An extension of the 'AlphaSimR' package
  (<https://cran.r-project.org/package=AlphaSimR>) for stochastic simulations of
  honeybee populations and breeding programmes. 'SIMplyBee' enables simulation of
  individual bees that form a colony, which includes a queen, fathers (drones
  the queen mated with), virgin queens, workers, and drones. Multiple colony can
  be merged into a population of colonies, such as an apiary or a whole country
  of colonies. Functions enable operations on castes, colony, or colonies, to
  ease 'R' scripting of whole populations. All 'AlphaSimR' functionality with
  respect to genomes and genetic and phenotype values is available and further
  extended for honeybees, including haplo-diploidy, complementary sex determiner
  locus, colony events (swarming, supersedure, etc.), and colony phenotype values.

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.