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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>.

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VersionRepositoryFileSize
0.2.0 rolling linux/jammy R-4.5 skipTrack_0.2.0.tar.gz 561.5 KiB
0.2.0 rolling linux/noble R-4.5 skipTrack_0.2.0.tar.gz 561.4 KiB
0.2.0 rolling source/ R- skipTrack_0.2.0.tar.gz 470.3 KiB
0.2.0 latest linux/jammy R-4.5 skipTrack_0.2.0.tar.gz 561.5 KiB
0.2.0 latest linux/noble R-4.5 skipTrack_0.2.0.tar.gz 561.4 KiB
0.2.0 latest source/ R- skipTrack_0.2.0.tar.gz 470.3 KiB
0.2.0 2026-04-26 source/ R- skipTrack_0.2.0.tar.gz 470.3 KiB
0.2.0 2026-04-23 source/ R- skipTrack_0.2.0.tar.gz 470.3 KiB
0.2.0 2026-04-09 windows/windows R-4.5 skipTrack_0.2.0.zip 567.8 KiB
0.1.2 2025-04-20 source/ R- skipTrack_0.1.2.tar.gz 420.8 KiB

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