Tools and methods to simulate populations for surveys based
    on auxiliary data. The tools include model-based methods, calibration and
    combinatorial optimization algorithms, see Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v079.i10>) and
    Templ (2017) <doi:10.1007/978-3-319-50272-4>. The package was developed with support of
    the International Household Survey Network, DFID Trust Fund TF011722 and funds
    from the World bank.

A general, 'tidyverse'-friendly framework 
  for simulation studies, design analysis, and power analysis. 
  Specify data generation, define varying parameters, generate data, 
  fit models, and tidy model results in a single pipeline, without 
  needing loops or custom functions.

Provides functionality for analytically calculating parameters (via the 
    'InteractionPoweR' package) useful for simulation of moderated multiple 
    regression, based on the correlations among the predictors and outcome and 
    the reliability of predictors.

Easy to use interfaces to a number of imputation methods
        that fit in the not-a-pipe operator of the 'magrittr' package.

This is an all-encompassing suite to facilitate the simulation of
    so-called quantities of interest by way of a multivariate normal distribution
    of the regression model's coefficients and variance-covariance matrix.

Calculate power for generalised linear mixed models, using
    simulation. Designed to work with models fit using the 'lme4' package.
    Described in Green and MacLeod, 2016 <doi:10.1111/2041-210X.12504>.

Simulate populations  with desired 
  properties and extract respondent driven samples. To better understand the 
  usage of the package and the algorithm used, please refer to Perera, A., and 
  Ramanayake, A. (2019) 
  <https://www.aimr.tirdiconference.com/assets/images/portfolio/Conference-Proceeding-AIMR-19.pdf>.

Simulation of recurrent event data for non-constant baseline
    hazard in the total time model with risk-free intervals and possibly a competing event.
    Possibility to cut the data to an interim data set. Data can be plotted.
    Details about the method can be found in Jahn-Eimermacher, A. et al. (2015) <doi:10.1186/s12874-015-0005-2>.