CompatHelper: bump compat for Turing to 0.40, (keep existing compat)

[github-actions]

This pull request changes the compat entry for the Turing package from 0.20 - 0.39 to 0.20 - 0.39, 0.40.
This keeps the compat entries for earlier versions.

Note: I have not tested your package with this new compat entry.
It is your responsibility to make sure that your package tests pass before you merge this pull request.

[github-actions]

TuringGLM.jl documentation for PR #173 is available at:
https://TuringLang.github.io/TuringGLM.jl/previews/PR173/

[coveralls]

Pull Request Test Coverage Report for Build 16993043643

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Details

• 0 of 0 changed or added relevant lines in 0 files are covered.
• No unchanged relevant lines lost coverage.
• Overall coverage remained the same at 70.566%

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Totals
Change from base Build 16908503471:	0.0%
Covered Lines:	187
Relevant Lines:	265

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💛 - Coveralls

[storopoli]

This is really strange, why the LTS CI is giving wrong estimates when compared to the latest Julia?
@penelopeysm can you please take a look when you get the chance?

[storopoli]

Changed the compat bound to 0.20-0.40

[penelopeysm]

why the LTS CI is giving wrong estimates when compared to the latest Julia?

It's only off by a bit, so I think it's just an unfortunate seed. In general the PRNGs in Random.default_rng() are not guaranteed to give reproducible results across Julia versions (https://docs.julialang.org/en/v1/stdlib/Random/#Reproducibility), so for example:

julia> # 1.11
       randn(Xoshiro(468), 10000)[4502]
-3.6670359282971967

julia> # 1.10
       randn(Xoshiro(468), 10000)[4502]
-3.6557814790472025

The solution to this is to use StableRNGs.

[storopoli]

ACK 2e8a78b
Thanks! Too bad one seed had to be incremented

[storopoli]

Feel free to merge once CI passes, and tag a new version.