Changes in version 0.6.0 (2025-09-12)                  

  - Updated dag_numpyro() to address compatibility issues with
    JAX 0.7.1, resolving the error:
    ImportError: cannot import name 'pjit_p' from
    'jax.experimental.pjit'.
  - Removed the dependency on Python’s arviz package to improve
    long-term compatibility across Python environments. The arviz
    dependency has introduced bugs in past releases and remains unstable
    in its current version.
  - Revised dag_plotp() to suppress warning messages triggered by recent
    updates to ggplot2.

                     Changes in version 0.5.8.9000                      

                 Changes in version 0.5.8 (2025-08-22)                  

  - patch release to support changes to object types in ggplot2.

                        Changes in version 0.5.6                        

  - patch release to fix numpyro integration issues. The default
    installation workflow now uses numpyro==0.16.1 to avoid TypeError
    when using a discrete data distribution, e.g. Bernoulli.

                 Changes in version 0.5.5 (2024-04-24)                  

  - patch release to fix installation issues. The default installation
    workflow now uses scipy==0.12 to avoid error cannot import name
    'gaussian' from 'scipy.signal'.

                 Changes in version 0.5.4 (2024-02-25)                  

  - patch release to fix installation issues. The default installation
    workflow now uses numpyro==0.13.2 to avoid ModuleNotFoundError: No
    module named 'jax.linear_util'.

                 Changes in version 0.5.3 (2023-09-05)                  

  - Fixed bug where beta distribution was being treated as Laplace
    distribution.
  - Fixed bug where nested plated were being indexed incorrectly in
    Python.

                 Changes in version 0.5.2 (2023-08-19)                  

  - Switched inference to Python's numpyro
  - dag_greta() is now deprecated; dag_numpyro() should be used as a
    drop-in replacement.
  - Added helper function for Python dependencies: install_causact_deps.
  - Updated documentation to reflect switch to numpyro.
  - Added ability to choose fill of nodes during dag_render().
  - Added causact probability distribution functions. See
    ?causact::distributions.
  - Temporarily removed support for the dim argument of probability
    distributions.

                 Changes in version 0.4.2 (2022-06-14)                  

  - Added `vignette("narrative-to-insight-with-causact") to introduce
    the package's encouraged user workflow.
  - Allow greta::variable() to be used for flat priors.

                 Changes in version 0.4.1 (2022-01-19)                  

  - Fixed bugs introduced by changes in tidyr::replace_na().
  - Fixed bug related to rendering generative DAGs where two variables
    were both on multiple plates.

                 Changes in version 0.4.0 (2021-01-25)                  

Minor improvements and fixes

  - Support for using greta matrix operations like %*% and other greta
    functions within the rhs argument of the dag_node function has been
    added.
  - The readme has been updated to include a mixture example.

                 Changes in version 0.3.3 (2020-07-25)                  

Minor improvements and fixes

  - Small graphs without an rhs argument supplied to dag_node()
    displayed a dplyr warning message.
  - Exported meaningfulLabels() function for users who are using code
    generated by dag_greta(mcmc=FALSE).

                 Changes in version 0.3.2 (2020-07-09)                  

Minor improvements and fixes

  - dag_render() displayed weird DAGs when users have dplyr (>=v1.0)
    installed. Isolated problem to changes in dplyr::group_indices() and
    dplyr::bind_cols that caused a jumbling of node id's.
  - Updated README.md to display images and give CRAN install
    instructions.

                 Changes in version 0.3.1 (2020-06-24)                  

  - Initial release for CRAN
  - Biggest change from previous development versions is that
    dag_greta() now returns a dataframe as opposed to creating variables
    in your global environment.