R2jags, R2OpenBUGS, ggplot2, grid, INLA, splancs, mgcv

R2jags, R2OpenBUGS, ggplot2, grid, INLA, splancs, mgcv, ldr

R2jags, R2OpenBUGS, ggplot2, grid, INLA, splancs, mgcv, ldr,
shiny, shinythemes

R2jags, R2OpenBUGS, ggplot2, grid, INLA, splancs, mgcv, ldr,
shiny, shinythemes, knitr, rmarkdown, plotly

c(
person("Gianluca", "Baio",
email = "gianluca@stats.ucl.ac.uk",
role = c("aut", "cre")),
person("Andrea", "Berardi",
email = "a.berardi@ucl.ac.uk",
role = c("aut")),
person("Anna", "Heath",
email = "anna.heath@sickkids.ca",
role = c("aut")),
person("Nathan", "Green",
email = "n.green@ucl.ac.uk",
role = c("aut")))
dplyr, ggplot2, GrassmannOptim, gridExtra, graphics, MASS,
purrr, reshape2, rlang
coda, grid, INLA, knitr, markdown, Matrix, MCMCvis, mgcv,
plotly, rmarkdown, rstan, RColorBrewer, R2jags, splancs,
testthat (>= 2.1.0), vdiffr, withr
Produces an economic evaluation of a sample of suitable variables of
cost and effectiveness / utility for two or more interventions,
e.g. from a Bayesian model in the form of MCMC simulations.
This package computes the most cost-effective alternative and
produces graphical summaries and probabilistic sensitivity analysis.
Gianluca Baio [aut, cre],
Andrea Berardi [aut],
Anna Heath [aut],
Nathan Green [aut]

c(
person("Gianluca", "Baio",
email = "gianluca@stats.ucl.ac.uk",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-4314-2570")),
person("Andrea", "Berardi",
email = "a.berardi@ucl.ac.uk",
role = c("aut"),
comment = c(ORCID = "0000-0002-2906-496X")),
person("Anna", "Heath",
email = "anna.heath@sickkids.ca",
role = c("aut"),
comment = c(ORCID = "0000-0002-7263-4251")),
person("Nathan", "Green",
email = "n.green@ucl.ac.uk",
role = c("aut"),
comment = c(ORCID = "0000-0003-2745-1736")))
cli (>= 3.3.0), dplyr, ggplot2, gridExtra, graphics, MASS,
Matrix, purrr, Rdpack, reshape2, rlang, scales
coda, grid, INLA, knitr, markdown, MCMCvis, mgcv, plotly,
rjags, rmarkdown, rstan, RColorBrewer, splancs, testthat (>=
2.1.0), vdiffr, withr
Produces an economic evaluation of a sample of suitable variables of
cost and effectiveness / utility for two or more interventions,
e.g. from a Bayesian model in the form of MCMC simulations.
This package computes the most cost-effective alternative and
produces graphical summaries and probabilistic sensitivity analysis,
see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>.