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Changelog

0.2.x

0.2.4

This version of HSSM incorporated the following changes:

  1. We updated HSSM to be compatible with the major API changes in bambi v0.14.0.
  2. We fixed various graphing issues in pymc 5.16.0+, thanks to the API changes in bambi.
  3. We added variational inference via native pymc.
  4. We can now use float64 inference.
  5. We fixed some minor bugs in providing initial values.
  6. We added a model.dic() convenience function.
  7. We added a model.restore_traces() convenience function.
  8. Other minor bug fixes.

0.2.3

This is a maintenance release of HSSM, mainly to add a version constraint on bambi in light of the many breaking changes that version 0.1.4 introduces. This version also improved compatibility with PyMC>=5.15 and incorporated minor bug fixes:

  1. We incorporated a temporary fix to graphing which broke after PyMC>=5.15.
  2. We deprecated ndim and ndim_supp definition in SSMRandomVariable in PyMC>-5.16.
  3. We fixed a bug that prevents new traces from being returned if model.sample() is called again.

0.2.2

HSSM is now on Conda! We now recommend installing HSSM through conda install -c conda-forge hssm. For advanced users, we also support installing the GPU version of JAX through pip install hssm[cuda12].

This version incorporates various bug fixes:

  1. We fixed a major bug that causes divergences for models using approx_differentiable and blackbox likelihoods. We are still looking into the issues of divergence with analytical likelihoods.
  2. We made the model creation process more robust, fixing errors when categorical variables are used with group identifiers.
  3. We updated the codebase according to the deprecations in higher versions of JAX.
  4. We implemented a temporary fix to an issue that might cause the kernel to die due to OOM.

0.2.1

We added a few new features in 0.2.1:

  1. We have finished updating the HSSM code base to support go-nogo data and deadline. We will provide documentation once the networks are added to our huggingface repo.
  2. We updated hssm.distribution_utils to streamline the creation of pm.Distributions.
  3. We now support response variables other than rt and response. They can be specified through model_config via the new response field.
  4. We have fixed some of the issues with convergence when using log-logit link functions and/or safe priors.

Other minor updates

  • Fixed an incompatible shape error during posterior predictive sampling when p_outlier is estimated as a parameter.
  • Updated documentation for using make_distribution with PyMC.

Bug fixes:

  • Fixed default list of parameters for ddm_full model and the bounds for ddm_sdv model.

0.2.0

This is a major version update! Many changes have taken place in this version:

Breaking changes

When hierarchical argument of hssm.HSSM is set to True, HSSM will look into the data provided for the participant_id field. If it does not exist, an error will be thrown.

New features

  • Added link_settings and prior_settings arguments to hssm.HSSM, which allows HSSM to use intelligent default priors and link functions for complex hierarchical models.

  • Added an hssm.plotting submodule with plot_posterior_predictive() and plot_quantile_probability for creating posterior predictive plots and quantile probability plots.

  • Added an extra_fields argument to hssm.HSSM to pass additional data to the likelihood function computation.

  • Limited PyMC, pytensor, numpy, and jax dependency versions for compatibility.

0.1.x

0.1.5

We fixed the errors in v0.1.4. Sorry for the convenience! If you have accidentally downloaded v0.1.4, please make sure that you update hssm to the current version.

  • We made Cython dependencies of this package available via pypi. We have also built wheels for (almost) all platforms so there is no need to build these Cython dependencies.

0.1.4

  • Added support of blackbox likelihoods for ddm and ddm_sdv models.
  • Added support for full_ddm models via blackbox likelihoods.
  • Added the ability to use hssm.Param and hssm.Prior to specify model parameters.
  • Added support for non-parameter fields to be involved in the computation of likelihoods.
  • Major refactor of the code to improve readability and maintainability.
  • Fixed a bug in model.sample_posterior_predictive().

0.1.3

  • Added the ability to specify infs in bounds.
  • Fixed an issue where nuts_numpyro sampler fails with regression and lapse distribution.
  • Defaults to nuts_numpyro sampler with approx_differentiable likelihoods and jax backend.
  • Added a hssm.show_defaults() convenience function to print out default configs.
  • Added default blackbox likelihoods for ddm and ddm_sdv models.
  • Various under-the-hood documentation improvements.

0.1.2

  • Improved numerical stability of analytical likelihoods.
  • Added the ability to handle lapse distributions.
  • Added the ability to perform prior predictive sampling.
  • Improved model information output - now default priors provided by bambi is also printed.
  • Added a hierarchical switch which turns all parameters into hierarchical when participant_id is provided in data.

  • Parameters are now named more consistently (group-specific terms are now aliased correctly).

  • Fixed a bug where information about which parameter is regression is incorrectly passed.

  • Added links to Colab to try out hssm in Google Colab.

0.1.1

  • Handle float types in PyTensor and JAX more consistently and explicitly.
  • Updated model output format to include likelihood kinds and display bounds more consistently.
  • Support for infs in bounds.
  • Convenient method for simulating data with ssm_simulators.
  • More test coverage.
  • CI workflows for publishing package to PyPI.
  • Enhancement to documentations.