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Merge branch 'main' of github.com:JuliaStats/MixedModels.jl into glmm_dispersion_deviance
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‎.github/workflows/MKL.yml‎

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name: current release
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‎.github/workflows/docs-cleanup.yml‎

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# remove PR previews once they're merged
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# <https://juliadocs.github.io/Documenter.jl/dev/man/hosting/#gh-pages-Branch>
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name: Doc Preview Cleanup
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on:
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pull_request:
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types: [closed]
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runs-on: ubuntu-latest
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uses: actions/checkout@v2
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ref: gh-pages
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- name: Delete preview and history + push changes
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run: |
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if [ -d "previews/PR$PRNUM" ]; then
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git config user.name "Documenter.jl"
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git config user.email "[email protected]"
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git rm -rf "previews/PR$PRNUM"
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git commit -m "delete preview"
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git branch gh-pages-new $(echo "delete history" | git commit-tree HEAD^{tree})
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git push --force origin gh-pages-new:gh-pages
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‎.github/workflows/documenter.yml‎

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‎.github/workflows/minimum.yml‎

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name: "minimum compat"
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cancel-in-progress: true
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on:
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branches:
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- main
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paths-ignore:
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- 'LICENSE.md'
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- 'README.md'
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pull_request:
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- main
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paths-ignore:
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- 'LICENSE.md'
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- 'README.md'
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ci:
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strategy:
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julia-version: [1.8]
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os: [ubuntu-20.04, macos-11, windows-2019]
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steps:
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- uses: actions/checkout@v2
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- uses: julia-actions/cache@v1
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cache-compiled: "true"
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- uses: julia-actions/julia-buildpkg@v1
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- uses: julia-actions/julia-runtest@v1
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- uses: julia-actions/julia-processcoverage@v1
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‎.github/workflows/style.yml‎

