c(person(given = "Torsten", family = "Hothorn", role = c("aut", "cre"), email = "Torsten.Hothorn@R-project.org",
comment = c(ORCID = "0000-0001-8301-0471")),
person(given = "Heidi", family = "Seibold", role = "ctb", email = "Heidi.Seibold@uzh.ch",
comment = c(ORCID = "0000-0002-8960-9642")),
person(given = "Achim", family = "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org",
comment = c(ORCID = "0000-0003-0918-3766")))
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) <http://jmlr.org/papers/v16/hothorn15a.html>.
grDevices, stats, utils, survival, Formula (>= 1.2-1), inum
(>= 1.0-0)
XML, pmml, rJava, sandwich, strucchange, vcd, AER, mlbench,
TH.data (>= 1.0-3), coin (>= 1.1-0), RWeka (>= 0.4-19),
datasets, parallel, psychotools, psychotree, party

c(person(given = "Torsten", family = "Hothorn", role = c("aut", "cre"), email = "Torsten.Hothorn@R-project.org",
comment = c(ORCID = "0000-0001-8301-0471")),
person(given = "Heidi", family = "Seibold", role = "ctb", email = "heidi@seibold.co",
comment = c(ORCID = "0000-0002-8960-9642")),
person(given = "Achim", family = "Zeileis", role = "aut", email = "Achim.Zeileis@R-project.org",
comment = c(ORCID = "0000-0003-0918-3766")))
XML, pmml, rJava, sandwich, strucchange, vcd, AER, mlbench,
TH.data (>= 1.0-3), coin (>= 1.1-0), RWeka (>= 0.4-19),
datasets, parallel, psychotools (>= 0.3-0), psychotree, party
(>= 1.3-0)

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>.
R (>= 3.5.0), graphics, grid, libcoin (>= 1.0-0), mvtnorm
grDevices, stats, utils, survival, Formula (>= 1.2-1), inum
(>= 1.0-0), rpart (>= 4.1-11)