coda, mockery, testthat (>= 3.0.0), knitr, rmarkdown

bayesplot, coda, mockery, testthat (>= 3.0.0), knitr,
rmarkdown

c( person("S. McKay", "Curtis", role=c("aut", "cre"),
email="s.mckay.curtis@gmail.com"), person("Ilya", "Goldin",
role="ctb") )
S. McKay Curtis with contributions from Ilya Goldin

c(
person("S. McKay", "Curtis", role=c("aut", "cre"), email="s.mckay.curtis@gmail.com"),
person("Ilya", "Goldin", role="ctb"),
person("Evangelos", "Evangelou", role="ctb"),
person("'sumtxt' from GitHub", role="ctb")
)
Functions for convenient plotting and viewing of MCMC output.
S. McKay Curtis [aut, cre],
Ilya Goldin [ctb],
Evangelos Evangelou [ctb],
'sumtxt' from GitHub [ctb]

person("Joe", "Thorley", email = "joe@poissonconsulting.ca",
role = c("aut", "cre"))
Functions and classes to store, manipulate and summarise
Monte Carlo Markov Chain (MCMC) samples.
abind, checkr, coda, graphics, stats, tibble, utils

person("Joe", "Thorley", role = c("aut", "cre"),
email = "joe@poissonconsulting.ca",
comment = c(ORCID = "0000-0002-7683-4592"))

Functions and classes to store, manipulate and summarise
Monte Carlo Markov Chain (MCMC) samples. For more information see
Brooks et al. (2011) <isbn:978-1-4200-7941-8>.

Functions and classes to store, manipulate and
summarise Monte Carlo Markov Chain (MCMC) samples. For more
information see Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
abind, chk, coda, utils, stats, term, nlist, purrr,
universals, extras, lifecycle

abind, chk, coda, extras, generics, lifecycle, nlist (>=
0.3.1), purrr, stats, term, universals, utils

abind, chk, coda, extras, generics, lifecycle, nlist, purrr,
stats, term, universals, utils

abind, chk (>= 0.7.0), coda, extras, generics, lifecycle,
nlist, purrr, stats, term, universals, utils
covr, graphics, readr, rlang, testthat, tibble

c(
person("Harm Jan", "Boonstra", email="hjboonstra@gmail.com", role=c("aut", "cre")),
person("Grzegorz", "Baltissen", role="ctb"))
Markov Chain Monte Carlo Small Area Estimation
Fit multi-level models with possibly correlated
random effects using Markov Chain Monte Carlo simulation.
Such models allow smoothing over space and time and are useful in,
for example, small area estimation.
Matrix (>= 1.2.0), Rcpp (>= 0.11.0), methods, GIGrvg, loo (>=
2.0.0), matrixStats
BayesLogit, lintools, splines, spdep, maptools, bayesplot,
coda, parallel, testthat, roxygen2, knitr, rmarkdown, survey
'MCMCsim.R' 'MH.R' 'tabMatrix.R' 'MatrixUtils.R'
'RcppExports.R' 'TMVN_sampler.R' 'aux_closures.R' 'cholesky.R'
'family.R' 'formulas.R' 'kronprod.R' 'mc_block.R' 'mc_gen.R'
'mc_gl.R' 'mc_reg.R' 'mc_vfac.R' 'mc_vreg.R' 'mcmcsae.R'
'model_eval.R' 'modelmatrix.R' 'models.R' 'opts.R' 'parallel.R'
'plots.R' 'prediction.R' 'priors.R' 'random.R' 'samplers.R'
'sparse_template.R' 'utils.R'
Harm Jan Boonstra [aut, cre],
Grzegorz Baltissen [ctb]