Simulate age-structured populations that vary in space and time and
explore the efficacy of a range of built-in or user-defined sampling
protocols to reproduce the population parameters of the known population.
(See Regular et al. (2020) <doi:10.1371/journal.pone.0232822> for more
details).
sp, raster, rgeos, data.table, magrittr, progress, doParallel,
parallel, foreach, plotly, rlang

sp, raster, data.table, magrittr, progress, doParallel,
parallel, foreach, plotly, rlang
fields, rmarkdown, flexdashboard, shiny, crosstalk,
htmltools, viridis, lme4, ggplot2, INLA, rgdal, rgeos, knitr,
bezier

Simulate Longitudinal Dataset with Time-Varying Correlated
Covariates
Maya B. Mathur, Kristopher Kapphahn, Ariadna Garcia, Manisha Desai, Maria E. Montez-Rath
Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. <arXiv:1709.10074> for methodological details.
metafor, mvtnorm, ICC, miscTools, car, plyr, corpcor, psych,
stats, utils

Simulation Tools for Planning Vitamin D Studies
Simulation tools for planning Vitamin D studies including
approximation of power. Individual vitamin D status
profiles are simulated, modelling population heterogeneity.
Individuals experience random exposures to infectious 		agents. Development of infection considers a heterogeneous treatment effect. This is incorporated into the power approximation.

c( person( "Rebecca", "Mangan", email="manganre@tcd.ie", role = c( "aut" ) ), person( "Jason", "Wyse", email = "wyseja@tcd.ie", role = c("aut","cre") ), person("Lina", "Zgaga", email="zgagal@tcd.ie", role= "aut") )
Rebecca Mangan [aut],
Jason Wyse [aut, cre],
Lina Zgaga [aut]

c( person( "Rebecca", "Mangan", email="manganre@tcd.ie", role = c( "aut" ) ), person(  "Jason", "Wyse", email = "wyseja@tcd.ie", role = c("aut","cre") ), person("Lina", "Zgaga", email="zgagal@tcd.ie", role= "aut") )
Simulation tools for planning Vitamin D studies. Individual vitamin D status
profiles are simulated, modelling population heterogeneity in trial arms.
Exposures to infectious agents are generated, with infection depending on vitamin D status.