Formatting docstrings and typos

.JuliaFormatter.toml

@@ -1,2 +1,3 @@
 style = "sciml"
-format_markdown = true
+format_markdown = true
+format_docstrings = true

.github/dependabot.yml

@@ -5,3 +5,6 @@ updates:
  directory: "/" # Location of package manifests
  schedule:
  interval: "weekly"
+ ignore:
+ - dependency-name: "crate-ci/typos"
+ update-types: ["version-update:semver-patch", "version-update:semver-minor"]

.github/workflows/SpellCheck.yml

@@ -0,0 +1,13 @@
+name: Spell Check
+
+on: [pull_request]
+
+jobs:
+ typos-check:
+ name: Spell Check with Typos
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout Actions Repository
+ uses: actions/checkout@v3
+ - name: Check spelling
+ uses: crate-ci/typos@v1.16.23 

.typos.toml

@@ -0,0 +1 @@
+[default.extend-words]

src/dense.jl

@@ -11,7 +11,7 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
 
 # Fields
 
-- `ruleconfig::RC`: a [`ChainRulesCore.RuleConfig`](https://juliadiff.org/ChainRulesCore.jl/stable/rule_author/superpowers/ruleconfig.html) object.
+ - `ruleconfig::RC`: a [`ChainRulesCore.RuleConfig`](https://juliadiff.org/ChainRulesCore.jl/stable/rule_author/superpowers/ruleconfig.html) object.
 """
 Base.@kwdef struct AutoChainRules{RC} <: AbstractADType
  ruleconfig::RC
@@ -38,18 +38,19 @@ mode(::AutoDiffractor) = ForwardOrReverseMode()
  AutoEnzyme{M}
 
 Struct used to select the [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) backend for automatic differentiation.
- 
+
 Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
- 
+
 # Constructors
 
  AutoEnzyme(; mode=nothing)
 
 # Fields
 
-- `mode::M`: can be either 
- - an object subtyping `EnzymeCore.Mode` (like `EnzymeCore.Forward` or `EnzymeCore.Reverse`) if a specific mode is required
- - `nothing` to choose the best mode automatically
+ - `mode::M`: can be either
+
+ + an object subtyping `EnzymeCore.Mode` (like `EnzymeCore.Forward` or `EnzymeCore.Reverse`) if a specific mode is required
+ + `nothing` to choose the best mode automatically
 """
 Base.@kwdef struct AutoEnzyme{M} <: AbstractADType
  mode::M = nothing
@@ -85,9 +86,9 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
 
 # Fields
 
-- `fdtype::T1`: finite difference type
-- `fdjtype::T2`: finite difference type for the Jacobian
-- `fdhtype::T3`: finite difference type for the Hessian
+ - `fdtype::T1`: finite difference type
+ - `fdjtype::T2`: finite difference type for the Jacobian
+ - `fdhtype::T3`: finite difference type for the Hessian
 """
 Base.@kwdef struct AutoFiniteDiff{T1, T2, T3} <: AbstractADType
  fdtype::T1 = Val(:forward)
@@ -110,7 +111,7 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
 
 # Fields
 
-- `fdm::T`: a [`FiniteDifferenceMethod`](https://juliadiff.org/FiniteDifferences.jl/stable/pages/api/#FiniteDifferences.FiniteDifferenceMethod)
+ - `fdm::T`: a [`FiniteDifferenceMethod`](https://juliadiff.org/FiniteDifferences.jl/stable/pages/api/#FiniteDifferences.FiniteDifferenceMethod)
 """
 Base.@kwdef struct AutoFiniteDifferences{T} <: AbstractADType
  fdm::T
@@ -131,11 +132,11 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
 
 # Type parameters
 
-- `chunksize`: the preferred [chunk size](https://juliadiff.org/ForwardDiff.jl/stable/user/advanced/#Configuring-Chunk-Size) to evaluate several derivatives at once
+ - `chunksize`: the preferred [chunk size](https://juliadiff.org/ForwardDiff.jl/stable/user/advanced/#Configuring-Chunk-Size) to evaluate several derivatives at once
 
 # Fields
 
-- `tag::T`: a [custom tag](https://juliadiff.org/ForwardDiff.jl/release-0.10/user/advanced.html#Custom-tags-and-tag-checking-1) to handle nested differentiation calls (usually not necessary)
+ - `tag::T`: a [custom tag](https://juliadiff.org/ForwardDiff.jl/release-0.10/user/advanced.html#Custom-tags-and-tag-checking-1) to handle nested differentiation calls (usually not necessary)
 """
 struct AutoForwardDiff{chunksize, T} <: AbstractADType
  tag::T
@@ -149,22 +150,22 @@ mode(::AutoForwardDiff) = ForwardMode()
 
