Using AbstractArray instead of Array in finite_difference_jacobian!() ?

I attempted to use `curve_fit` from `LsqFit.jl` on data stored in a `DataFrame` column, as in `curve_fit(model, df[:x], df[:y], p0)`. This produced the following error:

```
LoadError: MethodError: `finite_difference_jacobian!` has no method matching finite_difference_jacobian!(::Function, ::Array{Float64,1}, ::DataArrays.DataArray{Float64,1}, ::Array{Float64,2}, ::Symbol)
Closest candidates are:
finite_difference_jacobian!{R<:Number,S<:Number,T<:Number}(::Function, ::Array{R<:Number,1}, !Matched::Array{S<:Number,1}, ::Array{T<:Number,N}, ::Symbol)
finite_difference_jacobian!{R<:Number,S<:Number,T<:Number}(::Function, ::Array{R<:Number,1}, !Matched::Array{S<:Number,1}, ::Array{T<:Number,N})
```

Of course this is easily resolved by explicitly converting the inputs to `Array`s, but it's a bit cumbersome. I'm pretty new to Julia and its type system, but from looking at the code there's no obvious reason to me why `finite_difference_jacobian!` couldn't work with its signature changed to use `AbstractArray` and `AbstractVector`, relieving the need to convert `DataArray`s. Is this reasonable? If there's no reason not to do it, I can try to test it out and make a pull request when I have time.