removing multiple precompilation

Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.10.0"
 manifest_format = "2.0"
-project_hash = "b63f305d0ed85d8bed38a9eb0d9764806b256538"
+project_hash = "e7cfeb6672a779b8a5adb0ecf46a10b9bba4c575"
 
 [[deps.ArgTools]]
 uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"

Project.toml

@@ -4,11 +4,14 @@ authors = ["Kieran Marray"]
 version = "0.1.0"
 
 [deps]
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 
 [compat]
-julia = "1"
 SpecialFunctions = "2.3"
+julia = "1"
+
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 

src/PowerLawSamplers.jl

@@ -1,11 +1,25 @@
 module PowerLawSamplers
 
-using SpecialFunctions
+using SpecialFunctions, LinearAlgebra, Random
 
 # Write your package code here.
-export doubling_up_sampler, bounded_doubling_up_sampler, inexact_sampler, pl_graph
 
 include("Samplers.jl")
-include("RandomGraph.jl")
+#include("RandomGraph.jl")
+
+function pl_graph(n, α, x_min) 
+    G = Matrix{Float64}(zeros(n, n)) 
+    us = rand(n)
+    for i in 1:n
+        #println(i)
+        neighbours = sample(1:n,  bounded_doubling_up_sampler(α, x_min, us[i], n); replace=false)
+        G[i,neighbours] .= 1.0
+    end
+
+    return(G)
+
+end
+
+export doubling_up_sampler, bounded_doubling_up_sampler, inexact_sampler, pl_graph
 
 end

src/RandomGraph.jl

@@ -1,14 +1 @@
-include("Samplers.jl")
 
-function pl_graph(n, α, x_min) 
-    G = Matrix{Float64}(zeros(n, n)) 
-    us = rand(n)
-    for i in 1:n
-        #println(i)
-        neighbours = sample(1:n,  bounded_double_up_sampler(α, x_min, us[i], n); replace=false)
-        G[i,neighbours] .= 1.0
-    end
-
-    return(G)
-
-end

src/Samplers.jl

@@ -1,6 +1,3 @@
-using SpecialFunctions
-
-
 """
 Exact sampler for observations from discrete power law distribution from Appendix D of Clauset, Shalizi, and Newman -
 power law distributions in empirical data.
@@ -25,7 +22,7 @@ function doubling_up_sampler(α, x_min,u)
     while zeta(α, guess)/zeta(α, x_min) ≥ 1-u 
         guess += 1
     end
-    return(guess)
+    return(Int64(guess))
 end
 
 """