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RELEASE_NOTES.MD

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2.0.0 - 2024-04-24

  • FEATURE: Domain Specific Language (DSL) for scaffolding models.
  • FEATURE: User-definable RNG seeds.
  • FEATURE: Scaffold many models into competing hypotheses.
  • FEATURE: One-step-ahead analysis of model performance.
  • FEATURE: Benchmark suite of common test functions for optimisation routines
  • FEATURE: Performance tuning of optimisers. For example, Filzbach 94% faster.
  • FEATURE: Documentation website uses latest fsdocs and includes fully worked examples.
  • BREAKING: We recommend using the DSL to construct models. Underlying F# record type signatures have changed and will break version 1.x scripts.
  • BUGFIX: Amoeba functions respect end conditions.

2.0.0-alpha2 - 2022-04-27

  • FEATURE: Add univariate gaussian -log likelihood function

2.0.0-alpha1 - 2021-09-28

  • FEATURE: Domain Specific Language (DSL) for scaffolding models in the Bristlecone.Language namespace.
  • FEATURE: Pass user-defined Random instances to the EstimationEngine to enable working with seeds for reproducing previous results.
  • FEATURE: Automatically generate named hypotheses from nested model systems.
  • BREAKING: We recommend using the DSL to construct models. Underlying F# record type signatures have changed and will break version 1.x scripts.

1.0.0 - 2019-09-10

  • FEATURE: Basic component logging of internal model processes.
  • FEATURE: Calculate and save convergence statistics between MCMC chains.
  • FEATURE: Sunlight cache to precompute day length calculations for faster analyses.

1.0.0-alpha6 - 2019-06-17

  • FEATURE: Configuration options for Filzbach and SA optimisation.
  • FEATURE: Calculations for day length (sunrise and sunset) built-in to Bristlecone.Dendro.
  • FEATURE: Calculate confidence intervals using likelihood interval technique.
  • FEATURE: Standardised formats for loading and saving model-selection results.

1.0.0-alpha5 - 2019-03-23

  • Release net47 and netstandard2.0 versions
  • Automatic documentation (based on ProjectScaffold)

1.0.0-alpha4 - 2019-03-18

  • BUGFIX: Fixed divide by zero error when running testModel

1.0.0-alpha3 - 2019-03-18

  • FEATURE: Simulated Annealing optimisation
  • FEATURE: Filzbach-style optimisation
  • FEATURE: Run models using higher-resolution forcing data with lower-resolution observations
  • FEATURE: Measurement variables

1.0.0-alpha2 - 2019-01-10

  • FEATURE: More optimisation algorithms, including: automatic generalised MCMC; Metropolis-within-Gibbs; and Adaptive Metropolis-within-Gibbs
  • FEATURE: Support for 'EndConditions' on optimisation algorithms. The library includes a) number of iterations, and b) linear trends in square jumping distances, as two possibilities.

1.0.0-alpha1 - 2018-12-20

  • FEATURE: Initial release