Rel 5.9.0

StatisticalRethinkingJulia · Aug 9, 2023 · 39a090f · 39a090f
1 parent d217eda
commit 39a090f
Show file tree

Hide file tree

Showing 11 changed files with 196 additions and 119 deletions.
diff --git a/notebooks/CausalInference/PC Algorithm: Example with real data.jl b/notebooks/CausalInference/PC Algorithm: Example with real data.jl
@@ -1,5 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.19.26
+# v0.19.27
 
 using Markdown
 using InteractiveUtils
@@ -65,7 +65,7 @@ begin
 end 
 
 # ╔═╡ f39b4739-7b63-43dc-bc2b-b65a173be1d5
-d_pc = create_pc_dag("d_pc", df, d1_str);
+d_pc = create_pcalg_gauss_dag("d_pc", df, d1_str);
 
 # ╔═╡ 6f457c4f-dbc5-4ac3-89d0-2a9c4c912fe4
 gvplot(d_pc)

diff --git a/notebooks/CausalInference/PC Algorithm: Further example.jl b/notebooks/CausalInference/PC Algorithm: Further example.jl
@@ -1,5 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.19.26
+# v0.19.27
 
 using Markdown
 using InteractiveUtils
@@ -87,7 +87,7 @@ dsep(dag_1, :x, :s, [:z], verbose=true)
 dsep(dag_1, :x, :z, [:v, :w], verbose=true)
 
 # ╔═╡ 4b75351b-c1d9-47b7-97c1-49eb90ea5fb1
-@time dag_2 = create_pc_dag("dag_2", df, g_dot_str, 0.025; est_func=cmitest);
+@time dag_2 = create_pcalg_cmi_dag("dag_2", df, g_dot_str; p=0.025);
 
 # ╔═╡ 66fae38a-f622-444f-bfce-2c52d336bfdb
 gvplot(dag_2)

diff --git a/notebooks/CausalInference/PC Algorithm: How it works.jl b/notebooks/CausalInference/PC Algorithm: How it works.jl
@@ -74,7 +74,7 @@ d1 = create_fci_dag("d1", df, g_dot_str);
 gvplot(d1)
 
 # ╔═╡ 71b95bc5-13f9-4278-bfc2-a3632b4c1552
-d2 = create_pc_dag("d1", df, g_dot_str);
+d2 = create_pcalg_gauss_dag("d1", df, g_dot_str);
 
 # ╔═╡ bfdb1885-15b9-4a6a-9bf4-30b0bb434b4c
 gvplot(d2)

diff --git a/notebooks/CausalInference/PC Algorithm: Reasoning about experiments.jl b/notebooks/CausalInference/PC Algorithm: Reasoning about experiments.jl
@@ -1,12 +1,15 @@
 ### A Pluto.jl notebook ###
-# v0.19.26
+# v0.19.27
 
 using Markdown
 using InteractiveUtils
 
-# ╔═╡ 62c80a26-975a-11ed-2e09-2dce0e33bb70
+# ╔═╡ 46ef488a-43ac-4b50-9093-d4e93dd38432
 using Pkg
 
+# ╔═╡ 081be249-4e6c-4d94-b2e8-044d0c7ddcae
+#Pkg.activate(expanduser("~/.julia/dev/SR2StanPluto"))
+
 # ╔═╡ 58ece6dd-a20f-4624-898a-40cae4b471e4
 begin
 	# General packages for this script
@@ -42,9 +45,6 @@ html"""
 </style>
 """
 
-# ╔═╡ aaea31c8-37ed-4f0f-8e3e-8e89d30ed918
-#Pkg.activate(expanduser("~/.julia/dev/SR2StanPluto"))
-
 # ╔═╡ 261cca70-a6dd-4bed-b2f2-8667534d0ceb
 let
 	Random.seed!(1)
@@ -72,7 +72,7 @@ md" ##### Dag d1 is the full generational causal graph."
 g_dot_str="DiGraph d1 {A->D; D->X; B->F; F->Y; X->E; E->Y; A->C; B->C; C->X; C->Y;}";
 
