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

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

## Documentation for package ‘skipTrack’ version 0.2.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

gibbsStepLi | Gibbs Step Li - One MCMC step for the Li Model
liInference | Perform hyperparameter inference assuming the model given in Li et al. (2022) on a cycle length dataset.
likVec | Monte Carlo estimate of negative marginal log-likelihood of Li model
liMCMC | Runs MCMC algorithm for performing inference using the model from Li et al. (2022)
liSim | Simulate user tracked menstrual cycle data for an individual using the li model.
mixSim | Simulate user tracked menstrual cycle data for an individual using the mixture model.
plot.skipTrack.model | Plot skipTrack.model objects
postB | Sample a value from the full conditional posterior of b_i
postBeta | Draw from Posterior Distribution for Beta Parameters
postCij | Sample a vector of values from the full conditional posterior of the c_ij vector
postGamma | Perform a Metropolis-Hastings Step for Drawing a New Gamma
postLambdai | Compute random draw from full conditional posterior for lambda_i in Li algorithm.
postPhi | Metropolis-Hastings step to draw a new value for phi.
postPi | Sample a value from the full conditional posterior of pi
postPii | Compute M-H draw for pi_i in Li algorithm
postRho | Sample a value from the full conditional posterior of rho
postSij | Compute random draw from full conditional posterior for s_ij in Li algorithm.
postTaui | Sample a value from the full conditional posterior of tau_i
print.skipTrack.model | Print skipTrack.model to console
sampleStep | Perform a single step of the MCMC sampling process for skipTrack
skipTrack.diagnostics | skipTrack MCMC Diagnostics
skipTrack.fit | Fits the skipTrack Model using 1 or more MCMC chains
skipTrack.MCMC | Perform one chain of MCMC sampling for the skipTrack model.
skipTrack.results | Get tables of Inference results from skipTrack.fit
skipTrack.simulate | Simulate user-tracked menstrual cycle data for multiple individuals
skipTrack.visualize | Visualize Results from skipTrack.fit
str.skipTrack.model | Report skipTrack.model structure to console
stSim | Simulate user tracked menstrual cycle data for an individual, based on the skipTrack model.
summary.skipTrack.model | Report skipTrack.model results to console