Package: skipTrack 0.1.1.9000

Luke Duttweiler

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>.

Authors:Luke Duttweiler [aut, cre, cph]

skipTrack_0.1.1.9000.tar.gz
skipTrack_0.1.1.9000.zip(r-4.5)skipTrack_0.1.1.9000.zip(r-4.4)skipTrack_0.1.1.9000.zip(r-4.3)
skipTrack_0.1.1.9000.tgz(r-4.5-any)skipTrack_0.1.1.9000.tgz(r-4.4-any)skipTrack_0.1.1.9000.tgz(r-4.3-any)
skipTrack_0.1.1.9000.tar.gz(r-4.5-noble)skipTrack_0.1.1.9000.tar.gz(r-4.4-noble)
skipTrack_0.1.1.9000.tgz(r-4.4-emscripten)skipTrack_0.1.1.9000.tgz(r-4.3-emscripten)
skipTrack.pdf |skipTrack.html✨
skipTrack/json (API)
NEWS

# Install 'skipTrack' in R:

install.packages('skipTrack', repos = c('https://lukeduttweiler.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/lukeduttweiler/skiptrack/issues

On CRAN:

4.90 score 4 scripts 135 downloads 5 exports 80 dependencies

Last updated 2 months agofrom:77149dd900. Checks:4 OK, 3 NOTE, 2 ERROR. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 28 2025
R-4.5-win	NOTE	Mar 28 2025
R-4.5-mac	NOTE	Mar 28 2025
R-4.5-linux	NOTE	Mar 28 2025
R-4.4-win	ERROR	Mar 28 2025
R-4.4-mac	OK	Mar 28 2025
R-4.4-linux	OK	Mar 28 2025
R-4.3-win	ERROR	Mar 28 2025
R-4.3-mac	OK	Mar 28 2025

Exports:skipTrack.diagnostics skipTrack.fit skipTrack.results skipTrack.simulate skipTrack.visualize

Dependencies:brio callr cli coda codetools colorspace commonmark crayon curl desc diffobj digest doParallel ellipse evaluate fansi farver fftwtools foreach fs genMCMCDiag ggplot2 ggtext glmnet glue gridExtra gridtext gtable highr isoband iterators jpeg jsonlite knitr labeling LaplacesDemon lattice lifecycle litedown magrittr markdown MASS Matrix mcmcse mgcv munsell mvtnorm nlme optimg pillar pkgbuild pkgconfig pkgload png praise processx ps R6 RColorBrewer Rcpp RcppArmadillo RcppEigen rlang rprojroot scales shape stringi stringr survival testthat tibble ucminf utf8 vctrs viridisLite waldo withr xfun xml2 yaml

Getting Started with the SkipTrack Package

Rendered fromskipTrack_intro.Rmdusingknitr::rmarkdownon Mar 28 2025.

Last update: 2024-07-01
Started: 2024-05-03

Citation

Development and contributors

Readme and manuals

Help Manual

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