Cmdstanpy

- Fix an issue where Stan fit objects were not `pickle`-able when they previously were.
Note: We still do not recommend pickling cmdstanpy objects, but rather using functions `save_csvfiles` and `from_csv`.

- CmdStanPy can now format (and canonicalize) your Stan files with `CmdStanModel.format()`
- Stan variables can now be accessed from fit objects using the `.` syntax when no naming conflicts occur. For example, previous code `fit.stan_variable("my_cool_variable")` can now be written `fit.my_cool_variable`
- CmdStanPy is more robust to running in threaded environments and tries harder to not overwrite its own output files
- The `install_cmdstan` script can now be run in interactive mode using `--interactive`/`-i`
- CmdStanPy now computes some diagnostics after running HMC and will warn you about post-warmup divergences and treedepth exceptions
- Runtime exceptions in the `generated quantities` block should be recognized better now.
- The default level of precision to `fit.summary()` is now 6, as it is when `stansummary` is used from the command line.\
- Various documentation improvements

- Support new optimizations in CmdStan 2.29
- Support complex numbers as both inputs and outputs of Stan programs
- Sped up assembling output by only reading draws at most once
- Fixed an issue where a command failing could change your working directory
- Improve error messages in some cases
- CmdStanPy no longer changes the global root logging level

Note: The minimum supported version for CmdStanPy is now Python 3.7.

After almost 2 years in the beta barrel, we’re proud to release CmdStanPy 1.0. Many thanks to all of our [contributors](https://github.com/stan-dev/cmdstanpy/graphs/contributors), as well as to all users who’ve submitted feature requests and bug reports!

CmdStanPy provides access to the Stan compiler, inference algorithms, and diagnostics. It supports both [develop](https://github.com/stan-dev/cmdstan)ment and production workflows and is well-suited for teaching and learning Stan.

Why use CmdStanPy for your next project?

- Use new features: CmdStanPy runs with the latest CmdStan (or any version of CmdStan, including develop)
- Ease of installation: CmdStanPy can automatically install CmdStan and the underlying C++ toolchain.
- Scalable: can fit big models and big datasets – if CmdStan can fit a model and dataset, so can CmdStanPy.
- Cross-platform: runs on Linux, macOS, and Windows.
- Non-restrictive [BSD-3 license](https://github.com/stan-dev/cmdstanpy/blob/develop/LICENSE.md)

Documentation for the latest release is at https://mc-stan.org/cmdstanpy
and documentation for older versions can be found on https://cmdstanpy.readthedocs.io/.