fix typos

src/deprecated.jl

@@ -8,9 +8,9 @@ end
 """
     rand_tangent([rng::AbstractRNG,] x)
 
-Returns a arbitary tangent vector _appropriate_ for the primal value `x`.
+Returns an arbitrary tangent vector _appropriate_ for the primal value `x`.
 Note that despite the name, no promises on the statistical randomness are made.
-Rather it is an arbitary value, that is generated using the `rng`.
+Rather it is an arbitrary value, that is generated using the `rng`.
 """
 rand_tangent(x) = rand_tangent(Random.GLOBAL_RNG, x)
 

src/methods.jl

@@ -67,9 +67,9 @@ difference method, `bound_estimator`, will be tasked with estimating the `P`th o
 derivative in a _neighbourhood_, not just at some `x`. To do this, it will use a careful
 reweighting of the function evaluations to estimate the `P`th order derivative at, in the
 case of a central method, `x - h`, `x`, and `x + h`, where `h` is the step size. The
-coeffients for this estimate, the _neighbourhood estimate_, are given by the three sets of
-coeffients in `bound_estimator.coefs_neighbourhood`. The round-off error is estimated by the
-round-off error of the function evaluations performed by `bound_estimator`. The trunction
+coefficients for this estimate, the _neighbourhood estimate_, are given by the three sets of
+coefficients in `bound_estimator.coefs_neighbourhood`. The round-off error is estimated by the
+round-off error of the function evaluations performed by `bound_estimator`. The truncation
 error is amplified by `condition`, and the round-off error is amplified by `factor`. The
 quantities `∇f_magnitude_mult` and `f_error_mult` are precomputed quantities that facilitate
 the step size adaptation procedure.
@@ -490,7 +490,7 @@ for direction in [:forward, :central, :backward]
         geom::Bool=false
     )
 
-Contruct a finite difference method at a $($(Meta.quot(direction))) grid of `p` points.
+Construct a finite difference method at a $($(Meta.quot(direction))) grid of `p` points.
 
 # Arguments
 - `p::Int`: Number of grid points.

src/to_vec.jl

@@ -196,7 +196,7 @@ end
 
 function to_vec(x::F) where {F <: SVD}
     # Convert the vector S to a matrix so we can work with a vector of matrices
-    # only and inferrence work
+    # only and inference work
     v = [x.U, reshape(x.S, length(x.S), 1), x.Vt]
     x_vec, back = to_vec(v)
     function SVD_from_vec(v)
@@ -216,7 +216,7 @@ end
 
 function to_vec(x::S) where {U, S <: Union{LinearAlgebra.QRCompactWYQ{U}, LinearAlgebra.QRCompactWY{U}}}
     # x.T is composed of upper triangular blocks. The subdiagonals elements
-    # of the blocks are abitrary. We make sure to set all of them to zero
+    # of the blocks are arbitrary. We make sure to set all of them to zero
     # to avoid NaN.
     blocksize, cols = size(x.T)
     T = zeros(U, blocksize, cols)

test/deprecated.jl

@@ -102,7 +102,7 @@ rand_tangent(args...) = @test_deprecated FiniteDifferences.rand_tangent(args...)
         )
     end
 
-    @testset "compsition of addition" begin
+    @testset "composition of addition" begin
         x = Foo(1.5, 2, Foo(1.1, 3, [1.7, 1.4, 0.9]))
         @test x + rand_tangent(x) isa typeof(x)
         @test x + (rand_tangent(x) + rand_tangent(x)) isa typeof(x)

test/grad.jl

@@ -61,7 +61,7 @@ using FiniteDifferences: grad, jacobian, _jvp, jvp, j′vp, _j′vp, to_vec
         @test _jvp(fdm, f, x, ẋ) ≈ J_exact * ẋ
         @test _j′vp(fdm, f, ȳ, x) ≈ transpose(J_exact) * ȳ
 
-        # Check that no mutation occured that wasn't reverted.
+        # Check that no mutation occurred that wasn't reverted.
         @test xc == x
     end
 

test/to_vec.jl

@@ -146,7 +146,7 @@ end
                 test_to_vec(cholesky(P))
 
                 # Special treatment for QR since it is represented by a matrix
-                # with some arbirtrary values.
+                # with some arbitrary values.
                 F = qr(M)
                 @inferred to_vec(F)
                 F_vec, back = to_vec(F)