Base-learners

  • added base-learners for smooth monotonic (or convex/concave) functions of one or two variables (bmono())
  • added base-learners for radial basis functions (brad())
  • added base-learners for Markov random fields (bmrf())
  • bbs(x, cyclic = TRUE) for cyclic covariates ensures that predictions at the boundaries coincide and that the resulting function estimate is smoothly joined
  • bols(x, intercept = FALSE) only reasonable if x is centered. A warning is now issued if x is not centered.
  • changed default for degrees of freedom in bspatial() to df = 6
  • added checks in bbs (and brandom) to ensure that the specified degrees of freedom are greater than the range of the (unpenalized) null space
  • bolscw can be mixed with other base-learners (although not yet exported and not via the formula interface)
  • new experimental base-learner %O% for smoothing matrix-values responses

Families

  • add Binomial(link = "probit") and general cdf's as link functions (experimental)
  • added new families: • AUC() for AUC loss function • GammaReg() for gamma regression models

Methods

  • added extract() methods for base-learners and fitted models
  • added residuals() function to extract residuals from the model
  • improved predict.mboost(): added names where missing and the offset as attribute where applicable.
  • fixed bug in predict() with glmboost.matrix(..., center = TRUE)
  • coef now also works with tree base-learners (returns NULL in this case)
  • changed coef.gamboost to coef.mboost
  • various improvements in plot.mboost function

Miscellaneous

  • changed default in glmboost() to center = TRUE
  • speed up glmboost() a little bit
  • changed behavior of cvrisk() if weights are used: out-of-bag-risk now weighted according to "weights" as specified in call to mboost
  • added warning if df2lambda is likely to become numerically unstable (i.e. in the case of large entries in the design matrix)
  • improved storage, speed and stability using Matrix technology for bols() for factors with many levels and brandom(); further improvements in base-learners that are combined via %+%.
  • various improvements and fixes in manuals