Priors: Sometimes investigators may be interested in using prior that they want to supply themselves based on their prior knowledge. So, mtc.model function can have addition parameter: priors =("empirical", "supplied") with default as "empirical" that gemtc currently employes. If "supplied" is chosen, add few parameter for prior parameters; for example, prior.mu_ib = c(0, 10000) and prior.d_xy=c(0, 10000) for mean and variance respectively under Normal(mean, variance) prior, and tau=2 for upper limit in the Uniform(0, tau) prior.
Thanks to Binod Neupane:
Priors: Sometimes investigators may be interested in using prior that they want to supply themselves based on their prior knowledge. So, mtc.model function can have addition parameter: priors =("empirical", "supplied") with default as "empirical" that gemtc currently employes. If "supplied" is chosen, add few parameter for prior parameters; for example, prior.mu_ib = c(0, 10000) and prior.d_xy=c(0, 10000) for mean and variance respectively under Normal(mean, variance) prior, and tau=2 for upper limit in the Uniform(0, tau) prior.