c(person("Tobias", "Mütze", role=c("aut","cre"),
email="tobias.muetze@outlook.com",
comment = c(ORCID = "0000-0002-4111-1941")),
person("Tim", "Friede", role="ctb"))
Design and analyze three-arm non-inferiority or superiority trials
which follow a gold-standard design, i.e. trials with an experimental treatment,
an active, and a placebo control. Method for the following distributions are implemented:
Poisson (Mielke and Munk (2009) <arXiv:0912.4169>), negative binomial (Muetze et al. (2016) <doi:10.1002/sim.6738>),
normal (Pigeot et al. (2003) <doi:10.1002/sim.1450>; Hasler et al. (2009) <doi:10.1002/sim.3052>),
binary (Friede and Kieser (2007) <doi:10.1002/sim.2543>), nonparametric (Muetze et al. (2017) <doi:10.1002/sim.7176>),
exponential (Mielke and Munk (2009) <arXiv:0912.4169>).
Tobias Mütze [aut, cre] (<https://orcid.org/0000-0002-4111-1941>),
Tim Friede [ctb]

ML Estimator for Baseline-Placebo-Treatment (Three-group)
designs
This package implements the Maximum Likelihood estimator
for three-group designs proposed by Gerber, Green, Kaplan, and
Kern (2010).

ML Estimator for Baseline-Placebo-Treatment (Three-Group)
Experiments
Implements the Maximum Likelihood estimator for baseline, placebo, and treatment groups (three-group) experiments with non-compliance proposed by Gerber, Green, Kaplan, and Kern (2010).

Maria Antonietta Del Ferraro, Henk A.L. Kiers, Paolo Giordani
Component analysis for three-way data arrays by means of
Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models

Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models

Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.
R(>= 2.8.1), base, stats, graphics, grDevices

The Japanese Pictorial Maxim "See No Evil, Hear No Evil, Speak
No Evil"
Does nothing useful, but perhaps does that nothing in an entertaining or informative fashion.

Threshing and Reaping for Principal Components
Defines the classes used to identify
outliers (threshing) and compute the number of significant principal
components and number of clusters (reaping) in a joint application
of PCA and hierarchical clustering. See Wang et al., 2018,
<doi:10.1186/s12859-017-1998-9>.
methods, stats, graphics, grDevices, MASS, colorspace, movMF,
ade4, oompaBase