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attempt to find better starting values for GLMM when defaults aren't positive semidefinite (#796)
* re-initialize to small values * simplify example * BlueStyle * use StableRNG * test * NEWS + version bump * add zenodo JSON
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.zenodo.json

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{
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"access_right": "open",
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"license": "MIT",
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"title": "MixedModels.jl",
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"description": "A Julia package for fitting (statistical) mixed-effects models",
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"upload_type": "software",
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"creators": [
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{
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"affiliation": "Beacon Biosignals",
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"name": "Alday, Phillip M.",
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"orcid": "0000-0002-9984-5745"
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},
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{
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"affiliation": "University of Wisconsin - Madison",
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"name": "Bates, Douglas",
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"orcid": "0000-0001-8316-9503"
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}
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]
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}

NEWS.md

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MixedModels v4.28.0 Release Notes
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==============================
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- `GeneralizedLinearMixedModel` now attempts to fall back to very constrained variance values when the default initial values result in a non positive semidefinite covariance matrix. [#792]
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MixedModels v4.27.1 Release Notes
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==============================
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- `profile` now includes a `finally` block to restore the original model even if an error occurs before profiling is complete [#795]
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[#783]: https://github.com/JuliaStats/MixedModels.jl/issues/783
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[#785]: https://github.com/JuliaStats/MixedModels.jl/issues/785
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[#791]: https://github.com/JuliaStats/MixedModels.jl/issues/791
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[#792]: https://github.com/JuliaStats/MixedModels.jl/issues/792
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[#795]: https://github.com/JuliaStats/MixedModels.jl/issues/795

Project.toml

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name = "MixedModels"
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uuid = "ff71e718-51f3-5ec2-a782-8ffcbfa3c316"
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author = ["Phillip Alday <[email protected]>", "Douglas Bates <[email protected]>", "Jose Bayoan Santiago Calderon <[email protected]>"]
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version = "4.27.1"
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author = ["Phillip Alday <[email protected]>", "Douglas Bates <[email protected]>"]
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version = "4.28.0"
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Arrow = "69666777-d1a9-59fb-9406-91d4454c9d45"

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