Simulates individual-based models of agricultural pest management
    and the evolution of pesticide resistance. Management occurs on a spatially
    explicit landscape that is divided into an arbitrary number of farms that
    can grow one of up to 10 crops and apply one of up to 10 pesticides. Pest
    genomes are modelled in a way that allows for any number of pest traits with
    an arbitrary covariance structure that is constructed using an evolutionary
    algorithm in the mine_gmatrix() function. Simulations are then run using the
    run_farm_sim() function. This package thereby allows for highly mechanistic
    social-ecological models of the evolution of pesticide resistance under
    different types of crop rotation and pesticide application regimes.

Flexibly restructure and aggregate data using 
  just two functions: melt and cast.

Flexibly restructure and aggregate data using just two
    functions: melt and 'dcast' (or 'acast').

Summarize model output using a robust effect size index. The index is introduced in Vandekar, Tao, & Blume (2020, <doi:10.1007/s11336-020-09698-2>). Software paper available at <doi:10.18637/jss.v112.i03>.

Developed to assist researchers with planning analysis,
    prior to obtaining data from Trusted Research Environments (TREs) also known as safe havens.
    With functionality to export and import marginal distributions as well as synthesise data, both with
    and without correlations from these marginal distributions. Using a multivariate cumulative distribution (COPULA).
    Additionally the International Stroke Trial (IST) is included as an example dataset under ODC-By licence
    Sandercock et al. (2011) <doi:10.7488/ds/104>,
    Sandercock et al. (2011) <doi:10.1186/1745-6215-12-101>.

Datasets with energy consumption data of different data measurement frequencies. 
    The data stems from several publicly funded research projects of the Chair of Information  
    Systems and Energy Efficient Systems at the University of Bamberg.