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‎.gitignore‎

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‎NEWS.md‎

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MixedModels v4.19.0 Release Notes
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==============================
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* New method `StatsAPI.coefnames(::ReMat)` returns the coefficient names associated with each grouping factor. [#709]
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MixedModels v4.18.0 Release Notes
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==============================
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* More user-friendly error messages when a formula contains variables not in the data. [#707]
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MixedModels v4.17.0 Release Notes
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==============================
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* **EXPERIMENTAL** New kwarg `amalgamate` can be used to disable amalgation of random effects terms sharing a single grouping variable. Generally, `amalgamate=false` will result in a slower fit, but may improve convergence in some pathological cases. Note that this feature is experimental and changes to it are **not** considered breakings. [#673]
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* More informative error messages when passing a `Distribution` or `Link` type instead of the desired instance. [#698]
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* More informative error message on the intentional decision not to define methods for the coefficient of determination. [#698]
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* **EXPERIMENTAL** Return `finitial` when PIRLS drifts into a portion of the parameter space that yields a (numerically) invalid covariance matrix. This recovery strategy may be removed in a future release. [#616]
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MixedModels v4.16.0 Release Notes
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==============================
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* Support for check tolerances in deserialization. [#703]
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MixedModels v4.15.0 Release Notes
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==============================
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* Support for different optimization criteria during the bootstrap. [#694]
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* Support for combining bootstrap results with `vcat`. [#694]
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* Support for saving and restoring bootstrap replicates with `savereplicates` and `restorereplicates`. [#694]
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MixedModels v4.14.0 Release Notes
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==============================
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* New function `profile` for computing likelihood profiles for `LinearMixedModel`. The resultant `MixedModelProfile` can be then be used for computing confidence intervals with `confint`. Note that this API is still somewhat experimental and as such the internal storage details of `MixedModelProfile` may change in a future release without being considered breaking. [#639]
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* A `confint(::LinearMixedModel)` method has been defined that returns Wald confidence intervals based on the z-statistic, i.e. treating the denominator degrees of freedom as infinite. [#639]
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MixedModels v4.13.0 Release Notes
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==============================
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* `raneftables` returns a `NamedTuple` where the names are the grouping factor names and the values are some `Tables.jl`-compatible type. This type has been changed to a `Table` from `TypedTables.jl`. [#682]
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MixedModels v4.12.1 Release Notes
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==============================
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* Precompilation is now handled with `PrecompileTools` instead of `SnoopPrecompile`. [#681]
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* An unnecessary explicit `Vararg` in an internal method has been removed. This removal eliminates a compiler warning about the deprecated `Vararg` pattern. [#680]
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MixedModels v4.12.0 Release Notes
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==============================
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* The pirated method `Base.:/(a::AbstractTerm, b::AbstractTerm)` is no longer defined. This does not impact the use of `/` as a nesting term in `@formula` within MixedModels, only the programmatic runtime construction of formula, e.g. `term(:a) / term(:b)`. If you require `Base.:/`, then [`RegressionFormulae.jl`](https://github.com/kleinschmidt/RegressionFormulae.jl) provides this method. (Avoiding method redefinition when using `RegressionFormulae.jl` is the motivating reason for this change.) [#677]
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MixedModels v4.11.0 Release Notes
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==============================
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* `raneftables` returns a `NamedTuple` where the names are the grouping factor names and the values are some `Tables.jl`-compatible type. Currently this type is a `DictTable` from `TypedTables.jl`. [#634]
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MixedModels v4.10.0 Release Notes
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==============================
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* Rank deficiency in prediction is now supported, both when the original model was fit to rank-deficient data and when the new data are rank deficient. The behavior is consistent but may be surprising when both old and new data are rank deficient. See the `predict` docstring for an example. [#676]
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* Multithreading in `parametricbootstrap` with `use_threads` is now deprecated and a noop. With improvements in BLAS threading, multithreading at the Julia level did not help performance and sometimes hurt it. [#674]
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MixedModels v4.9.0 Release Notes
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==============================
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* Support `StatsModels` 0.7, drop support for `StatsModels` 0.6. [#664]
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* Revise code in benchmarks to work with recent Julia and PkgBenchmark.jl [#667]
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* Julia minimum compat version raised to 1.8 because of BSplineKit [#665]
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MixedModels v4.8.2 Release Notes
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==============================
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* Use `SnoopPrecompile` for better precompilation performance. This can dramatically increase TTFX, especially on Julia 1.9. [#663]
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MixedModels v4.8.1 Release Notes
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==============================
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* Don't fit a GLM internally during construction of GLMM when the fixed effects are empty (better compatibility with
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`dropcollinear` kwarg in newer GLM.jl) [#657]
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MixedModels v4.8.0 Release Notes
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==============================
