skipTrack

skipTrack-package

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) tools:::Rd_expr_doi("10.1093/jamia/ocab182").

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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>.

Luke Duttweiler

A Bayesian Hierarchical Model that Controls for Non-Adherence in
Mobile Menstrual Cycle Tracking

skipTrack-package function

Maintainer: Luke Duttweiler <a href="/link/lduttweiler%40hsph.harvard.edu?package=skipTrack&version=0.1.0" data-mini-rdoc="skipTrack::lduttweiler@hsph.harvard.edu">lduttweiler@hsph.harvard.edu</a> (<a href="https://orcid.org/0000-0002-0467-995X">ORCID</a>) [copyright holder]

Author

skipTrack: A Bayesian Hierarchical Model that Controls for Non-Adherence in Mobile Menstrual Cycle Tracking — skipTrack-package

Maintainer: Luke Duttweiler <a href='mailto:lduttweiler@hsph.harvard.edu'>lduttweiler@hsph.harvard.edu</a> (<a href='https://orcid.org/0000-0002-0467-995X'>ORCID</a>) [copyright holder]

skipTrack-package: skipTrack: A Bayesian Hierarchical Model that Controls for Non-Adherence in Mobile Menstrual Cycle Tracking

Description

Arguments

Author

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