c(person("Hugo", "Pedder", email = "hugopedder@gmail.com", role = c("aut", "cre")),
person("Tobias", "Saueressig", role = c("rev")),
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) that allows inclusion of multiple
time-points from studies. Repeated measures over time are accounted for 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.
knitr, 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)
Hugo Pedder [aut, cre],
Tobias Saueressig [rev],
Nicky Welton [ctb, rev],
Sofia Dias [ctb, rev],
Meg Bennetts [ctb, rev],
Martin Boucher [ctb, rev]

Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models

Multivariate Bayesian Sparse Group Selection with Spike and Slab
An implementation of a Bayesian sparse group model using spike and slab priors in a regression context. It is designed for regression with a multivariate response variable, but also provides an implementation for univariate response.

Multivariate Bayesian Model with Shrinkage Priors
Implements a sparse Bayesian multivariate linear regression model using shrinkage priors from the three parameter beta normal family. The method is described in Bai and Ghosh (2018) <arXiv:1711.07635>.
R (>= 3.1.0), Matrix, MASS, GIGrvg, coda, MCMCpack