A weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 26 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc.
    Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation.
    Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. <https://ui.adsabs.harvard.edu/abs/2019AGUFMGC41G1267G>.

Meta testing is the ability to test a function without having to 
    provide its parameter values.
    Those values will be generated, based on semantic naming of parameters, as 
    introduced by package 'wyz.code.offensiveProgramming'.
    Value generation logic can be completed with your own data types 
    and generation schemes. This to meet your most specific requirements and to 
    answer to a wide variety of usages, from general use case to very specific
    ones.
    While using meta testing, it becomes easier to generate stress test 
    campaigns, non-regression test campaigns and robustness test campaigns, as 
    generated tests can be saved and reused from session to session. 
    Main benefits of using 'wyz.code.metaTesting' is ability to discover valid 
    and invalid function parameter combinations, ability to infer valid 
    parameter values, and to provide smart summaries that allows you to focus
    on dysfunctional cases.

Allows to turn standard R code into offensive programming code. 
    Provides code instrumentation to ease this change and tools to assist and 
    accelerate code production and tuning while using offensive programming code 
    technics. 
    Should improve code robustness and quality. Function calls can be easily 
    verified on-demand or in batch mode to assess parameter types and length 
    conformities. 
    Should improve coders productivity as offensive programming reduces the code
    size due to reduced number of controls all along the call chain. 
    Should speed up processing as many checks will be reduced to one single check.