documentation for CmdStanPy optimization
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Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 45/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- numpy, pandas, python
- Domain
- api, documentation
Research direction
Start with the User's Guide section titled “Maximum Likelihood Estimation” and the API documentation for CmdStanMLE. Review each checklist item against the current wording and examples, including the NumPy and pandas result descriptions. Done means the guide and API docs explain the requested scales, estimates, suffixes, output steps, and rendered quotes.
Written by the indexing model from the issue text.
Description
Summary:
A bunch of suggestions for making the optimization doc easier to understand.
-
User's Guide: Maximum Likelihood Estimation
- we should indicate that (a) we get penalized MLEs as well as MLE in the title and description, (b) explain that this means turning the Jacobian adjustment off, and (c) that with Jacobian adjustment off we do not get the posterior mode, aka the max a posteriori (MAP) estimate a Bayesian is likely to expect
- we should indicate
_npsuffix is NumPy ndarray and_pdis a pandas data frame - we should break the output for running
optimize, printing the_dict, printing the_np, and printing the_pdvalues - we should indicate the result is on the constrained scale
-
API Docs
- indicate that results are on the constrained scale in
CmdStanMLE - replace forward apostrophes with back ticks to get rendered quotes (both here and above)
- indicate that results are on the constrained scale in
Current Version:
1.0.7
- Dominant language
- Python
- Stars
- 198
- Forks
- 81
- PR merge metrics
- No merged PRs in 30d
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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