• 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.