Use interlinks and citations in docs

Project.toml

@@ -1,7 +1,7 @@
 name = "Pathfinder"
 uuid = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454"
 authors = ["Seth Axen <[email protected]> and contributors"]
-version = "0.9.6"
+version = "0.9.7"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

docs/Project.toml

@@ -2,6 +2,8 @@
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 AdvancedHMC = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DocumenterCitations = "daee34ce-89f3-4625-b898-19384cb65244"
+DocumenterInterLinks = "d12716ef-a0f6-4df4-a9f1-a5a34e75c656"
 DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -20,6 +22,8 @@ Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 ADTypes = "0.2, 1"
 AdvancedHMC = "0.6"
 Documenter = "1"
+DocumenterCitations = "1.2"
+DocumenterInterLinks = "1"
 DynamicHMC = "3.4.0"
 ForwardDiff = "0.10.19"
 LogDensityProblems = "2.1.0"

docs/inventories/Distributions.toml

@@ -0,0 +1,11 @@
+# DocInventory version 1
+project = "Distributions.jl"
+version = "0.25.113"
+
+# Filtered to just the types we link to
+[[jl.type]]
+name = "Distributions.MixtureModel"
+uri = "mixture/#$"
+[[jl.type]]
+name = "Distributions.MvNormal"
+uri = "multivariate/#$"

docs/inventories/DynamicHMC.toml

@@ -0,0 +1,13 @@
+# DocInventory version 1
+project = "DynamicHMC.jl"
+version = "3.4.7"
+
+# Filtered to just the types we link to
+[[std.doc]]
+dispname = "A worked example"
+name = "worked_example"
+uri = "worked_example/"
+
+[[jl.type]]
+name = "DynamicHMC.GaussianKineticEnergy"
+uri = "interface/#$"

docs/inventories/Transducers.toml

@@ -0,0 +1,11 @@
+# DocInventory version 1
+project = "Transducers.jl"
+version = "0.4.84"
+
+# Filtered to just the types we link to
+[[jl.type]]
+name = "Transducers.PreferParallel"
+uri = "reference/manual/#$"
+[[jl.type]]
+name = "Transducers.SequentialEx"
+uri = "reference/manual/#$"

docs/make.jl

@@ -1,8 +1,41 @@
 using Pathfinder
 using Documenter
+using DocumenterCitations
+using DocumenterInterLinks
 
 DocMeta.setdocmeta!(Pathfinder, :DocTestSetup, :(using Pathfinder); recursive=true)
 
+bib = CitationBibliography(joinpath(@__DIR__, "src", "references.bib"); style=:numeric)
+
+links = InterLinks(
+    "AdvancedHMC" => "https://turinglang.org/AdvancedHMC.jl/stable/",
+    "ADTypes" => "https://sciml.github.io/ADTypes.jl/stable/",
+    "Distributions" => (
+        "https://juliastats.org/Distributions.jl/stable/",
+        "https://juliastats.org/Distributions.jl/dev/objects.inv",
+        joinpath(@__DIR__, "inventories", "Distributions.toml"),
+    ),
+    "DynamicHMC" => (
+        "https://www.tamaspapp.eu/DynamicHMC.jl/stable/",
+        "https://www.tamaspapp.eu/DynamicHMC.jl/dev/objects.inv",
+        joinpath(@__DIR__, "inventories", "DynamicHMC.toml"),
+    ),
+    "DynamicPPL" => "https://turinglang.org/DynamicPPL.jl/stable/",
+    "LogDensityProblems" => "https://www.tamaspapp.eu/LogDensityProblems.jl/stable/",
+    "MCMCChains" => (
+        "https://turinglang.org/MCMCChains.jl/stable/",
+        "https://turinglang.org/MCMCChains.jl/dev/objects.inv",
+    ),
+    "Optim" => "https://julianlsolvers.github.io/Optim.jl/stable/",
+    "Optimization" => "https://docs.sciml.ai/Optimization/stable/",
+    "PSIS" => "https://julia.arviz.org/PSIS/stable/",
+    "Transducers" => (
+        "https://juliafolds2.github.io/Transducers.jl/stable/",  # not built for a while
+        "https://juliafolds2.github.io/Transducers.jl/dev/objects.inv",
+        joinpath(@__DIR__, "inventories", "Transducers.toml"),
+    ),
+)
+
 makedocs(;
     modules=[Pathfinder],
     authors="Seth Axen <[email protected]> and contributors",
@@ -11,7 +44,7 @@ makedocs(;
     format=Documenter.HTML(;
         prettyurls=get(ENV, "CI", "false") == "true",
         canonical="https://mlcolab.github.io/Pathfinder.jl",
-        assets=String[],
+        assets=String["assets/citations.css"],
     ),
     pages=[
         "Home" => "index.md",
@@ -21,7 +54,9 @@ makedocs(;
             "Initializing HMC" => "examples/initializing-hmc.md",
             "Turing usage" => "examples/turing.md",
         ],
+        "References" => "references.md",
     ],
+    plugins=[bib, links],
 )
 