 """
  AutoPolyesterForwardDiff{chunksize,T}
- 
+
 Struct used to select the [PolyesterForwardDiff.jl](https://github.com/JuliaDiff/PolyesterForwardDiff.jl) backend for automatic differentiation.
- 
+
 Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
- 
+
 # Constructors
- 
+
  AutoPolyesterForwardDiff(; chunksize=nothing, tag=nothing)
 
 # Type parameters
 
-- `chunksize`: the preferred [chunk size](https://juliadiff.org/ForwardDiff.jl/stable/user/advanced/#Configuring-Chunk-Size) to evaluate several derivatives at once
+ - `chunksize`: the preferred [chunk size](https://juliadiff.org/ForwardDiff.jl/stable/user/advanced/#Configuring-Chunk-Size) to evaluate several derivatives at once
 
 # Fields
 
-- `tag::T`: a [custom tag](https://juliadiff.org/ForwardDiff.jl/release-0.10/user/advanced.html#Custom-tags-and-tag-checking-1) to handle nested differentiation calls (usually not necessary)
+ - `tag::T`: a [custom tag](https://juliadiff.org/ForwardDiff.jl/release-0.10/user/advanced.html#Custom-tags-and-tag-checking-1) to handle nested differentiation calls (usually not necessary)
 """
 struct AutoPolyesterForwardDiff{chunksize, T} <: AbstractADType
  tag::T
@@ -189,7 +190,7 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
 
 # Fields
 
-- `compile::Bool`: whether to [compile the tape](https://juliadiff.org/ReverseDiff.jl/api/#ReverseDiff.compile) prior to differentiation
+ - `compile::Bool`: whether to [compile the tape](https://juliadiff.org/ReverseDiff.jl/api/#ReverseDiff.compile) prior to differentiation
 """
 Base.@kwdef struct AutoReverseDiff <: AbstractADType
  compile::Bool = false
@@ -225,7 +226,7 @@ Defined by [ADTypes.jl](https://github.com/SciML/ADTypes.jl).
 
 # Fields
 
-- `safe_mode::Bool`: whether to run additional checks to catch errors early. On by default. Turn off to maximise performance if your code runs correctly.
+ - `safe_mode::Bool`: whether to run additional checks to catch errors early. On by default. Turn off to maximise performance if your code runs correctly.
 """
 Base.@kwdef struct AutoTapir <: AbstractADType
  safe_mode::Bool = true

src/mode.jl

@@ -5,10 +5,10 @@ Abstract supertype for the traits identifying differentiation modes.
 
 # Subtypes
 
-- [`ForwardMode`](@ref)
-- [`ReverseMode`](@ref)
-- [`ForwardOrReverseMode`](@ref)
-- [`SymbolicMode`](@ref)
+ - [`ForwardMode`](@ref)
+ - [`ReverseMode`](@ref)
+ - [`ForwardOrReverseMode`](@ref)
+ - [`SymbolicMode`](@ref)
 """
 abstract type AbstractMode end
 
@@ -41,7 +41,8 @@ struct ReverseMode <: AbstractMode end
 Trait for AD choices that can work either in [`ForwardMode`](@ref) or [`ReverseMode`](@ref), depending on their configuration.
 
 !!! warning
- This trait should rarely be used, because more precise dispatches to [`ForwardMode`](@ref) or [`ReverseMode`](@ref) should be defined. 
+
+ This trait should rarely be used, because more precise dispatches to [`ForwardMode`](@ref) or [`ReverseMode`](@ref) should be defined.
 """
 struct ForwardOrReverseMode <: AbstractMode end
 

src/sparse.jl

@@ -7,8 +7,8 @@ Abstract supertype for sparsity pattern detectors.
 
