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 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.


  • 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.


  • 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.