Convert ArviZ.jl into a meta-package (#297)

* Remove unneeded `@doc` * Remove all unneeded dependencies * Remove ArviZStats * Remove R installation from CI * Add PosteriorStats and Reexport as deps * Reexport component packages * Fix broken links * Update stats API docs * Add back PSIS compat bound * Remove unused test dependencies * Remove test Project.toml * Add missing SampleChainsDynamicHMC compat * Remove docs compat entry * Add component packages as docs deps This is needed so that extensions can load the package with `using SomePackage` * Correctly set up to build docstrings in extensions * Build docs strictly * Document meta-ness of package * Build docs nightly * Increment minor version number * Add StatsBase for doctests * Disable caching * Try building docs directly * Copy from InferenceObjects's docs build [no ci] * Revert "Copy from InferenceObjects's docs build [no ci]" This reverts commit cafadf8. * Revert "Revert "Copy from InferenceObjects's docs build"" This reverts commit 0cdfd99. * Revert "Disable caching" This reverts commit 6ccbadf.
arviz-devs · Aug 8, 2023 · 5d5a2d2 · 5d5a2d2 · sethaxen · Aug 8, 2023
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diff --git a/.github/workflows/CompatHelper.yml b/.github/workflows/CompatHelper.yml
@@ -10,11 +10,7 @@ jobs:
       - name: Pkg.add("CompatHelper")
         run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
       - name: CompatHelper.main()
-        run: |
-          using CompatHelper
-          subdirs = ["", "test", "docs"]
-          CompatHelper.main(; subdirs)
-        shell: julia --color=yes {0}
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          COMPATHELPER_PRIV: ${{ secrets.COMPATHELPER_KEY }}
+          COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }}
+        run: julia --color=yes -e 'using CompatHelper; CompatHelper.main()'
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -18,19 +18,6 @@ jobs:
         os: [ubuntu-latest]
     steps:
       - uses: actions/checkout@v2
-      - uses: r-lib/actions/setup-r@v2
-        if: matrix.os == 'ubuntu-latest'
-      - name: Set R lib path for RCall.jl
-        if: matrix.os == 'ubuntu-latest'
-        run: echo "LD_LIBRARY_PATH=$(R RHOME)/lib" >> $GITHUB_ENV
-      - name: Install R packages
-        if: matrix.os == 'ubuntu-latest'
-        run: |
-          install.packages("remotes")
-          remotes::install_github("stan-dev/loo")
-        shell: Rscript {0}
-        env:
-          GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       - uses: julia-actions/setup-julia@v1
         with:
           version: ${{ matrix.julia-version }}

diff --git a/.github/workflows/documenter.yml b/.github/workflows/documenter.yml
@@ -4,6 +4,8 @@ on:
     branches: [main]
     tags: [v*]
   pull_request:
+  schedule:
+    - cron: "0 0 * * *"
 
 jobs:
   docs:
@@ -13,8 +15,12 @@ jobs:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@v1
       - uses: julia-actions/cache@v1
-      - uses: julia-actions/julia-buildpkg@latest
+      - uses: julia-actions/julia-buildpkg@v1
+      - name: Install dependencies
+        run: julia --color=yes --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
       - uses: julia-actions/julia-docdeploy@v1
+        with:
+          install-package: false
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
diff --git a/Project.toml b/Project.toml
@@ -1,33 +1,16 @@
 name = "ArviZ"
 uuid = "131c737c-5715-5e2e-ad31-c244f01c1dc7"
 authors = ["Seth Axen <[email protected]>"]
-version = "0.9.2"
+version = "0.10.0"
 
