Renewal processes model inter-event times between recurrent events using a parametric distribution, but selecting a single model can ignore model uncertainty and affect inference, particularly in small samples. This paper introduces marp, an R package for model-averaged inference in parametric renewal processes. The package fits six commonly used distributions, compares them using Akaike and Bayesian information criteria, and reports selected-model and model-averaged estimates of the mean inter-event time, event probability and hazard function, with percentile and Studentised bootstrap confidence intervals. A simulation study under three data-generating models showed that model averaging did not improve point estimation but often improved interval coverage relative to the AIC-selected model. Applications to seizure-recurrence and engineering failure-time data illustrate the proposed methods. The marp package provides a reproducible framework for inference that accounts for model uncertainty in parametric renewal processes.

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