🔧 dependency to Statistics + update tests

Manifest.toml

@@ -79,10 +79,10 @@ deps = ["Base64"]
 uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
 
 [[Missings]]
-deps = ["Dates", "InteractiveUtils", "SparseArrays", "Test"]
-git-tree-sha1 = "d1d2585677f2bd93a97cfeb8faa7a0de0f982042"
+deps = ["SparseArrays", "Test"]
+git-tree-sha1 = "f0719736664b4358aa9ec173077d4285775f8007"
 uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
-version = "0.4.0"
+version = "0.4.1"
 
 [[Mmap]]
 uuid = "a63ad114-7e13-5084-954f-fe012c677804"

Project.toml

@@ -11,6 +11,7 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 

docs/src/properties/resilience.md

@@ -40,13 +40,10 @@ with
 ## Available functions
 
 ```@docs
-s_in
-s_out
-s_mean
+s
 σ_in
 σ_out
 symmetry
 heterogeneity
-βeff
 resilience
 ```

src/EcologicalNetworks.jl

@@ -110,7 +110,7 @@ export AJS, EAJS
 
 # Overlap
 include(joinpath(".", "community/resilience.jl"))
-export βeff, resilience
+export resilience
 export symmetry, heterogeneity
 export s
 export σ_in, σ_out

src/community/resilience.jl

@@ -14,10 +14,10 @@ density of interactions.
 If dims is provided, the incoming (`dims=1`) or outgoing (`dims=2`) is computed.
 """
 function s(N::AbstractUnipartiteNetwork; dims::Union{Nothing,Integer}=nothing)
-    dims == 1 && return sum(N, dims=2)
-    dims == 2 && return sum(N, dims=1)'
+    dims == 1 && return sum(N.A, dims=2)
+    dims == 2 && return sum(N.A, dims=1)'
     if dims == nothing
-        return sum(N) / size(N)[1]
+        return sum(N.A) / size(N)[1]
     end
 end
 #=
@@ -43,15 +43,15 @@ s_out(N::AbstractUnipartiteNetwork) = sum(N.A, dims=1)'
 Computes the standard deviation of the ingoing weighted degree of an unipartite
 network.
 """
-σ_in(N::AbstractUnipartiteNetwork) = std(s_in(N), corrected=false)
+σ_in(N::AbstractUnipartiteNetwork) = std(s(N, dims=1), corrected=false)
 
 """
     σ_out(N::AbstractUnipartiteNetwork)
 
 Computes the standard deviation of the outgoing weighted degree of an unipartite
 network.
 """
-σ_out(N::AbstractUnipartiteNetwork) = std(s_out(N), corrected=false)
+σ_out(N::AbstractUnipartiteNetwork) = std(s(N, dims=2), corrected=false)
 
 """
     symmetry(N::AbstractUnipartiteNetwork)
@@ -68,7 +68,7 @@ to be prey would have a negative symmetry.
 > Nature 530(7590), 307-312. doi:10.1038/nature16948
 
 """
-symmetry(N::AbstractUnipartiteNetwork) = (mean(s_in(N) .* s_out(N)) - mean(s_in(N)) * mean(s_out(N))) / (σ_in(N) * σ_out(N))
+symmetry(N::AbstractUnipartiteNetwork) = (mean(s(N, dims=1) .* s(N, dims=2)) - mean(s(N, dims=1)) * mean(s(N, dims=2))) / (σ_in(N) * σ_out(N))
 
 """
     heterogeneity(N::AbstractUnipartiteNetwork)
@@ -82,25 +82,7 @@ have the same (weighted) in- and outdegrees.
 > Nature 530(7590), 307-312. doi:10.1038/nature16948
 
 """
-heterogeneity(N::AbstractUnipartiteNetwork) = (σ_in(N) * σ_out(N)) / s_mean(N)
-
-"""
-    βeff(N::AbstractUnipartiteNetwork)
-
-A resilience parameters described by Gao et al. (2016). It is a global parameters
-describing the dynamics of an unipartite network as an effective 1D equation of
-the form
-
-f(xeff) = F(xeff) + βeff G(xeff, xeff)
-
-i.e. describing a second-order term representing the effect of the network on the
-dynamics of the 'effective state' xeff of the system.
-
-> Goa, J., Barzael, B. and Barabási 2016. Universal resilience patterns in complex networks.
-> Nature 530(7590), 307-312. doi:10.1038/nature16948
-
-"""
-βeff(N::AbstractUnipartiteNetwork) = dot(s_in(N), s_out(N)) / sum(N.A)
+heterogeneity(N::AbstractUnipartiteNetwork) = (σ_in(N) * σ_out(N)) / s(N)
 
 """
     resilience(N::AbstractUnipartiteNetwork)
@@ -118,4 +100,4 @@ dynamics of the 'effective state' xeff of the system.
 > Nature 530(7590), 307-312. doi:10.1038/nature16948
 
 """
-resilience(N::AbstractUnipartiteNetwork) = βeff(N)
+resilience(N::AbstractUnipartiteNetwork) = dot(s(N, dims=1), s(N, dims=2)) / sum(N)

test/community/resilience.jl

@@ -4,11 +4,11 @@ module TestResilience
     A = rand([0, 1, 0.5], 10, 10)
     N = UnipartiteQuantitativeNetwork(A)
 
-    @assert s_mean(N) ≈ sum(A) / 10
-    @assert s_in(N) ≈ sum(A, dims=2)
-    @assert s_out(N) ≈ sum(A, dims=1)'
+    @assert s(N) ≈ sum(A) / 10
+    @assert s(N, dims=1) ≈ sum(A, dims=2)
+    @assert s(N, dims=2) ≈ sum(A, dims=1)'
 
-    @assert βeff(N) ≈ s_mean(N) + symmetry(N) * heterogeneity(N)
+    @assert resilience(N) ≈ s(N) + symmetry(N) * heterogeneity(N)
 
     # species only interact with themselves
     Nid = UnipartiteNetwork([true false false false;