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* Allow predicting from a single observation, as long as `Grouping()` is used for the grouping variables. The simplified implementation of `Grouping()` also removes several now unnecessary `StatsModels` methods that should not have been called directly by the user. [#653]
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MixedModels v4.7.3 Release Notes
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==============================
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* More informative error message for formulae lacking random effects [#651]
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MixedModels v4.7.2 Release Notes
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==============================
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* Replace separate calls to `copyto!` and `scaleinflate!` in `updateL!` with `copyscaleinflate!` [#648]
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MixedModels v4.7.1 Release Notes
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==============================
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* Avoid repeating initial objective evaluation in `fit!` method for `LinearMixedModel`
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* Ensure that the number of function evaluations from NLopt corresponds to `length(m.optsum.fitlog) when `isone(thin)`. [#637]
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MixedModels v4.7.0 Release Notes
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==============================
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* Relax type restriction for filename in `saveoptsum` and `restoreoptsum!`. Users can now pass any type with an appropriate `open` method, e.g. `<:AbstractPath`. [#628]
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MixedModels v4.6.5 Release Notes
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========================
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* Attempt recovery when the initial parameter values lead to an invalid covariance matrix by rescaling [#615]
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* Return `finitial` when the optimizer drifts into a portion of the parameter space that yields a (numerically) invalid covariance matrix [#615]
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MixedModels v4.6.4 Release Notes
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========================
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* Support transformed responses in `predict` [#614]
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* Simplify printing of BLAS configuration in tests. [#597]
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MixedModels v4.6.3 Release Notes
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========================
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* Add precompile statements to speed up first `LinearMixedModel` and Bernoulli `GeneralizedLinearModel` fit [#608]
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* An `AbstractReMat` type has now been introduced to support [#380] work on constrained
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random-effects structures and random-effects structures appropriate for applications
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in GLM-based decovolution as used in fMRI and EEG (see e.g. [unfold.jl](https://github.com/unfoldtoolbox/unfold.jl).)
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in GLM-based deconvolution as used in fMRI and EEG (see e.g. [unfold.jl](https://github.com/unfoldtoolbox/unfold.jl).)
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* Similarly, a constructor for `FeMat{::SparseMatrixCSC,S}` has been introduced [#309].
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deficiency may be extended to this constructor as well.
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[#577]: https://github.com/JuliaStats/MixedModels.jl/issues/577
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[#578]: https://github.com/JuliaStats/MixedModels.jl/issues/578
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[#588]: https://github.com/JuliaStats/MixedModels.jl/issues/588
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[#597]: https://github.com/JuliaStats/MixedModels.jl/issues/597
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[#598]: https://github.com/JuliaStats/MixedModels.jl/issues/598
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[#603]: https://github.com/JuliaStats/MixedModels.jl/issues/603
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[#604]: https://github.com/JuliaStats/MixedModels.jl/issues/604
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[#608]: https://github.com/JuliaStats/MixedModels.jl/issues/608
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[#614]: https://github.com/JuliaStats/MixedModels.jl/issues/614
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[#615]: https://github.com/JuliaStats/MixedModels.jl/issues/615
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[#616]: https://github.com/JuliaStats/MixedModels.jl/issues/616
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[#628]: https://github.com/JuliaStats/MixedModels.jl/issues/628
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[#634]: https://github.com/JuliaStats/MixedModels.jl/issues/634
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[#637]: https://github.com/JuliaStats/MixedModels.jl/issues/637
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[#639]: https://github.com/JuliaStats/MixedModels.jl/issues/639
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[#648]: https://github.com/JuliaStats/MixedModels.jl/issues/648
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[#651]: https://github.com/JuliaStats/MixedModels.jl/issues/651
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[#653]: https://github.com/JuliaStats/MixedModels.jl/issues/653
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[#657]: https://github.com/JuliaStats/MixedModels.jl/issues/657
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[#663]: https://github.com/JuliaStats/MixedModels.jl/issues/663
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[#664]: https://github.com/JuliaStats/MixedModels.jl/issues/664
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[#665]: https://github.com/JuliaStats/MixedModels.jl/issues/665
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[#667]: https://github.com/JuliaStats/MixedModels.jl/issues/667
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[#673]: https://github.com/JuliaStats/MixedModels.jl/issues/673
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[#674]: https://github.com/JuliaStats/MixedModels.jl/issues/674
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[#676]: https://github.com/JuliaStats/MixedModels.jl/issues/676
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[#677]: https://github.com/JuliaStats/MixedModels.jl/issues/677
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[#680]: https://github.com/JuliaStats/MixedModels.jl/issues/680
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[#681]: https://github.com/JuliaStats/MixedModels.jl/issues/681
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[#682]: https://github.com/JuliaStats/MixedModels.jl/issues/682
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[#694]: https://github.com/JuliaStats/MixedModels.jl/issues/694
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[#698]: https://github.com/JuliaStats/MixedModels.jl/issues/698
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[#703]: https://github.com/JuliaStats/MixedModels.jl/issues/703
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[#707]: https://github.com/JuliaStats/MixedModels.jl/issues/707
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[#709]: https://github.com/JuliaStats/MixedModels.jl/issues/709

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