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.

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

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>.

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>.

Similarity regression,
    evaluating the probability of association between sets of ontological terms
    and binary response vector. A no-association model is compared with one in which
    the log odds of a true response is linked to the semantic similarity
    between terms and a latent characteristic ontological profile - 'Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases', Greene et al 2016 <doi:10.1016/j.ajhg.2016.01.008>.

Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts.
    This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an 'RStudio' gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) <doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., Almøy, T., Sæbø, S. (2018) <doi:10.1016/j.chemolab.2018.02.009>.