Kaspar Rufibach <kaspar.rufibach@stanford.edu> and Guenther Walther <gwalther@stanford.edu>

Stratified and Personalised Models Based on Model-Based Trees
and Forests
c(person(given = "Heidi", family = "Seibold",
role = c("aut", "cre"), email = "heidi@seibold.co"),
person(given = "Achim", family = "Zeileis",
role = "aut", email = "Achim.Zeileis@R-project.org"),
person(given = "Torsten", family = "Hothorn",
role = "aut", email = "Torsten.Hothorn@R-project.org"))
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>.
sandwich, stats, ggplot2, Formula, gridExtra, survival
mvtnorm, TH.data, psychotools, strucchange, plyr, knitr,
ggbeeswarm, MASS
Heidi Seibold [aut, cre],
Achim Zeileis [aut],
Torsten Hothorn [aut]

sandwich, stats, methods, ggplot2, Formula, gridExtra,
survival

Make Static HTML Website for Predictive Models
c(person("Przemysław", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut")),
person("Magda", "Tatarynowicz", email = "magda.tatarynowicz@gmail.com", role = c("aut")),
person("Kamil", "Romaszko", email = "kamil.romaszko@gmail.com", role = c("aut", "cre")),
person("Mateusz", "Urbański", email = "matiszak@gmail.com", role = c("aut")))
Website generator with HTML summaries for predictive models.
This package uses 'DALEX' explainers to describe global model behavior.
We can see how well models behave (tabs: Model Performance, Auditor),
how much each variable contributes to predictions (tabs: Variable Response)
and which variables are the most important for a given model (tabs: Variable Importance).
We can also compare Concept Drift for pairs of models (tabs: Drifter).
Additionally, data available on the website can be easily recreated in current R session.
Work on this package was financially supported by the NCN Opus grant 2017/27/B/ST6/01307
at Warsaw University of Technology, Faculty of Mathematics and Information Science.
Przemysław Biecek [aut],
Magda Tatarynowicz [aut],
Kamil Romaszko [aut, cre],
Mateusz Urbański [aut]