Author: Olha Bodnar [aut] (<https://orcid.org/0000-0003-1359-3311>),

Title: Testing and Detecting Multimodality using Bayesian Methods
Authors@R: 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"))
Description: 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>).
Imports: ggpubr, dplyr, tidyr, ggplot2, stringr, ggh4x, magrittr,
Packaged: 2022-10-10 22:53:20 UTC; paullabonne

Title: BayesNI: Bayesian Testing Procedure for Noninferiority with
Description: 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

Title: Bayesian Analysis of Non-Stationary Gaussian Process Models
Description: Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <arXiv:1702.00434v2>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the 'nimble' package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.