c(person(given = "Olha",
family = "Bodnar",
role = "aut",
comment = c(ORCID = "0000-0003-1359-3311")),
person(given = "Taras",
family = "Bodnar",
role = "aut",
comment = c(ORCID = "0000-0001-7855-8221")),
person(given = "Erik",
family = "Thorsén",
role = c("aut", "cre"),
email = "erik.thorsen@math.su.se",
comment = c(ORCID = "0000-0001-5992-1216")))

Testing and Detecting Multimodality using Bayesian Methods
c(person(given = "Nalan", family = "Baştürk", role = "aut"),
person(given = "Jamie", family = "Cross", role = "aut"),
person(given = "Peter", family = "de Knijff", role = "aut"),
person(given = "Lennart", family = "Hoogerheide", role = "aut"),
person(given = "Paul", family = "Labonne", email= "paul.labonne@bi.no", role = c("aut", "cre")),
person(given = "Herman", family = "van Dijk", role = "aut"))
The testing approach works in two stages. First, a mixture distribution is
fitted on the data using a Sparse Finite Mixture (SFM) Markov chain Monte Carlo
(MCMC) algorithm following Malsiner-Walli, Frühwirth-Schnatter and Grün (2016)
<doi:10.1007/s11222-014-9500-2>). The number of mixture components does not have to
be specified; it is estimated simultaneously with the mixture weights and components
through the SFM approach. Second, the resulting MCMC output is used to calculate the
number of modes and their locations following Basturk, Hoogerheide and van Dijk (2021)
<doi:10.2139/ssrn.3783351>. Posterior probabilities are retrieved for both of these
quantities providing a powerful tool for mode inference. Currently the package
supports a flexible mixture of shifted Poisson distributions (see
Basturk, Hoogerheide and van Dijk (2021) <doi:10.2139/ssrn.3783351>).
ggpubr, dplyr, tidyr, ggplot2, stringr, ggh4x, magrittr,
gtools, Rdpack
Nalan Baştürk [aut],
Jamie Cross [aut],
Peter de Knijff [aut],
Lennart Hoogerheide [aut],
Paul Labonne [aut, cre],
Herman van Dijk [aut]

BayesNI: Bayesian Testing Procedure for Noninferiority with
Binary Endpoints
A Bayesian testing procedure for noninferiority trials
with binary endpoints. The prior is constructed based on
Bernstein polynomials with options for both informative and
non-informative prior. The critical value of the test statistic
(Bayes factor) is determined by minimizing total weighted error
(TWE) criteria