use as keyword

CDCgov · Dec 13, 2024 · 0c244b8 · 0c244b8
1 parent 14e68a9
commit 0c244b8
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diff --git a/EpiAware/docs/src/showcase/replications/chatzilena-2019/index.jl b/EpiAware/docs/src/showcase/replications/chatzilena-2019/index.jl
@@ -591,7 +591,7 @@ Starting from the initial guess, the MAP point is calculated rapidly in one pass
 
 # ╔═╡ 6796ae76-bc2d-4895-ba0a-5e2c23c50dfb
 map_fit_stoch_mdl = maximum_a_posteriori(stochastic_mdl;
-    adtype = AutoMooncake(config = nothing),
+    adtype = ADTypes.AutoMooncake(; config = nothing),
     initial_params = initial_guess
 )
 
@@ -603,7 +603,7 @@ Now we can run NUTS, sampling 1000 posterior draws per chain for 4 chains.
 # ╔═╡ 156272d7-56c4-4ac4-bf3e-7882f4edc144
 chn2 = sample(
     stochastic_mdl,
-    NUTS(; adtype = AutoMooncake(config = nothing)),
+    NUTS(; adtype = ADTypes.AutoMooncake(; config = nothing)),
     MCMCThreads(), 1000, 4;
     initial_params = fill(map_fit_stoch_mdl.values.array, 4)
 )

diff --git a/EpiAware/docs/src/showcase/replications/mishra-2020/index.jl b/EpiAware/docs/src/showcase/replications/mishra-2020/index.jl
@@ -394,7 +394,7 @@ num_threads = min(10, Threads.nthreads())
 inference_method = EpiMethod(
     pre_sampler_steps = [ManyPathfinder(nruns = 4, maxiters = 100)],
     sampler = NUTSampler(
-        adtype = AutoEnzyme(),
+        adtype = ADTypes.AutoEnzyme(),
         ndraws = 2000,
         nchains = num_threads,
         mcmc_parallel = MCMCThreads())

diff --git a/benchmark/make_epiaware_suite.jl b/benchmark/make_epiaware_suite.jl
@@ -4,7 +4,7 @@
 function make_epiaware_suite(model; check = true,
         adbackends = [
             :forwarddiff, :reversediff, :reversediff_compiled,
-            ADTypes.AutoMooncake(config = nothing),
+            ADTypes.AutoMooncake(; config = nothing),
             ADTypes.AutoEnzyme()
         ])
     suite = prefix_warnings(