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, and there are a number of informative plots and outputs to explore model results and assumptions. Additions for version 0.4.2 Reference SDs can now be used when modelling using SMDs to avoid using study-specific SDs, which can be problematic. Network Meta-Regression: Effect modifiers can now be incorporated using regress.vars argument in <code>mbnma.run()</code>. Various sharing assumptions for effects can be specified in regress.effect. Predictions can be estimated for class effect models Fractional polynomial powers in <code>dfpoly()</code> can only take numeric values from set defined in Jansen 2015. Added <code>calc.edx()</code> to allow easy estimation of different ED values (e.g. ED90 = the dose at which 90% of the maximum response (Emax) is reached) <code>get.relative()</code> now allows simultaneous comparison of two models in a single league table - can be used to compare MBNMA models with different dose-response functions, or MBNMA and NMA models, or NMA models that assume consistency versus those that use Unrelated Mean Effects. Plots of predictions look prettier Dose-response parameters that were previously modelled on an exponential scale (ed50, hill, onset) are now on the natural scale and are assigned truncated normal default priors Separate prior distributions can be specified for different indices of a parameter - allows for agent-specific prior distributions on dose-response parameters.