mkdocs.yml

site_name: HSSM
repo_name: lnccbrown/HSSM
repo_url: https://github.com/lnccbrown/HSSM/
edit_uri: edit/main/docs

nav:
  - Home:
      - Overview: index.md
      - Credits: credits.md
      - License: license.md
      - Changelog: changelog.md
  - Getting Started:
      - Getting HSSM:
          - Installation: getting_started/installation.md
      - Using HSSM:
          - Quickstart Guide: getting_started/getting_started.ipynb
          - Hierarchical modeling: getting_started/hierarchical_modeling.ipynb
  - API References:
      - HSSM core classes:
          - hssm.HSSM: api/hssm.md
          - hssm.Param: api/param.md
          - hssm.ModelConfig: api/model_config.md
          - hssm.Prior: api/prior.md
          - hssm.Link: api/link.md
      - Useful functions:
          - hssm.load_data: api/load_data.md
          - hssm.simulate_data: api/simulate_data.md
          - hssm.set_floatX: api/set_floatx.md
          - hssm.show_defaults: api/show_defaults.md
          - hssm.check_data_for_rl: api/check_data_for_rl.md
      - Likelihoods:
          - hssm.likelihoods: api/likelihoods.md
      - Working with distributions:
          - hssm.distribution_utils: api/distribution_utils.md
      - Plotting:
          - hssm.plotting: api/plotting.md
  - Tutorials:
      - Main tutorial: tutorials/main_tutorial.ipynb
      - Choice-only models: tutorials/choice_only_models.ipynb
      - Understanding likelihood functions in HSSM: tutorials/likelihoods.ipynb
      - Plotting: tutorials/plotting.ipynb
      - Using lapse probabilities: tutorials/lapse_prob_and_dist.ipynb
      - Variational Inference: tutorials/variational_inference.ipynb
      - Hierarchical Variational Inference: tutorials/variational_inference_hierarchical.ipynb
      - Using HSSM low-level API directly with PyMC: tutorials/pymc.ipynb
      - Reinforcement Learning - Sequential Sampling Models (RLSSM): tutorials/rlssm_tutorial.ipynb
      - Add custom RLSSM models: tutorials/add_custom_rlssm_model.ipynb
      - Custom models: tutorials/jax_callable_contribution_onnx_example.ipynb
      - BayesFlow LRE Integration: tutorials/bayesflow_lre_integration.ipynb
      - Custom models from onnx files: tutorials/blackbox_contribution_onnx_example.ipynb
      - Using compiled log-likelihood functions: tutorials/compile_logp.ipynb
      - Saving and loading models: tutorials/save_load_tutorial.ipynb
      - Bayesian t-test: tutorials/tutorial_bayesian_t_test.ipynb
      - Using the do-operator: tutorials/do_operator.ipynb
      - Getting trial-wise parameters: tutorials/tutorial_trial_wise_parameters.ipynb
      - Stim Coding: tutorials/tutorial_stim_coding.ipynb
      - P-outlier Regression: tutorials/tutorial_p_outlier_regression.ipynb
      - Sampling with Bayeux: tutorials/tutorial_bayeux.ipynb
      - Hidden Markov Models with DDM emissions: tutorials/hmm_ddm_regime_switching.ipynb
  - Contributed Tutorials:
      - Winterbrain 2025 Workshop Talk 1: tutorials/hssm_tutorial_workshop_1.ipynb
      - Winterbrain 2025 Workshop Talk 2: tutorials/hssm_tutorial_workshop_2.ipynb
      - Scientific Workflow: tutorials/scientific_workflow_hssm.ipynb
      - MathPsych Workshop 2025: tutorials/pymc_to_hssm.ipynb

  - Contributing:
      - Developing locally: local_development.md
      - Contributing: CONTRIBUTING.md

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        - tutorials/likelihoods.ipynb
        - tutorials/variational_inference.ipynb
        - tutorials/variational_inference_hierarchical.ipynb
        - tutorials/initial_values.ipynb
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extra:
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