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: 0.1.2 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: 2025-01-27 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=skipTrack to link to this page.
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