c(person("Hugo", "Pedder", email = "hugopedder@gmail.com", role = c("aut", "cre")),
person("David", "Mawdsley", role = c("rev")),
person("Nicky", "Welton", role = c("rev")),
person("Sofia", "Dias", role = c("rev")),
person("Meg", "Bennetts", role = c("rev")),
person("Martin", "Boucher", role = c("rev")),
person("Lujin", "Li", role = c("rev")),
person("Adil", "Karim", role = c("ctb")))
Hugo Pedder [aut, cre],
David Mawdsley [rev],
Nicky Welton [rev],
Sofia Dias [rev],
Meg Bennetts [rev],
Martin Boucher [rev],
Lujin Li [rev],
Adil Karim [ctb]

c(person("Hugo", "Pedder", email = "hugopedder@gmail.com", role = c("aut", "cre")),
person("Lujin", "Li", role = c("rev")),
person("Adil", "Karim", role = c("ctb")))
grDevices, stats, graphics, utils, scales, utf8, formatR,
dplyr (>= 0.7.4), R2jags (>= 0.5-7), rjags (>= 4-8), magrittr
(>= 1.5), checkmate (>= 1.8.5), Rdpack (>= 0.11-0), igraph (>=
1.1.2), rgeos (>= 0.5-2), reshape2 (>= 1.4.3)
Hugo Pedder [aut, cre],
Lujin Li [rev],
Adil Karim [ctb]

c(person("Hugo", "Pedder",
email = "hugopedder@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-7813-3749")),
person("Daniel", "Gallardo Gomez",
role = c("rev"),
comment = c(ORCID = "0000-0002-3029-026X")),
person("Lujin", "Li", role = c("rev")),
person("Adil", "Karim", role = c("ctb")))
Hugo Pedder [aut, cre] (<https://orcid.org/0000-0002-7813-3749>),
Daniel Gallardo Gomez [rev] (<https://orcid.org/0000-0002-3029-026X>),
Lujin Li [rev],
Adil Karim [ctb]

c(person("Hugo", "Pedder", email = "hugopedder@gmail.com", role = c("aut", "cre")),
person("Nicky", "Welton", role = c("ctb", "rev")),
person("Sofia", "Dias", role = c("ctb", "rev")),
person("Meg", "Bennetts", role = c("ctb", "rev")),
person("Martin", "Boucher", role = c("ctb", "rev")))
Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) and model-based meta
-analysis (MBMA) that account for repeated measures over time within studies by applying different time-course functions,
following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>.
The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple
treatment comparisons. Several general time-course functions are provided; others may be added
by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared
class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects
models and/or by node-splitting.
grDevices, stats, graphics, utils, dplyr (>= 0.7.4), R2jags
(>= 0.5-7), rjags (>= 4-8), reshape2 (>= 1.4.3), magrittr (>=
1.5), checkmate (>= 1.8.5), Rdpack (>= 0.10-1)
knitr, scales (>= 1.0.0), overlapping (>= 1.5.0), ggplot2 (>=
2.2.1), igraph (>= 1.1.2), rmarkdown, testthat (>= 1.0.2),
RColorBrewer (>= 1.1-2), mcmcplots (>= 0.4.3)
Hugo Pedder [aut, cre],
Nicky Welton [ctb, rev],
Sofia Dias [ctb, rev],
Meg Bennetts [ctb, rev],
Martin Boucher [ctb, rev]