boot, coda, utils, testthat, numbers, lattice, rootSolve
Craig Wang [aut, cre] (0000-0003-1804-2463),
Michaela Paul [aut],
Tea Isler [ctb],
Reinhard Furrer [ctb] (0000-0002-6319-2332),
Trustees of Columbia University [cph]
An implementation of Bayesian hierarchical models
for faecal egg count data to assess anthelmintic
efficacy. Bayesian inference is done via MCMC sampling using Stan.
stan_fit4paired_mod, stan_fit4unpaired_mod,
stan_fit4zipaired_mod, stan_fit4ziunpaired_mod,
stan_fit4nb_mod, stan_fit4zinb_mod, stan_fit4indefficacy_mod,
stan_fit4simple_mod

c(
person("Craig", "Wang", email = "craig.wang@uzh.ch", role = c("aut","cre"),comment = c(ORCID = "0000-0003-1804-2463")),
person("Michaela", "Paul", role = "aut"),
person("Tea", "Isler", role = "ctb"),
person("Reinhard", "Furrer", email = "reinhard.furrer@math.uzh.ch", role = "ctb",comment = c(ORCID = "0000-0002-6319-2332")),
person("Trustees of Columbia University", role = "cph"))
Craig Wang [aut, cre] (<https://orcid.org/0000-0003-1804-2463>),
Michaela Paul [aut],
Tea Isler [ctb],
Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>),
Trustees of Columbia University [cph]

c(
person("Craig", "Wang", email = "craig.wang@uzh.ch", role = c("aut","cre"),comment = c(ORCID = "0000-0003-1804-2463")),
person("Michaela", "Paul", role = "aut"),
person("Tea", "Isler", role = "ctb"),
person("Reinhard", "Furrer", email = "reinhard.furrer@math.uzh.ch", role = "ctb",comment = c(ORCID = "0000-0002-6319-2332")),
person("Trustees of Columbia University", role = "cph", comment="src/init.cpp, tools/make_cc.R, R/stanmodels.R"))
Craig Wang [aut, cre] (<https://orcid.org/0000-0003-1804-2463>),
Michaela Paul [aut],
Tea Isler [ctb],
Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>),
Trustees of Columbia University [cph] (src/init.cpp, tools/make_cc.R,
R/stanmodels.R)

methods, utils, rstan (>= 2.18.1), boot, coda, numbers,
lattice, rootSolve
An implementation of Bayesian hierarchical models
for faecal egg count data to assess anthelmintic
efficacy. Bayesian inference is done via MCMC sampling using 'Stan' <https://mc-stan.org/>.