Authors@R: 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("Adil", "Karim", role = c("ctb")))
Description: Fits Bayesian dose-response model-based network meta-analysis (MBNMA)
that incorporate multiple doses within an agent by modelling different dose-response functions, as
described by Mawdsley et al. (2016) <doi:10.1002/psp4.12091>.
By modelling dose-response relationships this can connect networks of evidence that might
otherwise be disconnected, and can improve precision on treatment estimates. Several common
dose-response functions are provided; others may be added by the user. Various characteristics
and assumptions can be flexibly added to the models, such as 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 at the treatment level.
Imports: grDevices, stats, graphics, utils, scales, dplyr (>= 0.7.4),
R2jags (>= 0.5-7), rjags (>= 4-8), magrittr (>= 1.5), checkmate
Suggests: overlapping (>= 1.5.0), ggplot2 (>= 2.2.1), igraph (>=
1.1.2), RColorBrewer (>= 1.1-2), mcmcplots (>= 0.4.3), testthat

SystemRequirements: JAGS (>= 4.3.0) (http://mcmc-jags.sourceforge.net)

Imports: grDevices, stats, graphics, utils, scales, utf8, formatR,
Suggests: overlapping (>= 1.5.0), ggplot2 (>= 2.2.1), RColorBrewer (>=
1.3.4), Hmisc (>= 4.4-1), knitr, rmarkdown

2.4.0), lspline (>= 1.0-0), knitr, rmarkdown
Author: Hugo Pedder [aut, cre] (<https://orcid.org/0000-0002-7813-3749>),
Daniel Gallardo Gomez [rev] (<https://orcid.org/0000-0002-3029-026X>),

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")))
Description: 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
Imports: grDevices, stats, graphics, utils, dplyr (>= 0.7.4), R2jags
Suggests: knitr, scales (>= 1.0.0), overlapping (>= 1.5.0), ggplot2 (>=
RColorBrewer (>= 1.1-2), mcmcplots (>= 0.4.3)