- 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.