diff --git a/Project.toml b/Project.toml
new file mode 100644
index 0000000..59c7a1f
--- /dev/null
+++ b/Project.toml
@@ -0,0 +1,19 @@
+name = "DiffEqUncertainty"
+uuid = "ef61062a-5684-51dc-bb67-a0fcdec5c97d"
+authors = ["Chris Rackauckas <accounts@chrisrackauckas.com>"]
+version = "v1.2.0"
+
+[deps]
+DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
+
+[compat]
+julia = "1"
+DiffEqBase = "6"
+
+[extras]
+DiffEqProblemLibrary = "a077e3f3-b75c-5d7f-a0c6-6bc4c8ec64a9"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["DiffEqProblemLibrary", "Test", "OrdinaryDiffEq"]
diff --git a/test/runtests.jl b/test/runtests.jl
index 756b909..692c28f 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -1,12 +1,13 @@
-using DiffEqUncertainty, DiffEqBase, OrdinaryDiffEq, DiffEqProblemLibrary, DiffEqMonteCarlo
+using DiffEqUncertainty, DiffEqBase, OrdinaryDiffEq, DiffEqProblemLibrary
 using Test
 
-using ParameterizedFunctions
-g = @ode_def_bare LorenzExample begin
-  dx = σ*(y-x)
-  dy = x*(ρ-z) - y
-  dz = x*y - β*z
-end σ ρ β
+function g(du,u,p,t)
+  σ,ρ,β = p
+  x,y,z = u
+  du[1] = σ*(y-x)
+  du[2] = x*(ρ-z) - y
+  du[3] = x*y - β*z
+end
 u0 = [1.0;0.0;0.0]
 tspan = (0.0,10.0)
 p = [10.0,28.0,8/3]
@@ -14,14 +15,15 @@ prob = ODEProblem(g,u0,tspan,p)
 
 cb = ProbIntsUncertainty(1e4,5)
 solve(prob,Tsit5())
-monte_prob = MonteCarloProblem(prob)
-sim = solve(monte_prob,Tsit5(),num_monte=10,callback=cb,adaptive=false,dt=1/10)
+monte_prob = EnsembleProblem(prob)
+sim = solve(monte_prob,Tsit5(),trajectories=10,callback=cb,adaptive=false,dt=1/10)
 
 #using Plots; plotly(); plot(sim,vars=(0,1),linealpha=0.4)
 
-fitz = @ode_def_nohes FitzhughNagumo begin
-  dV = 3.0*(V - V^3/3 + R)
-  dR = -(1/3.0)*(V -  0.2 - 0.2*R)
+function fitz(du,u,p,t)
+  V,R = u
+  du[1] = 3.0*(V - V^3/3 + R)
+  du[2] = -(1/3.0)*(V -  0.2 - 0.2*R)
 end
 u0 = [-1.0;1.0]
 tspan = (0.0,20.0)
@@ -29,14 +31,14 @@ prob = ODEProblem(fitz,u0,tspan)
 
 cb = ProbIntsUncertainty(0.1,1)
 sol = solve(prob,Euler(),dt=1/10)
-monte_prob = MonteCarloProblem(prob)
-sim = solve(monte_prob,Euler(),num_monte=100,callback=cb,adaptive=false,dt=1/10)
+monte_prob = EnsembleProblem(prob)
+sim = solve(monte_prob,Euler(),trajectories=100,callback=cb,adaptive=false,dt=1/10)
 
 #using Plots; plotly(); plot(sim,vars=(0,1),linealpha=0.4)
 
 cb = AdaptiveProbIntsUncertainty(5)
 sol = solve(prob,Tsit5())
-monte_prob = MonteCarloProblem(prob)
-sim = solve(monte_prob,Tsit5(),num_monte=100,callback=cb,abstol=1e-3,reltol=1e-1)
+monte_prob = EnsembleProblem(prob)
+sim = solve(monte_prob,Tsit5(),trajectories=100,callback=cb,abstol=1e-3,reltol=1e-1)
 
 #using Plots; plotly(); plot(sim,vars=(0,1),linealpha=0.4)