Constrained and Unconstrained Zero-Inflated Generalized Additive Models with Model Selection Criterion
Constrained and Unconstrained Zero-Inflated Generalized Additive Models (ZIGAM) fitting with associated model plotting, prediction and selection.

Constrained and Unconstrained Zero-Inflated Generalized Additive
Models with Model Selection Criterion
Hai Liu <hailau@gmail.com>, Kung-Sik Chan
<kung-sik-chan@uiowa.edu>
Constrained and Unconstrained Zero-Inflated Generalized
Additive Models (ZIGAM) fitting with associated model plotting,
prediction and selection.

Hai Liu <liuhai@iupui.edu>, Kung-Sik Chan
<kung-sik-chan@uiowa.edu>

c(
person("Guillaume", "Cleuziou", email = "guillaume.cleuziou@univ-orleans.fr", role = "cre"),
person("Nicolas", "Hiot", email = "nicolas.hiot@etu.univ-orleans.fr", role = "aut"))
Provide functions for overlaps clustering, fuzzy clustering and interval-valued data manipulation. The package implement the following algorithms:
OKM (Overlapping Kmeans) from Cleuziou, G. (2007) <doi:10.1109/icpr.2008.4761079> ;
NEOKM (Non-exhaustive overlapping Kmeans) from Whang, J. J., Dhillon, I. S., and Gleich, D. F. (2015) <doi:10.1137/1.9781611974010.105> ;
Fuzzy Cmeans from Bezdek, J. C. (1981) <doi:10.1007/978-1-4757-0450-1> ;
Fuzzy I-Cmeans from de A.T. De Carvalho, F. (2005) <doi:10.1016/j.patrec.2006.08.014>.
Guillaume Cleuziou [cre],
Nicolas Hiot [aut]

Functions for calculating the conditional power
for different models in survival time analysis
within randomized clinical trials
with two different treatments to be compared
and survival as an endpoint.

person("Curtis", "Miller", email = "cmiller@math.utah.edu", role = c("aut", "cre"))
Implements several statistical tests for structural change, specifically the tests featured in Horváth, Rice and Miller (in press): CUSUM (with weighted/trimmed variants), Darling-Erdös, Hidalgo-Seo, Andrews, and the new Rényi-type test.

Bayesian Meta Analysis for Studying Cross-Phenotype Genetic
Associations
Arunabha Majumdar <statgen.arunabha@gmail.com> [aut, cre],
Tanushree Haldar <tanushree.haldar@gmail.com> [aut],
John Witte [ctb]
A Bayesian meta-analysis method for studying cross-phenotype
genetic associations. It uses summary-level data across multiple phenotypes to
simultaneously measure the evidence of aggregate-level pleiotropic association and
estimate an optimal subset of traits associated with the risk locus. CPBayes is based
on a spike and slab prior and is implemented by Markov chain Monte Carlo technique Gibbs sampling.