 [deps]
-DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
-Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
-DimensionalData = "0703355e-b756-11e9-17c0-8b28908087d0"
-Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 InferenceObjects = "b5cf5a8d-e756-4ee3-b014-01d49d192c00"
-IteratorInterfaceExtensions = "82899510-4779-5014-852e-03e436cf321d"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 MCMCDiagnosticTools = "be115224-59cd-429b-ad48-344e309966f0"
-Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
-Optim = "429524aa-4258-5aef-a3af-852621145aeb"
-OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 PSIS = "ce719bf2-d5d0-4fb9-925d-10a81b42ad04"
-PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
-Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
-REPL = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+PosteriorStats = "7f36be82-ad55-44ba-a5c0-b8b5480d7aa5"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
-Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
-Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-TableTraits = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
-Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [weakdeps]
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
@@ -36,32 +19,30 @@ SampleChainsDynamicHMC = "6d9fd711-e8b2-4778-9c70-c1dfb499d4c4"
 
 [extensions]
 ArviZMCMCChainsExt = "MCMCChains"
-ArviZSampleChainsExt = "SampleChains"
 ArviZSampleChainsDynamicHMCExt = ["SampleChains", "SampleChainsDynamicHMC"]
+ArviZSampleChainsExt = "SampleChains"
 
 [compat]
-DataInterpolations = "4"
-DimensionalData = "0.23, 0.24"
-Distributions = "0.21, 0.22, 0.23, 0.24, 0.25"
-DocStringExtensions = "0.8, 0.9"
-InferenceObjects = "0.3.10"
-IteratorInterfaceExtensions = "0.1.1, 1"
-LogExpFunctions = "0.2.0, 0.3"
+InferenceObjects = "0.3.11"
 MCMCChains = "6"
 MCMCDiagnosticTools = "0.3.4"
-Optim = "1"
-OrderedCollections = "1"
+PosteriorStats = "0.1.1"
 PSIS = "0.9.1"
-PrettyTables = "2.1, 2.2"
+Reexport = "1"
 Requires = "0.5.2, 1.0"
 SampleChains = "0.5"
-Setfield = "1"
+SampleChainsDynamicHMC = "0.3"
 StatsBase = "0.32, 0.33, 0.34"
-TableTraits = "0.4, 1"
-Tables = "1"
 julia = "1.6"
 
 [extras]
+DimensionalData = "0703355e-b756-11e9-17c0-8b28908087d0"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
+OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SampleChains = "754583d1-7fc4-4dab-93b5-5eaca5c9622e"
 SampleChainsDynamicHMC = "6d9fd711-e8b2-4778-9c70-c1dfb499d4c4"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["DimensionalData", "MCMCChains", "OrderedCollections", "Random", "SampleChains", "SampleChainsDynamicHMC", "Test"]
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -9,31 +9,16 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
 EvoTrees = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5"
+InferenceObjects = "b5cf5a8d-e756-4ee3-b014-01d49d192c00"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 MCMCDiagnosticTools = "be115224-59cd-429b-ad48-344e309966f0"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJIteration = "614be32b-d00c-4edb-bd02-1eb411ab5e55"
+PSIS = "ce719bf2-d5d0-4fb9-925d-10a81b42ad04"
 PlutoStaticHTML = "359b1769-a58e-495b-9770-312e911026ad"
 PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
+PosteriorStats = "7f36be82-ad55-44ba-a5c0-b8b5480d7aa5"
 SampleChains = "754583d1-7fc4-4dab-93b5-5eaca5c9622e"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-
-[compat]
-AlgebraOfGraphics = "0.6.9"
-ArviZ = "0.9"
-ArviZExampleData = "0.1.5"
-CairoMakie = "0.8.9, 0.9, 0.10"
-DataFrames = "1"
-DimensionalData = "0.23, 0.24"
-Distributions = "0.25"
-EvoTrees = "0.14.8, 0.15"
-Documenter = "0.27"
-MCMCChains = "6.0"
-MCMCDiagnosticTools = "0.3"
-MLJBase = "0.21.6"
-MLJIteration = "0.5.1"
-PlutoStaticHTML = "4.0.5, 5, 6"
-PlutoUI = "0.7"
-SampleChains = "0.5"
-Statistics = "1"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
diff --git a/docs/make.jl b/docs/make.jl
@@ -25,30 +25,50 @@ function download_asset(remote_fn, fn=remote_fn)
     )
 end
 
