In the early stages of drug development there is often uncertainty about the most promising among a set of different treatments, different doses of the same treatment, or combinations of treatments. Multi-arm multi-stage (MAMS) clinical studies provide an efficient solution to determine which intervention is most promising. In this paper we discuss the R package MAMS that allows designing such studies within the group-sequential framework. The package implements MAMS studies with normal, binary, ordinal, or timeto-event endpoints in which either the single best treatment or all promising treatments are continued at the interim analyses. Additionally unexpected design modifications can be accounted for via the use of the conditional error approach. We provide illustrative examples of the use of the package based on real trial designs.