BivRec: an R package for the nonparametric and semiparametric analysis of bivariate alternating recurrent events

Sandra Castro-Pearson, Aparajita Sur, Chi Hyun Lee, Chiung Yu Huang, Xianghua Luo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Background: Bivariate alternating recurrent event data can arise in longitudinal studies where patients with chronic diseases go through two states that occur repeatedly, e.g., care periods and break periods. However, there was no statistical software that provided tools for the analysis of such data. To meet this software need, we developed BivRec, a package for R that contains a set of tools for exploratory, nonparametric and semiparametric regression analysis of bivariate alternating recurrent events. Results: The BivRec package provides functions for nonparametric estimations for the joint distribution of bivariate gap times (bivrecNP) and semiparametric regression methods for evaluating covariate effects on the two types of gap times under the accelerated failure time model framework (bivrecReg). The package also provides exploratory data analysis tools such as a visualization of the gap times by groups. We utilize a subset of the South Verona Psychiatric Case Register (PCR) data to illustrate the use of the BivRec package for the reviewed methods. Conclusions: We demonstrate BivRec’s capability for data visualization, nonparametric and regression based analysis, as well as data simulation. The package has default methods with satisfactory performance despite the complexity of calculations and fills a gap in software for statistical analysis of bivariate alternating recurrent events. BivRec is accessible under the GPL-3 General Public License through CRAN, facilitating its installation.

Original languageEnglish (US)
Article number92
JournalBMC Medical Research Methodology
Issue number1
StatePublished - Dec 2022

Bibliographical note

Funding Information:
The authors would like to thank Dr. Sy Han Chiou who has provided valuable suggestions during the development of the package.

Publisher Copyright:
© 2022, The Author(s).


  • BivRec
  • Bivariate gap times
  • Recurrent events


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