 if get(ENV, "DEPLOY_DOCS", "true") == "true"

docs/src/assets/citations.css

@@ -0,0 +1,29 @@
+/* Adapted from DocumenterCitations.jl's docs */
+.citation dl {
+    display: grid;
+    grid-template-columns: max-content auto;
+}
+
+.citation dt {
+    grid-column-start: 1;
+}
+
+.citation dd {
+    grid-column-start: 2;
+    margin-bottom: 0.75em;
+}
+
+.citation ul {
+    padding: 0 0 2.25em 0;
+    margin: 0;
+    list-style: none !important;
+}
+
+.citation ul li {
+    text-indent: -2.25em;
+    margin: 0.33em 0.5em 0.5em 2.25em;
+}
+
+.citation ol li {
+    padding-left: 0.75em;
+}

docs/src/examples/initializing-hmc.md

@@ -7,7 +7,7 @@ When using MCMC to draw samples from some target distribution, there is often a
 2. adapt any tunable parameters of the MCMC sampler (optional)
 
 While (1) often happens fairly quickly, (2) usually requires a lengthy exploration of the typical set to iteratively adapt parameters suitable for further exploration.
-An example is the widely used windowed adaptation scheme of Hamiltonian Monte Carlo (HMC) in Stan, where a step size and positive definite metric (aka mass matrix) are adapted.[^1]
+An example is the widely used windowed adaptation scheme of Hamiltonian Monte Carlo (HMC) in Stan [StanHMCParameters](@citep), where a step size and positive definite metric (aka mass matrix) are adapted.
 For posteriors with complex geometry, the adaptation phase can require many evaluations of the gradient of the log density function of the target distribution.
 
 Pathfinder can be used to initialize MCMC, and in particular HMC, in 3 ways:
@@ -82,7 +82,7 @@ nothing # hide
 
 ## DynamicHMC.jl
 
-To use DynamicHMC, we first need to transform our model to an unconstrained space using [TransformVariables.jl](https://tamaspapp.eu/TransformVariables.jl/stable/) and wrap it in a type that implements the [LogDensityProblems.jl](https://github.com/tpapp/LogDensityProblems.jl) interface:
+To use DynamicHMC, we first need to transform our model to an unconstrained space using [TransformVariables.jl](https://tamaspapp.eu/TransformVariables.jl/stable/) and wrap it in a type that implements the [LogDensityProblems interface](@extref LogDensityProblems log-density-api) (see [DynamicHMC's worked example](@extref DynamicHMC worked_example)):
 
 ```@example 1
 using DynamicHMC, ForwardDiff, LogDensityProblems, LogDensityProblemsAD, TransformVariables
@@ -123,7 +123,7 @@ result_dhmc1 = mcmc_with_warmup(
 
 ### Initializing metric adaptation from Pathfinder's estimate
 
-To start with Pathfinder's inverse metric estimate, we just need to initialize a `GaussianKineticEnergy` object with it as input: 
+To start with Pathfinder's inverse metric estimate, we just need to initialize a [`DynamicHMC.GaussianKineticEnergy`](@extref) object with it as input: 
 
 ```@example 1
 result_dhmc2 = mcmc_with_warmup(
@@ -212,7 +212,7 @@ samples_ahmc2, stats_ahmc2 = sample(
 
 ### Use Pathfinder's metric estimate for sampling
 
-To use Pathfinder's metric with no metric adaptation, we need to use Pathfinder's own `RankUpdateEuclideanMetric` type, which just wraps our inverse metric estimate for use with AdvancedHMC:
+To use Pathfinder's metric with no metric adaptation, we need to use Pathfinder's own [`Pathfinder.RankUpdateEuclideanMetric`](@ref) type, which just wraps our inverse metric estimate for use with AdvancedHMC:
 
 ```@example 1
 nadapts = 75
@@ -233,5 +233,3 @@ samples_ahmc3, stats_ahmc3 = sample(
     progress=false,
 )
 ```
-
-[^1]: https://mc-stan.org/docs/reference-manual/hmc-algorithm-parameters.html