+function get_extension(mod::Module, submodule::Symbol)
+    if isdefined(Base, :get_extension)
+        return Base.get_extension(mod, submodule)
+    else
+        return getproperty(mod, submodule)
+    end
+end
+
 # download arviz-devs org logo assets
 download_asset("ArviZ.png", "logo.png")
 download_asset("ArviZ_white.png", "logo-dark.png")
 download_asset("favicon.ico")
 
+InferenceObjectsMCMCDiagnosticToolsExt = get_extension(
+    InferenceObjects, :InferenceObjectsMCMCDiagnosticToolsExt
+)
+InferenceObjectsPosteriorStatsExt = get_extension(
+    InferenceObjects, :InferenceObjectsPosteriorStatsExt
+)
+
+for subpkg in (InferenceObjects, MCMCDiagnosticTools, PosteriorStats, PSIS)
+    DocMeta.setdocmeta!(subpkg, :DocTestSetup, :(using $(Symbol(subpkg))))
+end
 DocMeta.setdocmeta!(
-    ArviZ.MCMCDiagnosticTools, :DocTestSetup, :(using ArviZ.MCMCDiagnosticTools);
+    InferenceObjectsMCMCDiagnosticToolsExt, :DocTestSetup, :(using MCMCDiagnosticTools)
 )
-DocMeta.setdocmeta!(ArviZ.InferenceObjects, :DocTestSetup, :(using ArviZ.InferenceObjects);)
+DocMeta.setdocmeta!(
+    InferenceObjectsPosteriorStatsExt, :DocTestSetup, :(using PosteriorStats)
+)
+
+modules = [
+    ArviZ,
+    InferenceObjects,
+    InferenceObjectsMCMCDiagnosticToolsExt,
+    InferenceObjectsPosteriorStatsExt,
+    MCMCDiagnosticTools,
+    PosteriorStats,
+    PSIS,
+]
 
 doctestfilters = [
     r"\s+\"created_at\" => .*",  # ignore timestamps in doctests
 ]
 
-modules = [ArviZ, InferenceObjects, MCMCDiagnosticTools, PSIS]
-if isdefined(Base, :get_extension)
-    # using Requires, these docstrings are automatically loaded, but as an extension we need
-    # to manually specify the module
-    push!(
-        modules,
-        Base.get_extension(InferenceObjects, :InferenceObjectsMCMCDiagnosticToolsExt),
-    )
-end
-
 makedocs(;
     modules,
     sitename="ArviZ.jl",
@@ -79,6 +99,7 @@ makedocs(;
     doctestfilters,
     linkcheck=true,
     analytics="G-W1G68W77YV",
+    strict=Documenter.except(:footnote, :missing_docs),
 )
 
 deploydocs(; repo="github.com/arviz-devs/ArviZ.jl.git", devbranch="main", push_preview=true)
diff --git a/docs/src/api/stats.md b/docs/src/api/stats.md
@@ -8,6 +8,10 @@ Pages = ["stats.md"]
 
 ```@docs
 SummaryStats
+default_summary_stats
+default_stats
+default_diagnostics
+summarize
 summarystats
 ```
 
@@ -22,9 +26,11 @@ r2_score
 ## Pareto-smoothed importance sampling
 
 ```@docs
-PSIS.PSISResult
-PSIS.psis
-PSIS.psis!
+PSISResult
+ess_is
+PSISPlots.paretoshapeplot
+psis
+psis!
 ```
 
