Heidi Seibold

Model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects (personalised models). Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), is supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models. For details on model-based trees for subgroup analyses see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for details on model-based forests for estimation of individual treatment effects see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.

This is an implementation of model-based trees with global model parameters (PALM trees). The PALM tree algorithm is an extension to the MOB algorithm (implemented in the 'partykit' package), where some parameters are fixed across all groups. Details about the method can be found in Seibold, Hothorn, Zeileis (2016) <arXiv:1612.07498>. The package offers coef(), logLik(), plot(), and predict() functions for PALM trees.

Functions for determining and evaluating high-risk zones and simulating and thinning point process data, as described in 'Determining high risk zones using point process methodology - Realization by building an R package' Seibold (2012) <http://highriskzone.r-forge.r-project.org/Bachelorarbeit.pdf> and 'Determining high-risk zones for unexploded World War II bombs by using point process methodology', Mahling et al. (2013) <doi:10.1111/j.1467-9876.2012.01055.x>.

An interface for interacting with 'OSF' (<https://osf.io>). 'osfr' enables you to access open research materials and data, or create and manage your own private or public projects.

A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.

Implementation of the SIMEX-Algorithm by Cook & Stefanski (1994) <doi:10.1080/01621459.1994.10476871> and MCSIMEX by K<c3><bc>chenhoff, Mwalili & Lesaffre (2006) <doi:10.1111/j.1541-0420.2005.00396.x>.