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.

Uses simulations from generalized linear mixed-effects models to incorporate random effects across multiple sources and levels of variation, and a dispersion parameter to account for overdispersion and capture unexplained variability. Covers design scenarios for both short-term and long-term trials evaluating the impact of single or combined vector control interventions. Methods build on Kipingu et al. (2025) <doi:10.1186/s12936-025-05454-y> and Johnson et al. (2015) <doi:10.1111/2041-210X.12306>.

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>.