Support for `emp_start` to apply to other non-spatial covariance structures
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 45/100
Research direction
Start at the existing emp_start entry point and trace how it derives starting values for us covariance estimation. Then inspect the other non-spatial covariance structures and determine the reasonable empirical-covariance starting values needed for each. Done means emp_start provides useful starts for those structures before the general optimization loop.
Written by the indexing model from the issue text.
Description
Summary
The current function emp_start works well to speed up us covariance estimation. I would like to extend this to the other non-spatial covariance structures so that we start with a close approximation before going into a general optimization loop.
Additional Information
Essentially, the existing emp_start need only use an additional helper function that calculates a reasonable starting value for non-us covariance structures from the empirical covariance matrix.
- Dominant language
- R
- Stars
- 162
- Forks
- 29
- Avg merge
- 1d 9h
- Merged PRs (30d)
- 2
Getting set up
- No Dockerfile or Docker Compose file
- Has a pull request template
- Read the contributing 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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