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>.
| Version: |
0.2.0 |
| 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) |
| Published: |
2025-09-10 |
| DOI: |
10.32614/CRAN.package.skipTrack |
| Author: |
Luke Duttweiler 
[aut, cre, cph] |
| Maintainer: |
Luke Duttweiler <lduttweiler at hsph.harvard.edu> |
| BugReports: |
https://github.com/LukeDuttweiler/skipTrack/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/LukeDuttweiler/skipTrack |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
skipTrack results |