 ## LOO and WAIC
@@ -61,8 +67,8 @@ Stacking
 loo_pit
 ```
 
-### Utilities
+## Utilities
 
 ```@docs
-ArviZStats.smooth_data
+PosteriorStats.smooth_data
 ```
diff --git a/docs/src/index.md b/docs/src/index.md
@@ -1,8 +1,20 @@
 # [ArviZ.jl: Exploratory analysis of Bayesian models in Julia](@id arvizjl)
 
-ArviZ.jl is a Julia package for exploratory analysis of Bayesian models.
+ArviZ.jl is a Julia meta-package for exploratory analysis of Bayesian models.
 It is part of the [ArviZ project](https://www.arviz.org/), which also includes a related [Python package](https://python.arviz.org/).
 
+ArviZ consists of and re-exports the following subpackages, along with extensions integrating them with InferenceObjects:
+- [InferenceObjects.jl](https://julia.arviz.org/InferenceObjects): a base package implementing the [`InferenceData`](@ref) type with utilities for building, saving, and working with it
+- [MCMCDiagnosticTools.jl](https://julia.arviz.org/MCMCDiagnosticTools): diagnostics for Markov Chain Monte Carlo methods
+- [PSIS.jl](https://julia.arviz.org/PSIS): Pareto-smoothed importance sampling
+- [PosteriorStats.jl](https://julia.arviz.org/PosteriorStats): common statistical analyses for the Bayesian workflow
+
+Additional functionality can be loaded with the following packages:
+- [ArviZExampleData.jl](https://julia.arviz.org/ArviZExampleData): example `InferenceData` objects, useful for demonstration and testing
+- [ArviZPythonPlots.jl](https://julia.arviz.org/ArviZPythonPlots): Python ArviZ's library of plotting functions for Julia types
+
+See the navigation bar for more useful packages.
+
 ## [Installation](@id installation)
 
 From the Julia REPL, type `]` to enter the Pkg REPL mode and run
@@ -17,4 +29,4 @@ See the [API Overview](@ref api) for description of functions.
 
 ## [Extending ArviZ.jl](@id extendingarviz)
 
-To use a custom data type with ArviZ.jl, simply overload [`convert_to_inference_data`](@ref) to convert your input(s) to an [`InferenceData`](@ref).
+To use a custom data type with ArviZ.jl, simply overload [`InferenceObjects.convert_to_inference_data`](@ref) to convert your input(s) to an [`InferenceObjects.InferenceData`](@ref).
diff --git a/docs/src/working_with_inference_data.md b/docs/src/working_with_inference_data.md
@@ -15,7 +15,7 @@ idata = load_example_data("centered_eight")
 ```
 
 !!! info
-	  `Dataset`s are [`DimensionalData.AbstractDimStack`](https://rafaqz.github.io/DimensionalData.jl/stable/api/#DimensionalData.AbstractDimStack)s and can be used identically.
+	  `Dataset`s are [`DimensionalData.AbstractDimStack`](https://rafaqz.github.io/DimensionalData.jl/stable/reference/#DimensionalData.AbstractDimStack)s and can be used identically.
 	  The variables a `Dataset` contains are called "layers", and dimensions of the same name that appear in more than one layer within a `Dataset` must have the same indices.
 
 `InferenceData` behaves like a `NamedTuple` and can be used similarly.
@@ -121,7 +121,7 @@ Let’s keep only chain 0 here.
 For the subset to take effect on all relevant `InferenceData` groups -- `posterior`, `sample_stats`, `log_likelihood`, and `posterior_predictive` -- we will index `InferenceData` instead of `Dataset`.
 
 Here we use DimensionalData's `At` selector.
-Its [other selectors](https://rafaqz.github.io/DimensionalData.jl/stable/api/#Selectors) are also supported.
+Its [other selectors](https://rafaqz.github.io/DimensionalData.jl/stable/reference/#selectors) are also supported.
 
 ```@example wwid
 idata[chain=At(0)]
@@ -179,7 +179,7 @@ dropdims(mean(post; dims=(:chain, :draw)); dims=(:chain, :draw))
 
 ## Renaming a dimension
 
-We can rename a dimension in a `Dataset` using DimensionalData's [`set`](https://rafaqz.github.io/DimensionalData.jl/stable/api/#DimensionalData.Dimensions.LookupArrays.set) method:
+We can rename a dimension in a `Dataset` using DimensionalData's [`set`](https://rafaqz.github.io/DimensionalData.jl/stable/reference/#DimensionalData.Dimensions.LookupArrays.set) method:
 
 ```@example wwid
 theta_bis = set(post.theta; school=:school_bis)