and added reference to citation("mboost").
- Fixed code of India example, which can be used to reproduce the
data analysis presented in
N. Fenske, T. Kneib, and T. Hothorn (2011), Identifying risk
factors for severe childhood malnutrition by boosting additive
quantile regression. _Journal of the American Statistical
Association_, *106*:494-510.
(see system.file("India_quantiles.R", package = "mboost"))
- Fixed package citation.
- Register C routines to make CRAN happy (again). Fixes #77.
Bug-fixes
- Make sure that family = Multinomial is only used with Kronecker
product base-learners. Fixes #46.
- Use argument PACKAGE in .Call. Fixes #72.
- If center is specified as boolean value in bols, we now throw
an error. Fixes #70.
- Fixed AUC family which expected fit to be equal to a constant
in the first iteration.
- Check for new data, e.g., in predict, was broken. Fixes #68.
- Make sure that newdata is discarded in fitted. Fixes #76.