docs/src/examples/quickstart.md

@@ -121,7 +121,7 @@ Now we will run Pathfinder on the following banana-shaped distribution with dens
 \pi(x_1, x_2) = e^{-x_1^2 / 2} e^{-5 (x_2 - x_1^2)^2 / 2}.
 ```
 
-Pathfinder can also take any object that implements the [LogDensityProblems](https://www.tamaspapp.eu/LogDensityProblems.jl) interface.
+Pathfinder can also take any object that implements the [LogDensityProblems interface](@extref LogDensityProblems log-density-api) interface.
 This can also be used to manually define the gradient of the log-density function.
 
 First we define the log density problem:
@@ -185,9 +185,9 @@ result = multipathfinder(prob_banana, ndraws; nruns=20, init_scale=10)
 `result` is a [`MultiPathfinderResult`](@ref).
 See its docstring for a description of its fields.
 
-`result.fit_distribution` is a uniformly-weighted `Distributions.MixtureModel`.
+`result.fit_distribution` is a uniformly-weighted [`Distributions.MixtureModel`](@extref).
 Each component is the result of a single Pathfinder run.
-It's possible that some runs fit the target distribution much better than others, so instead of just drawing samples from `result.fit_distribution`, `multipathfinder` draws many samples from each component and then uses Pareto-smoothed importance resampling (using [PSIS.jl](https://psis.julia.arviz.org/stable/)) from these draws to better target `prob_banana`.
+It's possible that some runs fit the target distribution much better than others, so instead of just drawing samples from `result.fit_distribution`, `multipathfinder` draws many samples from each component and then uses Pareto-smoothed importance resampling (using [PSIS.jl](@extref PSIS PSIS)) from these draws to better target `prob_banana`.
 
 The Pareto shape diagnostic informs us on the quality of these draws.
 Here the Pareto shape ``k`` diagnostic is bad (``k > 0.7``), which tells us that these draws are unsuitable for computing posterior estimates, so we should definitely run MCMC to get better draws.
@@ -238,7 +238,7 @@ nothing # hide
 
 First, let's fit this posterior with single-path Pathfinder.
 For high-dimensional problems, it's better to use reverse-mode automatic differentiation.
-Here, we'll use `ADTypes.AutoReverseDiff()` to specify that [ReverseDiff.jl](https://github.com/JuliaDiff/ReverseDiff.jl) should be used.
+Here, we'll use [`ADTypes.AutoReverseDiff`](@extref) to specify that [ReverseDiff.jl](https://github.com/JuliaDiff/ReverseDiff.jl) should be used.
 
 
 ```@example 1

docs/src/examples/turing.md

@@ -24,7 +24,7 @@ model = regress(collect(x), y)
 n_chains = 8
 ```
 
-For convenience, [`pathfinder`](@ref) and [`multipathfinder`](@ref) can take Turing models as inputs and produce `MCMCChains.Chains` objects as outputs.
+For convenience, [`pathfinder`](@ref) and [`multipathfinder`](@ref) can take Turing models as inputs and produce [`MCMCChains.Chains`](@extref) objects as outputs.
 
 ```@example 1
 result_single = pathfinder(model; ndraws=1_000)
@@ -36,7 +36,7 @@ result_multi = multipathfinder(model, 1_000; nruns=n_chains)
 
 Here, the Pareto shape diagnostic indicates that it is likely safe to use these draws to compute posterior estimates.
 
