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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>.

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 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:

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

Downloads:

Package source: skipTrack_0.1.0.tar.gz
Windows binaries: r-devel: skipTrack_0.1.0.zip, r-release: skipTrack_0.1.0.zip, r-oldrel: skipTrack_0.1.0.zip
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=skipTrack to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.