Fix typos

docs/src/methods.md

@@ -42,7 +42,7 @@ plot!(A)
 
 ## Lagrange Interpolation
 
-It fits polynomial of degree d (=length(t)-1), and is thuse a continuously
+It fits polynomial of degree d (=length(t)-1), and is thus a continuously
 differentiable function.
 
 ```@example tutorial
@@ -174,7 +174,7 @@ spaced, unordered, and/or repeat-valued). Generalized cross validation (GCV) or
 so-called L-curve methods can be used to determine an "optimal" value for the
 smoothing parameter. In this example, we perform smoothing in two ways. In the
 first, we find smooth values at the original ``t`` values and then
-interpolate. In the second, we perform the smoothing for the interpolatant
+interpolate. In the second, we perform the smoothing for the interpolant
 ``\hat{t}`` values directly. GCV is used to determine the regularization
 parameter for both cases.
 

ext/DataInterpolationsRegularizationToolsExt.jl

@@ -45,7 +45,7 @@ const LA = LinearAlgebra
                 derivative (i.e. the curvature) of the data is used to calculate roughness.
 
 # Keyword Arguments
-- `λ::{Number,Tuple} = 1.0`: regulariation parameter; larger values result in a smoother
+- `λ::{Number,Tuple} = 1.0`: regularization parameter; larger values result in a smoother
                              curve; the provided value is used directly when `alg = :fixed`;
                              otherwise it is used as an initial guess for the optimization
                              method, or as bounds if a 2-tuple is provided (TBD)
@@ -179,7 +179,7 @@ end
 # function RegularizationSmooth(u::AbstractVector, t::AbstractVector, t̂::AbstractVector,
 #                               wls::Symbol, d::Int=2; λ::Real=1.0, alg::Symbol=:gcv_svd)
 
-""" Solve for the smoothed depedent variables and create spline interpolator """
+""" Solve for the smoothed dependent variables and create spline interpolator """
 function _reg_smooth_solve(u::AbstractVector, t̂::AbstractVector, d::Int, M::AbstractMatrix,
     Wls½::AbstractMatrix, Wr½::AbstractMatrix, λ::Real, alg::Symbol)
     λ = float(λ) # `float` expected by RT

src/DataInterpolations.jl

@@ -90,7 +90,7 @@ end
     end
 end
 
-# Define an empty fucntion, so that it can be extended via `DataInterpolationsOptimExt`
+# Define an empty function, so that it can be extended via `DataInterpolationsOptimExt`
 Curvefit() = error("CurveFit requires loading Optim, e.g. `using Optim`")
 
 export Curvefit

src/interpolation_utils.jl

@@ -69,7 +69,7 @@ v[hi]` according to the specified order, assuming that `x` is actually within th
 values found in `v`.  If `x` is outside that range, either `lo` will be `firstindex(v)` or
 `hi` will be `lastindex(v)`.
 
-Note that the results will not typically satify `lo ≤ guess ≤ hi`.  If `x` is precisely
+Note that the results will not typically satisfy `lo ≤ guess ≤ hi`.  If `x` is precisely
 equal to a value that is not unique in the input `v`, there is no guarantee that `(lo, hi)`
 will encompass *all* indices corresponding to that value.
 

src/plot_rec.jl

@@ -202,7 +202,7 @@ end
 end
 
 ########################################
-#          Akima intepolation          #
+#          Akima interpolation          #
 ########################################
 
 @recipe function f(::Type{Val{:akima}},

test/interpolation_tests.jl

@@ -129,7 +129,7 @@ import ForwardDiff
     # Test derivative at point gives derivative to the right (except last is to left):
     ts = t[begin:(end - 1)]
     @test dA.(ts) == dA.(ts .+ 0.5)
-    # Test last derivitive is to the left:
+    # Test last derivative is to the left:
     @test dA(last(t)) == dA(last(t) - 0.5)
 
     # Test array-valued interpolation
@@ -537,7 +537,7 @@ A = QuadraticInterpolation(u, t)
 @test A(3.5) == [12.25, 12.25]
 @test A(2.5) == [6.25, 6.25]
 
-# ForwardDiff compatibility with respect to cofficients
+# ForwardDiff compatibility with respect to coefficients
 
 function square(INTERPOLATION_TYPE, c)  # elaborate way to write f(x) = x²
     xs = -4.0:2.0:4.0