marp: Model-Averaged Inference for Parametric Renewal Processes in R
Abstract
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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BibTeX
@misc{tsou2026marp,
title = {{marp: Model-Averaged Inference for Parametric Renewal Processes in R}},
author = {Veronica W. Tsou and Vanessa Huang and Jie Kang},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.07519},
eprint = {2611.07519},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.07519},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - marp: Model-Averaged Inference for Parametric Renewal Processes in R
AU - Veronica W. Tsou
AU - Vanessa Huang
AU - Jie Kang
PY - 2026
DA - 2026/11/05
PB - arXiv
DO - 10.48550/arxiv.2611.07519
UR - https://arxiv.org/abs/2611.07519
AB - 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.
ER -