 # Required methods
 
-- [`jacobian_sparsity`](@ref)
-- [`hessian_sparsity`](@ref)
+ - [`jacobian_sparsity`](@ref)
+ - [`hessian_sparsity`](@ref)
 """
 abstract type AbstractSparsityDetector end
 
@@ -34,7 +34,7 @@ Trivial sparsity detector, which always returns a full sparsity pattern (only on
 
 # See also
 
-- [`AbstractSparsityDetector`](@ref)
+ - [`AbstractSparsityDetector`](@ref)
 """
 struct NoSparsityDetector <: AbstractSparsityDetector end
 
@@ -51,18 +51,18 @@ Abstract supertype for Jacobian/Hessian coloring algorithms, defined for example
 
 # Required methods
 
-- [`column_coloring`](@ref)
-- [`row_coloring`](@ref)
-- [`symmetric_coloring`](@ref)
+ - [`column_coloring`](@ref)
+ - [`row_coloring`](@ref)
+ - [`symmetric_coloring`](@ref)
 
 # Note
 
 The terminology and definitions are taken from the following paper:
 
 > "What Color Is Your Jacobian? Graph Coloring for Computing Derivatives"
->
+> 
 > Assefaw Hadish Gebremedhin, Fredrik Manne, and Alex Pothen (2005)
->
+> 
 > https://epubs.siam.org/doi/10.1137/S0036144504444711
 """
 abstract type AbstractColoringAlgorithm end
@@ -88,12 +88,12 @@ function row_coloring end
 """
  symmetric_coloring(M::AbstractMatrix, ca::ColoringAlgorithm)::AbstractVector{<:Integer}
 
-Use algorithm `ca` to construct a symetrically structurally orthogonal partition of the columns (or rows) of the symmetric matrix `M`.
+Use algorithm `ca` to construct a symmetrically structurally orthogonal partition of the columns (or rows) of the symmetric matrix `M`.
 
 The result is a coloring vector `c` of length `size(M, 1) == size(M, 2)` such that for every non-zero coefficient `M[i, j]`, at least one of the following conditions holds:
 
-- column `j` is the only column of its color `c[j]` with a non-zero coefficient in row `i`;
-- column `i` is the only column of its color `c[i]` with a non-zero coefficient in row `j`.
+ - column `j` is the only column of its color `c[j]` with a non-zero coefficient in row `i`;
+ - column `i` is the only column of its color `c[i]` with a non-zero coefficient in row `j`.
 """
 function symmetric_coloring end
 
@@ -104,7 +104,7 @@ Trivial coloring algorithm, which always returns a different color for each matr
 
 # See also
 
-- [`AbstractColoringAlgorithm`](@ref)
+ - [`AbstractColoringAlgorithm`](@ref)
 """
 struct NoColoringAlgorithm <: AbstractColoringAlgorithm end
 
@@ -121,9 +121,9 @@ Wraps an ADTypes.jl object to deal with sparse Jacobians and Hessians.
 
 # Fields
 
-- `dense_ad::D`: the underlying AD package, subtyping [`AbstractADType`](@ref)
-- `sparsity_detector::S`: the sparsity pattern detector, subtyping [`AbstractSparsityDetector`](@ref)
-- `coloring_algorithm::C`: the coloring algorithm, subtyping [`AbstractColoringAlgorithm`](@ref)
+ - `dense_ad::D`: the underlying AD package, subtyping [`AbstractADType`](@ref)
+ - `sparsity_detector::S`: the sparsity pattern detector, subtyping [`AbstractSparsityDetector`](@ref)
+ - `coloring_algorithm::C`: the coloring algorithm, subtyping [`AbstractColoringAlgorithm`](@ref)
 
 # Constructors
 
@@ -158,10 +158,10 @@ end
  dense_ad(ad::AutoSparse)::AbstractADType
 
 Return the underlying AD package for a sparse AD choice.
- 
+
 # See also
 
-- [`AutoSparse`](@ref)
+ - [`AutoSparse`](@ref)
 """
 dense_ad(ad::AutoSparse) = ad.dense_ad
 
@@ -174,8 +174,8 @@ Return the sparsity pattern detector for a sparse AD choice.
 
 # See also
 
-- [`AutoSparse`](@ref)
-- [`AbstractSparsityDetector`](@ref)
+ - [`AutoSparse`](@ref)
+ - [`AbstractSparsityDetector`](@ref)
 """
 sparsity_detector(ad::AutoSparse) = ad.sparsity_detector
 
@@ -186,7 +186,7 @@ Return the coloring algorithm for a sparse AD choice.
 
 # See also
 
-- [`AutoSparse`](@ref)
-- [`AbstractColoringAlgorithm`](@ref)
+ - [`AutoSparse`](@ref)
+ - [`AbstractColoringAlgorithm`](@ref)
 """
 coloring_algorithm(ad::AutoSparse) = ad.coloring_algorithm

test/dense.jl

@@ -139,8 +139,8 @@ end
  @test ad isa AutoTapir
  @test mode(ad) isa ReverseMode
  @test ad.safe_mode
- 
- ad = AutoTapir(; safe_mode=false)
+
+ ad = AutoTapir(; safe_mode = false)
  @test !ad.safe_mode
 end