provides five methods to perform a multiscale analysis about f as well as the necessary critical

Package: modehunt
Type: Package
Title: Multiscale Analysis for Density Functions
Version: 1.0
Date: 2007-05-24
Author: Kaspar Rufibach <kaspar.rufibach@stanford.edu> and Guenther Walther <gwalther@stanford.edu>
Maintainer: Kaspar Rufibach <kaspar.rufibach@stanford.edu>
Description: Given independent and identically distributed observations X(1), ..., X(n) from a density f,
this package provides five methods to perform a multiscale analysis about f as well as the necessary critical
values. The first method, introduced in Duembgen and Walther (2006), provides simultaneous confidence statements
for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by
any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of
I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive
correction term Gamma in either method offers another two approaches which are more powerful on small scales and
less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a
compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically
equivalent to the first and was introduced in Rufibach and Walther (2007).
License: GPL version 2 or newer
URL: www.stanford.edu/~kasparr, www-stat.stanford.edu/~gwalther
Packaged: Thu May 24 18:44:37 2007; rufibach

Package: model4you
Title: Stratified and Personalised Models Based on Model-Based Trees
and Forests
Date: 2018-01-15
Version: 0.9-0
Authors@R: 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"))
Description: 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>.
Depends: R (>= 3.1.0), partykit (>= 1.2-0), grid
Imports: sandwich, stats, ggplot2, Formula, gridExtra, survival
Suggests: mvtnorm, TH.data, psychotools, strucchange, plyr, knitr,
ggbeeswarm, MASS
License: GPL-2 | GPL-3
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-01-16 10:27:31 UTC; heidi
Author: Heidi Seibold [aut, cre],
Achim Zeileis [aut],
Torsten Hothorn [aut]
Maintainer: Heidi Seibold <heidi@seibold.co>
Repository: CRAN
Date/Publication: 2018-01-16 15:24:17 UTC