- Extended mob() interface by a cluster argument. This can be a vector
(numeric, integer, factor) with cluster IDs that are then passed on
to the 'fit' function (if supported) and used for clustering the
covariance matrix in the parameter stability tests. lmtree() and
glmtree() hence both gained a cluster argument which is used only
for cluster covariances but not for the model estimation (i.e.,
corresponding to a working independence model).
- Optionally, the parameters' variance-covariance matrix in mob() can
now be estimated by the sandwich matrix instead of the default
outer-product-of-gradients (OPG) matrix or the information matrix.
- Reimplementation of cforest() available with extended prediction
facilities. Both the internal representation and the user interface
are still under development are likely to change in future
versions.
- Added multicore support to mob(), ctree(), and cforest(). If
control argument cores is specified (e.g., cores = 4) then the
search for the best variable or split point (often involving
numerous model fits in mob() or resampling in ctree()) is carried
out using parallel::mclapply() rathern than sequential for() or
sapply(). Additionally, other applyfuns can be provided, e.g.,
using networks of workstations etc.
- Bug fix in mob() that occurred when regressor variables and
partitioning variables overlapped and were not sorted in the
underlying model frame.