Welcome to the SkipTrack Package!

SkipTrack is a Bayesian hierarchical model for self-reported menstrual cycle length data on mobile health apps. The model is an extension of the hierarchical model presented in Li et al. (2022) that focuses on predicting an individual’s next menstrual cycle start date while accounting for cycle length inaccuracies introduced by non-adherence in user self-tracked data. Check out the ‘Getting Started’ vignette to see an overview of the SkipTrack Model!

Installation

#Install from CRAN
install.packages('skipTrack')
#Install Development Version
devtools::install_github("LukeDuttweiler/skipTrack")

Package Usage

The SkipTrack package provides functions for fitting the SkipTrack model, evaluating model run diagnostics, retrieving and visualizing model results, and simulating related data. We begin our tutorial by examining some simulated data.

library(skipTrack)

First, we simulate data on 100 individuals from the SkipTrack model where each observed