A Bayesian Hierarchical Model that Controls for Non-Adherence in Mobile Menstrual Cycle Tracking R package skipTrack version 0.2.0 Implements a Bayesian hierarchical model designed to identify skips in mobile menstrual cycle self-tracking on mobile apps. Future developments will allow for the inclusion of covariates affecting cycle mean and regularity, as well as extra information regarding tracking non-adherence. Main methods to be outlined in a forthcoming paper, with alternative models from Li et al. (2022) <doi:10.1093/jamia/ocab182>. Software Imports: doParallel (>= 1.0.0), foreach (>= 1.5.0), genMCMCDiag (>= 0.2.0), ggplot2 (>= 3.4.0), ggtext (>= 0.1.0), glmnet (>= 4.1.0), gridExtra (>= 2.0), LaplacesDemon (>= 16.0.0), lifecycle, mvtnorm (>= 1.2.0), optimg (>= 0.1.2), parallel (>= 4.0.0), stats (>= 4.0.0), utils (>= 4.0.0) Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Luke Duttweiler <lduttweiler@hsph.harvard.edu> Comprehensive R Archive Network (CRAN) Luke Duttweiler [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-0467-995X>) MIT + file LICENSE (https://CRAN.R-project.org/package=skipTrack/LICENSE) 2025-09-10 application/tgz https://CRAN.R-project.org/package=skipTrack doi:10.32614/CRAN.package.skipTrack