Pass order of derivative as Val()

Author: facusapienza21

README.md

@@ -45,9 +45,9 @@ TaylorDiff.jl is fast! See our dedicated [benchmarks](https://benchmark.tansongc
 using TaylorDiff
 
 x = 0.1
-derivative(sin, x, 10) # scalar derivative
+derivative(sin, x, Val(10)) # scalar derivative
 v, direction = [3.0, 4.0], [1.0, 0.0]
-derivative(x -> sum(exp.(x)), v, direction, 2) # directional derivative
+derivative(x -> sum(exp.(x)), v, direction, Val(2)) # directional derivative
 ```
 
 Please see our [documentation](https://juliadiff.org/TaylorDiff.jl) for more details.

docs/src/index.md

@@ -31,9 +31,9 @@ TaylorDiff.jl is fast! See our dedicated [benchmarks](https://benchmark.tansongc
 using TaylorDiff
 
 x = 0.1
-derivative(sin, x, 10) # scalar derivative
+derivative(sin, x, Val(10)) # scalar derivative
 v, direction = [3.0, 4.0], [1.0, 0.0]
-derivative(x -> sum(exp.(x)), v, direction, 2) # directional derivative
+derivative(x -> sum(exp.(x)), v, direction, Val(2)) # directional derivative
 ```
 
 Please see our [documentation](https://juliadiff.org/TaylorDiff.jl) for more details.

examples/ode.jl

@@ -37,7 +37,7 @@ display(different_time(ic, p_true, 2e-5))
 display(just_t(0.5))
 
 #display(ForwardDiff.derivative(just_t,1.0))
-display(TaylorDiff.derivative(just_t, 1.0, 1))  #isnan error
+display(TaylorDiff.derivative(just_t, 1.0, Val(1)))  #isnan error
 #display(value_and_gradient(just_t, AutoForwardDiff(), 1.0))
 #display(value_and_gradient(just_t, AutoReverseDiff(), 1.0))
 #display(value_and_gradient(just_t, AutoEnzyme(Enzyme.Reverse), 1.0))