Add explicit test dependency on FiniteDiff

It's generally better practice to do this since it means that stuff
doesn't break if NLSolversBase removes its dependency on FiniteDiff. In
this case it should also fix the failure on Julia 1.1.

Project.toml

@@ -18,8 +18,9 @@ StatsAPI = "1"
 julia = "1.1"
 
 [extras]
+FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "StableRNGs"]
+test = ["Test", "StableRNGs", "FiniteDiff"]

test/levenberg_marquardt.jl

@@ -1,4 +1,4 @@
-using LsqFit, Test, StableRNGs, NLSolversBase
+using LsqFit, Test, StableRNGs, NLSolversBase, FiniteDiff
 @testset "optimization" begin
     function f_lm(x)
         [x[1], 2.0 - x[2]]
@@ -62,7 +62,7 @@ using LsqFit, Test, StableRNGs, NLSolversBase
         ydata = model(xdata, [1.0 2.0]) + 0.01 * randn(rng, _nobs)
 
         f_lsq = p -> model(xdata, p) - ydata
-        g_lsq = p -> NLSolversBase.FiniteDiff.finite_difference_jacobian(f_lsq, p)
+        g_lsq = p -> FiniteDiff.finite_difference_jacobian(f_lsq, p)
 
         R3 = OnceDifferentiable(f_lsq, g_lsq, zeros(2), zeros(_nobs); inplace = false)
         results = LsqFit.levenberg_marquardt(R3, [0.5, 0.5])
@@ -81,7 +81,7 @@ using LsqFit, Test, StableRNGs, NLSolversBase
         ydata = model(xdata, p_generate) + 0.1 * randn(rng, length(xdata))
 
         f_lsq = p -> model(xdata, p) - ydata
-        g_lsq = p -> NLSolversBase.FiniteDiff.finite_difference_jacobian(f_lsq, p)
+        g_lsq = p -> FiniteDiff.finite_difference_jacobian(f_lsq, p)
 
         R4 = OnceDifferentiable(
             f_lsq,