Package: skipTrack 0.2.0.9000

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_0.2.0.9000.tar.gz
skipTrack_0.2.0.9000.zip(r-4.7-any)skipTrack_0.2.0.9000.zip(r-4.6-any)skipTrack_0.2.0.9000.zip(r-4.5-any)
skipTrack_0.2.0.9000.tgz(r-4.6-any)skipTrack_0.2.0.9000.tgz(r-4.5-any)
skipTrack_0.2.0.9000.tar.gz(r-4.7-any)skipTrack_0.2.0.9000.tar.gz(r-4.6-any)
skipTrack_0.2.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
skipTrack/json (API)

# 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:

4.48 score 4 scripts 335 downloads 5 exports 72 dependencies

Last updated from:e300e53a44. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	166
source / vignettes	OK	263
linux-release	OK	175
macos-release	OK	161
macos-oldrel	OK	182
windows-devel	OK	110
windows-release	OK	97
windows-oldrel	OK	125
wasm-release	OK	127

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

Dependencies:brio callr cli coda codetools commonmark cpp11 crayon curl desc diffobj doParallel ellipse evaluate 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 Matrix mcmcse mvtnorm optimg otel pkgbuild pkgload png praise processx ps R6 RColorBrewer Rcpp RcppArmadillo RcppEigen rlang rprojroot S7 scales shape stringi stringr survival testthat ucminf vctrs viridisLite waldo withr xfun xml2 yaml

Getting Started with the SkipTrack Package

Rendered fromskipTrack_intro.Rmdusingknitr::rmarkdown

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 li model.	liSimCAT
Simulate user tracked menstrual cycle data for an individual using the mixture model.	mixSim
Simulate user tracked menstrual cycle data for an individual using the mixture model (categorical covariates).	mixSimCAT
Plot skipTrack.model objects	plot.skipTrack.model
Sample a value from the full conditional posterior of b_i	postB
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
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
Simulate user tracked menstrual cycle data for an individual, based on the skipTrack model WITH CATEGORICAL COVARIATES	stSimCAT
Report skipTrack.model results to console	summary.skipTrack.model