## 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.