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This is a record of all past PyLCM releases and what went into them in reverse chronological order. We follow semantic versioning.

Unreleased

Phase grammar, cross-regime transitions, and model-level regime slots

  • Phased(solve=..., simulate=...) gives any regime-slot value a per-phase variant; a bare value broadcasts to both phases. Carried states — Phased(solve=callable, simulate=Grid) in states — are derived functions during backward induction and genuine seeded-and-evolved states in simulation. See the phase grammar explanation.

  • fixed_transition(state_name) marks a fixed state (identity law) in state_transitions. The None spelling for fixed states is removed; a regime-level None now masks a model-level entry instead.

  • Regime transitions take a third form: a per-target dict {target_regime: MarkovTransition(prob_func)} whose key set declares the regime's reachable targets — omitted regimes are structurally unreachable. Per-target dicts in state_transitions hand state values across regime boundaries, including into states the source regime does not carry and across grids that differ between regimes.

  • Model-level regime slots: Model(functions=..., constraints=..., states=..., state_transitions=..., actions=...) declares shared structure once and merges it into every regime under the exactly-one-level rule. Broadcast states and actions are pruned per regime by DAG reachability; model.pruned_variables records the result.

  • model.user_regimes holds plain lcm.regime.Regime instances, finalized at model build (model-level slots merged, default H injected, completeness validated).

Per-target parameters

  • Per-target transition parameters nest under the target regime's name in the params template — template[regime][target][func][param] — replacing the to_<target>_… spelling. Param qnames parallel engine function qnames.

  • Parameters resolve at four levels, most to least specific: target / function (one value broadcasts over the law's targets) / regime / model. Exactly one level per parameter; multi-level specifications are ambiguity errors.

  • Canonical flat params always key transition-law params per target, every target of a broadcast value sharing one leaf object. A coarse regime transition is evaluated once and shared, so it takes no per-target parameters.

  • Model-level derived_categoricals follow the exactly-one-level rule of the other model-level slots: a name declared at model level and regime level is an ambiguity error, also when the grids match.

0.0.1

Initial Release

  • First public release of PyLCM.

  • Includes core functionality:

    • Specification of finite-horizon discrete-continuous choice models with an arbitrary number of discrete and continuous states and actions.

    • Linearly and Log-linearly spaced grids that approximate continuous states and actions.

    • Linear interpolation and extrapolation of the value function for continuous states.

    • Grid search (brute-force) for finding the optimal continuous policy.

    • Stochastic state transitions for discrete states which may depend on other discrete states and actions.

  • Built with contributions from the PyLCM team.

Contributions

Thanks to everyone who contributed to this release:

  • {ghuser}hmgaudecker

    Initiated and drove the development agenda for PyLCM, ensuring strategic direction and alignment. He actively steered the project, facilitated collaboration, and secured funding to support core development. Additionally, he reviewed pull requests and provided feedback on the internal and external code structure and design.

  • {ghuser}janosg

    Designed and implemented the initial prototype of PyLCM, laying the foundation for its development. He onboarded {ghuser}timmens and played a key role in shaping the project's direction. After stepping back from active development, he contributed to implementation discussions and later provided guidance on architectural decisions.

  • {ghuser}timmens

    Took over development of PyLCM, expanding its functionality with key features like the simulation function, extrapolation capabilities, and special arguments. He led extensive refactoring to improve code clarity, maintainability, and testability, making the package easier to develop and extend. His contributions also include improved documentation, type annotations, static type checking, and the introduction of example and explanation notebooks.

  • {ghuser}mj023

    Analyzed and optimized PyLCM's performance on the GPU, profiling execution and examining the computational graph of JAX-compiled functions. He fine-tuned the solve function's just-in-time compilation to reduce runtime and improve efficiency. Additionally, he compared PyLCM's performance against similar libraries, providing insights into its computational efficiency.

  • {ghuser}mo2561057

    Added tests for the model processing and fully discrete models.

  • {ghuser}MImmesberger

    Added checks to test PyLCM's results against analytical solutions.

Early contributors

  • {ghuser}segsell

  • {ghuser}ChristianZimpelmann

  • {ghuser}tobiasraabe