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>.

Version: 0.1.0
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: 2024-05-16
DOI: 10.32614/CRAN.package.skipTrack
Author: Luke Duttweiler ORCID iD [aut, cre, cph]
Maintainer: Luke Duttweiler <lduttweiler at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: skipTrack results


Reference manual: skipTrack.pdf
Vignettes: Getting Started with the SkipTrack Package


Package source: skipTrack_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): skipTrack_0.1.0.tgz, r-oldrel (arm64): skipTrack_0.1.0.tgz, r-release (x86_64): skipTrack_0.1.0.tgz, r-oldrel (x86_64): skipTrack_0.1.0.tgz


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