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Meta-analysis methods are used to combine evidence from multiple studies. Meta-regression as well as model-based meta-analysis are extensions of standard pairwise meta-analysis in which information about study-level covariates and (arm-level) dosing amount or exposure may be taken into account. A Bayesian approach to inference is very attractive in this context, especially when a meta-analysis is based on few studies only or rare events. In this article, we present the R package MetaStan which implements a wide range of pairwise and model-based meta-analysis models. A generalised linear mixed model (GLMM) framework is used to describe the pairwise meta-analysis, meta-regression and model-based meta-analysis models. Within the GLMM framework, the likelihood and link functions are adapted to reflect the nature of the data. For example, a binomial likelihood with a logit link is used to perform a meta-analysis based on datasets with dichotomous endpoints. Bayesian computations are conducted using Stan via the rstan interface. Stan uses a Hamiltonian Monte Carlo sampler which belongs to the family of Markov chain Monte Carlo methods. Stan implementations are done by using suitable parametrizations to ease computations. The user-friendly R package MetaStan, available on CRAN, supports a wide range of pairwise and model-based meta-analysis models. MetaStan provides fitting functions for pairwise meta-analysis with the option of including covariates and model-based meta-analysis. The supported outcome types are continuous, binary, and count. Forest plots for the pairwise meta-analysis and dose-response plots for the model-based meta-analysis can be obtained from the package. The use of MetaStan is demonstrated through clinical examples.