update Documenter version (#539)

* update Documenter version

* work on bumping to Documenter v1.0

docs/Project.toml

@@ -6,5 +6,5 @@ Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 
 [compat]
-Documenter = "0.27"
+Documenter = "1"
 GR_jll = "< 0.58"

docs/make.jl

@@ -22,6 +22,7 @@ makedocs(
         ],
         "Extending" => "extending.md",
     ],
+    warnonly = [:cross_references, :missing_docs],
 )
 
 deploydocs(repo = "github.com/JuliaMath/Polynomials.jl.git")

docs/src/extending.md

@@ -1,6 +1,6 @@
 # Extending Polynomials
 
-The [`AbstractUnivaeriatePolynomial`](@ref) type was made to be extended.
+The [`AbstractUnivariatePolynomial`](@ref) type was made to be extended.
 
 A polynomial's  coefficients  are  relative to some *basis*. The `Polynomial` type relates coefficients  `[a0, a1,  ..., an]`, say,  to the  polynomial  ``a_0 +  a_1\cdot x + a_2\cdot x^2  + \cdots +  a_n\cdot x^n``,  through the standard  basis  ``1,  x,  x^2, ..., x^n``.  New polynomial  types typically represent the polynomial through a different  basis. For example,  `CheyshevT` uses a basis  ``T_0=1, T_1=x,  T_2=2x^2-1,  \cdots,  T_n  =  2xT_{n-1} - T_{n-2}``.  For this type  the  coefficients  `[a0,a1,...,an]` are associated with  the polynomial  ``a0\cdot T_0  + a_1 \cdot T_1 +  \cdots  +  a_n\cdot T_n`.
 

src/common.jl

@@ -105,7 +105,7 @@ the variance-covariance matrix.)
 
 ## large degree
 
-For fitting with a large degree, the Vandermonde matrix is exponentially ill-conditioned. The [`ArnoldiFit`](@ref) type introduces an Arnoldi orthogonalization that fixes this problem.
+For fitting with a large degree, the Vandermonde matrix is exponentially ill-conditioned. The `ArnoldiFit` type introduces an Arnoldi orthogonalization that fixes this problem.
 
 
 """

src/polynomials/ngcd.jl

@@ -1,7 +1,7 @@
 """
     ngcd(p, q, [k]; kwargs...)
 
-Find numerical GCD of polynomials `p` and `q`. Refer to [`NGCD.ngcd(p,q)`](@ref) for details.
+Find numerical GCD of polynomials `p` and `q`. Refer to `NGCD.ngcd` for details.
 
 The main entry point for this function is `gcd(p, q, method=:numerical)`, but `ngcd` outputs the gcd factorization -- `u, v, w` with `u*v ≈ p` and `u*w ≈ q` -- along with `Θ`, an estimate on how close `p`,`q` is to a gcd factorization of degree `k` and `κ` the GCD condition number.
 

src/polynomials/standard-basis/laurent-polynomial.jl

@@ -219,7 +219,7 @@ The `cconj` of a polynomial, `p̃`, conjugates the coefficients and applies `s -
 
 This satisfies for *imaginary* `s`: `conj(p(s)) = p̃(s) = (conj ∘ p)(s) = cconj(p)(s) `
 
-[ref](https://github.com/hurak/PolynomialEquations.jl#symmetrix-conjugate-equation-continuous-time-case)
+[reference](https://github.com/hurak/PolynomialEquations.jl#symmetrix-conjugate-equation-continuous-time-case)
 
 Examples:
 ```jldoctest laurent

src/polynomials/standard-basis/standard-basis.jl

@@ -232,7 +232,7 @@ By default, uses the Euclidean division algorithm (`method=:euclidean`), which i
 
 Passing `method=:noda_sasaki` uses scaling to circumvent some of these.
 
-Passing `method=:numerical` will call the internal method `NGCD.ngcd` for the numerical gcd. See the help page of [`Polynomials.NGCD.ngcd(p,q)`](@ref) for details.
+Passing `method=:numerical` will call the internal method `NGCD.ngcd` for the numerical gcd. See the docstring of `NGCD.ngcd` for details.
 """
 function Base.gcd(p1::P, p2::Q, args...;
                   method=:euclidean,

src/rational-functions/fit.jl

@@ -83,7 +83,7 @@ end
 """
     fit(::Type{RationalFunction}, r::Polynomial, m, n; var=:x)
 
-Fit a Pade approximant ([`pade_fit`](@ref)) to `r`.
+Fit a Pade approximant (cf docstring for `Polynomials.pade_fit`) to `r`.
 
 Examples:
 

src/rational-functions/rational-function.jl

@@ -5,7 +5,7 @@
 Create a rational expression (`p//q`) from the two polynomials.
 
 Common factors are not cancelled by the constructor, as they are for
-the base `Rational` type. The [`lowest_terms(pq)`](@ref) function attempts
+the base `Rational` type. The [`lowest_terms`](@ref) function attempts
 that operation.
 
 For purposes of iteration, a rational function is treated like a two-element container.