add docstring and update README

README.md

@@ -1,3 +1,20 @@
 # TaylorTest
 
 Simple package to check derivatives
+
+# Usage
+
+```
+  check(f, Jf, x[, constant_components]; f_kwargs...)
+```
+
+Returns true if `Jf` approximates the derivative/gradient/Jacobian of `f` at point `x` (along a random direction).
+`f_kwargs` are keywords arguments to be passed to `f` and `Jf`.
+`constant_components` is an optional `Vector{Int}` corresponding to components of the direction which should be set to zero,
+effectively ignoring the dependency of `f` on these components.
+
+## Examples
+```julia-repl
+julia> f = x -> cos(x); Jf = x -> -sin(x); check(f, Jf, rand())
+true
+```

src/TaylorTest.jl

@@ -3,6 +3,21 @@ module TaylorTest
 import LinearAlgebra: norm, dot
 import TensorOperations: @tensor
 
+"""
+  check(f, Jf, x[, constant_components]; f_kwargs...)
+
+Returns true if `Jf` approximates the derivative/gradient/Jacobian of `f` at point `x` (along a random direction).
+`f_kwargs` are keywords arguments to be passed to `f` and `Jf`.
+`constant_components` is an optional `Vector{Int}` corresponding to components of the direction which should be set to zero,
+effectively ignoring the dependency of `f` on these components.
+
+# Examples
+```julia-repl
+julia> f = x -> cos(x); Jf = x -> -sin(x); check(f, Jf, rand())
+true
+```
+"""
+
 function check(f, Jf, x, constant_components::Vector{Int}=Int[]; f_kwargs...)
   direction = 2 * (rand(size(x)...) .- 0.5)
   for cst in constant_components