Define distribution families and fit them to
    interval-censored and interval-truncated data, where the truncation
    bounds may depend on the individual observation. The defined
    distributions feature density, probability, sampling and fitting
    methods as well as efficient implementations of the log-density log
    f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow'
    neural networks via the 'tensorflow' package. Allows training
    parametric neural networks on interval-censored and interval-truncated
    data with flexible parameterization. Applications include Claims
    Development in Non-Life Insurance, e.g. modelling reporting delay
    distributions from incomplete data, see Bücher, Rosenstock (2022)
    <doi:10.1007/s13385-022-00314-4>.

Contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. 
  Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for 
  single sample gene set testing based on randomized reduced rank reconstruction error" <doi:10.1101/2023.04.03.535366>.

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').