-When passed a `Model`, Pathfinder also gives access to the posterior draws in a familiar `MCMCChains.Chains` object.
+When passed a [`DynamicPPL.Model`](@extref), Pathfinder also gives access to the posterior draws in a familiar `Chains` object.
 
 ```@example 1
 result_multi.draws_transformed

docs/src/index.md

@@ -4,7 +4,7 @@ CurrentModule = Pathfinder
 
 # Pathfinder.jl: Parallel quasi-Newton variational inference
 
-This package implements Pathfinder, [^Zhang2021] a variational method for initializing Markov chain Monte Carlo (MCMC) methods.
+This package implements Pathfinder [ZhangPathfinder2021](@citep), a variational method for initializing Markov chain Monte Carlo (MCMC) methods.
 
 ## Single-path Pathfinder
 
@@ -45,8 +45,3 @@ Pathfinder uses several packages for extended functionality:
 - [Distributions.jl](https://juliastats.org/Distributions.jl/stable/)/[PDMats.jl](https://github.com/JuliaStats/PDMats.jl): fits can be used anywhere a `Distribution` can be used
 - [LogDensityProblems.jl](https://www.tamaspapp.eu/LogDensityProblems.jl/stable/): defining the log-density function, gradient, and Hessian
 - [ProgressLogging.jl](https://julialogging.github.io/ProgressLogging.jl/stable/): In Pluto, Juno, and VSCode, nested progress bars are shown. In the REPL, use TerminalLoggers.jl to get progress bars.
-
-[^Zhang2021]: Lu Zhang, Bob Carpenter, Andrew Gelman, Aki Vehtari (2021).
-              Pathfinder: Parallel quasi-Newton variational inference.
-              arXiv: [2108.03782](https://arxiv.org/abs/2108.03782) [stat.ML].
-              [Code](https://github.com/LuZhangstat/Pathfinder)

docs/src/references.bib

@@ -0,0 +1,34 @@
+@article{Byrd1994,
+  title   = {Representations of Quasi-{{Newton}} Matrices and Their Use in Limited Memory Methods},
+  author  = {Byrd, Richard H. and Nocedal, Jorge and Schnabel, Robert B.},
+  year    = {1994},
+  month   = jan,
+  journal = {Mathematical Programming},
+  volume  = {63},
+  number  = {1-3},
+  pages   = {129--156},
+  issn    = {0025-5610, 1436-4646},
+  doi     = {10.1007/BF01582063}
+}
+
+@misc{StanHMCParameters,
+  title   = {Stan Reference Manual: {HMC} algorithm parameters},
+  urldate = {2024-12-06},
+  url     = {https://mc-stan.org/docs/reference-manual/mcmc.html#hmc-algorithm-parameters}
+}
+
+@article{ZhangPathfinder2021,
+  title      = {Pathfinder: {{Parallel}} Quasi-{{Newton}} Variational Inference},
+  shorttitle = {Pathfinder},
+  author     = {Zhang, Lu and Carpenter, Bob and Gelman, Andrew and Vehtari, Aki},
+  year       = {2022},
+  journal    = {Journal of Machine Learning Research},
+  volume     = {23},
+  number     = {306},
+  pages      = {1--49},
+  issn       = {1533-7928},
+  urldate    = {2024-12-06},
+  url        = {http://jmlr.org/papers/v23/21-0889.html},
+  eprint     = {2108.03782},
+  eprinttype = {arXiv}
+}

docs/src/references.md

@@ -0,0 +1,4 @@
+# References
+
+```@bibliography
+```

ext/PathfinderTuringExt.jl

@@ -34,8 +34,9 @@ end
 """
     draws_to_chains(model::DynamicPPL.Model, draws) -> MCMCChains.Chains
 
-Convert a `(nparams, ndraws)` matrix of unconstrained `draws` to an `MCMCChains.Chains`
-object with corresponding constrained draws and names according to `model`.
+Convert a `(nparams, ndraws)` matrix of unconstrained `draws` to a
+[`MCMCChains.Chains`](@extref) object with corresponding constrained draws and names
+according to `model`.
 """
 function draws_to_chains(model::DynamicPPL.Model, draws::AbstractMatrix)
     varinfo = DynamicPPL.link(DynamicPPL.VarInfo(model), model)

src/integration/advancedhmc.jl

@@ -4,7 +4,8 @@ using .Random
 """
     RankUpdateEuclideanMetric{T,M} <: AdvancedHMC.AbstractMetric
 
-A Gaussian Euclidean metric whose inverse is constructed by rank-updates.
+A Gaussian Euclidean [metric](@extref AdvancedHMC Hamiltonian-mass-matrix-(metric)) whose
+inverse is constructed by rank-updates.
 
 # Constructors
 

src/inverse_hessian.jl

@@ -73,10 +73,10 @@ Compute approximate inverse Hessian initialized from `H₀` from history stored
 `history_ind` indicates the column in `S₀` and `Y₀` that was most recently added to the
 history, while `history_length` indicates the number of first columns in `S₀` and `Y₀`
 currently being used for storing history.
-`S = S₀[:, history_ind+1:history_length; 1:history_ind]` reorders the columns of `₀` so that the
-oldest is first and newest is last.
+`S = S₀[:, history_ind+1:history_length; 1:history_ind]` reorders the columns of `S₀` so
+that the oldest is first and newest is last.
 
-From Theorem 2.2 of [^Byrd1994], the expression for the inverse Hessian ``H`` is
+From [Byrd1994; Theorem 2.2](@citet), the expression for the inverse Hessian ``H`` is
 
 ```math
 \\begin{align}
@@ -91,10 +91,9 @@ H &= H_0 + B D B^\\mathrm{T}
 \\end{align}
 ```
 
-[^Byrd1994]: Byrd, R.H., Nocedal, J. & Schnabel, R.B.
-             Representations of quasi-Newton matrices and their use in limited memory methods.
-             Mathematical Programming 63, 129–156 (1994).
-             doi: [10.1007/BF01582063](https://doi.org/10.1007/BF01582063)
+# References
+
+- [Byrd1994](@cite): Byrd et al. Math. Program. 63, 1994.
 """
 function lbfgs_inverse_hessian(H₀::Diagonal, S0, Y0, history_ind, history_length)
     J = history_length