c(person(given = "Reza",
family = "Mohammadi",
role = c("aut", "cre"),
email = "a.mohammadi@uva.nl",
comment = c(ORCID = "0000-0001-9538-0648"))
)
Reza Mohammadi [aut, cre] (<https://orcid.org/0000-0001-9538-0648>)

Provides statistical tools for Bayesian estimation of mixture distributions, mainly a mixture of Gamma, Normal, and t-distributions. The package is implemented based on the Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <doi:10.1080/03610918.2011.588358>.

MCMC diagnostic package that contains tools to diagnose
convergence as well as to evaluate sensitivity studies,
Includes summary functions which output mean, median,
95percentCI, Gelman & Rubin diagnostics and the Hellinger
distance based diagnostics, Also contains functions to
determine when an MCMC chain has converged via Hellinger
distance, A function is also provided to compare outputs from
identically dimensioned chains for determining sensitivy to
prior distribution assumptions
'bmkconverge.R' 'bmk.R' 'bmksensitive.R' 'bmksummary.R'
'HBconverg1.R' 'HDistNoSize.R' 'HDistSize.R' 'HWconverg1.R'

person("Matti", "Vuorre", email = "mv2521@columbia.edu",
role = c("aut", "cre"))
Easy estimation of Bayesian multilevel mediation models with Stan.
qgraph, knitr, rmarkdown, reshape2, dplyr, ReporteRs

person(given = "Matti",
family = "Vuorre",
email = "mv2521@columbia.edu",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-5052-066X"))
qgraph, knitr, rmarkdown, reshape2, dplyr
Matti Vuorre [aut, cre] (<https://orcid.org/0000-0001-5052-066X>)

person(given = "Matti",
family = "Vuorre",
email = "matti.vuorre@oii.ox.ac.uk",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-5052-066X"))

c(
person("Benno", "Simmons", email = "benno.simmons@gmail.com", role = c("aut", "cre")),
person("Michelle", "Sweering", email = "michelle.sweering@gmail.com", role = c("aut")),
person("Riccardo", "Di Clemente", email = "rdicle@mit.edu", role = c("aut")))
Counts occurrences of motifs in bipartite networks, as well as the number of
times each node appears in each unique position within motifs. Intended for
use in ecology, but its methods are general and can be applied to any
bipartite network.
Benno Simmons [aut, cre],
Michelle Sweering [aut],
Riccardo Di Clemente [aut]