 # ╔═╡ 6bbfe4cb-f7e1-4503-a386-092882a1a49c
-d1 = create_fci_dag("d1", df_full, g_dot_str,0.25);
+d1 = create_fci_dag("d1", df_full, g_dot_str);
 
 # ╔═╡ b1f81e4e-cd1c-416d-8300-dcd6042a00bb
 gvplot(d1)
@@ -81,7 +81,7 @@ gvplot(d1)
 all_paths(d1, :A, :B)
 
 # ╔═╡ ff523e85-4760-4fba-99d9-1c19930fc855
-d2 = create_pc_dag("d2", df_full, g_dot_str,0.25);
+d2 = create_pcalg_gauss_dag("d2", df_full, g_dot_str; p=0.25);
 
 # ╔═╡ 27350494-87a9-4619-beeb-e9b31502c1cf
 gvplot(d2)
@@ -417,8 +417,8 @@ md" ##### Model m3.0s will likely provide the best estimate of the average causa
 # ╔═╡ Cell order:
 # ╟─ad08dd09-222a-4071-92d4-38deebaf2e82
 # ╠═e4552c81-d0db-4434-b81a-c86f1af515e5
-# ╠═62c80a26-975a-11ed-2e09-2dce0e33bb70
-# ╠═aaea31c8-37ed-4f0f-8e3e-8e89d30ed918
+# ╠═46ef488a-43ac-4b50-9093-d4e93dd38432
+# ╠═081be249-4e6c-4d94-b2e8-044d0c7ddcae
 # ╠═58ece6dd-a20f-4624-898a-40cae4b471e4
 # ╠═261cca70-a6dd-4bed-b2f2-8667534d0ceb
 # ╟─3a7676c8-38be-41d9-949e-d8de8c1b9cff

diff --git a/notebooks/CausalInference/PC and FCI Algorithms: Basic example.jl b/notebooks/CausalInference/PC and FCI Algorithms: Basic example.jl
@@ -1,5 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.19.26
+# v0.19.27
 
 using Markdown
 using InteractiveUtils
@@ -69,7 +69,7 @@ dag_fci = create_fci_dag("dag_fci", df, g_dot_str);
 gvplot(dag_fci)
 
 # ╔═╡ 55f9ca6b-1196-4b0f-928f-42c640d09477
-dag_pc = create_pc_dag("dag_pc", df, g_dot_str);
+dag_pc = create_pcalg_gauss_dag("dag_pc", df, g_dot_str);
 
 # ╔═╡ 4acdd821-3172-43a1-9f71-f862e74bea38
 gvplot(dag_pc)

diff --git a/notebooks/Maintenance/Notebook-to-reset-SR2StanPluto-jl-notebooks.jl b/notebooks/Maintenance/Notebook-to-reset-SR2StanPluto-jl-notebooks.jl
@@ -78,7 +78,6 @@ notebook_files = [
     "~/.julia/dev/SR2StanPluto/notebooks/SR2/09.4-Easy HMC.jl",
     "~/.julia/dev/SR2StanPluto/notebooks/SR2/09.5-Care of Markov chains.jl",
 
-    "~/.julia/dev/SR2StanPluto/notebooks/SR3/threads/Pluto_CI_v0.11.1_threads_test.jl",
     "~/.julia/dev/SR2StanPluto/notebooks/SR3/Adjustment explorations.jl",
     "~/.julia/dev/SR2StanPluto/notebooks/SR3/GES explorations.jl",
     "~/.julia/dev/SR2StanPluto/notebooks/SR3/Lecture_3_4.jl",

diff --git a/notebooks/SR2/06.2-Post-treatment bias.jl b/notebooks/SR2/06.2-Post-treatment bias.jl
@@ -282,7 +282,7 @@ end
 # ╔═╡ 2ddcd39f-6318-42cc-aad1-99c414536c7c
 let
 	g_dot_str = "DiGraph plant_2 {h0 -> h1; t -> f;}"
-	dag_plants_2 = create_pc_dag("dag_plants_2", df2, g_dot_str);
+	dag_plants_2 = create_pcalg_gauss_dag("dag_plants_2", df2, g_dot_str);
 	gvplot(dag_plants_2; title_g="Observed model")
 end