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skipTrack: A Bayesian Hierarchical Model that Controls for Non-Adherence in Mobile Menstrual Cycle Tracking
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>.
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Package details |
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| Author | Luke Duttweiler [aut, cre, cph] (<https://orcid.org/0000-0002-0467-995X>) |
| Maintainer | Luke Duttweiler <lduttweiler@hsph.harvard.edu> |
| License | MIT + file LICENSE |
| Version | 0.1.2 |
| URL | https://github.com/LukeDuttweiler/skipTrack |
| Package repository | View on CRAN |
| Installation |
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