Dust off: CI, docs, remove SimpleUnpack.

.github/workflows/CI.yml

@@ -17,8 +17,8 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6' # Replace this with the minimum Julia version that your package supports. E.g. if your package requires Julia 1.5 or higher, change this to '1.5'.
-          - '1' # Leave this line unchanged. '1' will automatically expand to the latest stable 1.x release of Julia.
+          - 'min'
+          - '1' # will automatically expand to the latest stable 1.x release of Julia.
           # - 'nightly' # NOTE: nightly disabled as it currently fails
         os:
           - ubuntu-latest
@@ -30,7 +30,7 @@ jobs:
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: actions/cache@v1
+      - uses: actions/cache@v4
         env:
           cache-name: cache-artifacts
         with:
@@ -43,7 +43,9 @@ jobs:
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1
-      - uses: codecov/codecov-action@v4
+      - uses: codecov/codecov-action@v5
         with:
+          file: lcov.info
+          # NOTE: you need to add the token to Github secrets, see
+          # https://docs.codecov.com/docs/adding-the-codecov-token
           token: ${{ secrets.CODECOV_TOKEN }}
-          fail_ci_if_error: false  # or true if you want CI to fail when Codecov fails

Project.toml

@@ -11,7 +11,7 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 [compat]
 ArgCheck = "1, 2"
 DocStringExtensions = "0.8, 0.9"
-julia = "1.6"
+julia = "1.10"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

docs/Project.toml

@@ -2,8 +2,8 @@
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"
-SimpleUnPack = "ce78b400-467f-4804-87d8-8f486da07d0a"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 TransformVariables = "84d833dd-6860-57f9-a1a7-6da5db126cff"
 TransformedLogDensities = "f9bc47f6-f3f8-4f3b-ab21-f8bc73906f26"

docs/src/index.md

@@ -46,9 +46,13 @@ which is added to the log likelihood to obtain the log posterior.
 
 It is useful to define a *callable* that implements this, taking some vector `x` as an input and calculating the summary statistics, then, when called with a `NamedTuple` containing the parameters, evaluating to the log posterior.
 
-```@example 1
+```@setup A
+using LogDensityProblems
+```
+
+```@example A
 using Random; Random.seed!(1) # hide
-using Statistics, SimpleUnPack # imported for our implementation
+using Statistics
 
 struct NormalPosterior{T} # contains the summary statistics
     N::Int
@@ -63,8 +67,8 @@ end
 
 # define a callable that unpacks parameters, and evaluates the log likelihood
 function (problem::NormalPosterior)(θ)
-    @unpack μ, σ = θ
-    @unpack N, x̄, S = problem
+    (; μ, σ) = θ
+    (; N, x̄, S) = problem
     loglikelihood = -N * (log(σ) + (S + abs2(μ - x̄)) / (2 * abs2(σ)))
     logprior = - abs2(σ)/8 - abs2(μ)/50
     loglikelihood + logprior
@@ -76,7 +80,7 @@ nothing # hide
 
 Let's try out the posterior calculation:
 
-```@repl 1
+```@repl A
 problem((μ = 0.0, σ = 1.0))
 ```
 
@@ -90,13 +94,13 @@ In our example, we require ``\sigma > 0``, otherwise the problem is meaningless.
 !!! note
     Since version 1.0, TransformedLogDensity has been moved to the package TransformedLogDensities.
 
-```@repl 1
+```@repl A
 using LogDensityProblems, TransformVariables, TransformedLogDensities
 ℓ = TransformedLogDensity(as((μ = asℝ, σ = asℝ₊)), problem)
 ```
 
 Then we can query the dimension of this problem, and evaluate the log density:
-```@repl 1
+```@repl A
 LogDensityProblems.dimension(ℓ)
 LogDensityProblems.logdensity(ℓ, zeros(2))
 ```
@@ -108,7 +112,7 @@ LogDensityProblems.logdensity(ℓ, zeros(2))
 
 If you prefer to implement the transformation yourself, you just have to define the following three methods for your problem: declare that it can evaluate log densities (but not their gradient, hence the `0` order), allow the dimension of the problem to be queried, and then finally code the density calculation with the transformation. (Note that using `TransformedLogDensities.TransformedLogDensity` takes care of all of these for you, as shown above).
 
-```@example 1
+```@example A
 function LogDensityProblems.capabilities(::Type{<:NormalPosterior})
     LogDensityProblems.LogDensityOrder{0}()
 end
@@ -123,7 +127,7 @@ end
 nothing # hide
 ```
 
-```@repl 1
+```@repl A
 LogDensityProblems.logdensity(problem, zeros(2))
 ```
 
@@ -134,7 +138,7 @@ Here we use the exponential function to transform from ``\mathbb{R}`` to the pos
 Using either definition, you can transform to another object which is capable of evaluating the *gradient*, using automatic differentiation. For this, you need the [LogDensityProblemsAD.jl](https://github.com/tpapp/LogDensityProblemsAD.jl) package.
 
 Now observe that we can obtain gradients, too:
-```@repl 1
+```@repl A
 import ForwardDiff
 using LogDensityProblemsAD
 ∇ℓ = ADgradient(:ForwardDiff, ℓ)