CoexistenceHoles.jl Documentation

Quick Overview

CoexistenceHoles is a julia and R package that originally was made for the project "Coexistence holes characterize the assembly and disassembly of multispecies sytems". However, the package has the potential for a variety of applications. In short, it provides efficient tools for analyzing the homology of general hypergraphs.

Installation

Julia

This package is not registered (yet). You can install it via the Julia REPL like this:

julia> using Pkg
julia> Pkg.add(PackageSpec(url="https://github.com/SyntheticDynamics/CoexistenceHoles.jl.git", rev="master"))

Or you can install it via the Pkg REPL like this:

(v1.3) pkg> add https://github.com/SyntheticDynamics/CoexistenceHoles.jl.git#master

R

This package can also be used with R. With R installed, you'll still need to download install julia, although you won't be using it directly. You can check if julia is installed correctly by running the julia command in a terminal. If this command is not found, you will need to add it to your path following the proper instructions for your operating system.

In R we use JuliaCall as an interface to access the julia code. There are many ways to access the package with R. Here are someuseful ones, or just keep reading to get started right away. For more in-depth function summaries take a look at the official documentation.

The follwoing are steps to install CoexistenceHoles in R. See the examples or tutorials for more specific instructions.

install.packages("JuliaCall")

library(JuliaCall)
julia <- julia_setup()

# only need to run this once
julia_install_package("https://github.com/SyntheticDynamics/CoexistenceHoles.jl.git#master")

# add the library every time you open a new session of R and want to use CoexistenceHoles
julia_library("CoexistenceHoles")

Quick Example

Julia

using CoexistenceHoles

N = 8 # number of species in our ecosystem

# create a random community matrix
σA = 0.1 # standard devation for entries
C = 0.1 # success rate of Bernoulli distribution used to populate matrix
A = random_communitymatrix(N, σA, C)

# create a random growth vector
μ = 0.3 # mean of LogNormal distribution used to generate each value
σr = 0.2 # standard deviation of LogNormal distribution used to generate each value
r = random_growthvector(N, μ, σr)


reg = 0
max_dim  = 4
H = assembly_hypergraph_GLV(A, r; method = "localstability", regularization = reg)
R = disassembly_hypergraph(H)

# maybe save these for later if you want
save_hypergraph_dat("~/hypergraphs/assembly_hypergraph.dat", H)
save_hypergraph_dat("~/hypergraphs/disassembly_hypergraph.dat", R)

# get the betti numbers
betti_H = betti_hypergraph_ripscomplex(H; max_dim = max_dim)

R

julia_library("CoexistenceHoles")

opt <- julia_pkg_import("CoexistenceHoles", func_list = c("random_communitymatrix",
                                                           "random_growthvector",
                                                           "assembly_hypergraph_GLV",
                                                           "dissassembly_hypergraph",
                                                           "save_hypergraph_dat"))
N = 8 # number of species in our ecosystem

# create a random community matrix
sA = 0.1 # standard deviation for community matrix
C = 0.1 # success rate of Bernoulli distribution used to populate matrix
A = opt$random_communitymatrix(N, sA, C)

# create a random growth vector
mr = 0.1
sr = 0.1
r = opt$random_growthvector(N, mr, sr)

# create assembly and disassembly hypergraph
reg = 0
H = opt$assembly_hypergraph_GLV(A,R; method="localstability", regularization=reg)
M = opt$disassembly_hypergraph(H)

# save these for later if you want
save_hypergraph_dat("~/hypergraphs/assembly_hypergraph.dat", H)
save_hypergraph_dat("~/hypergraphs/disassembly_hypergraph.dat", R)

# get the betti numbers
betti_H = betti_hypergraph_ripscomplex(H; max_dim = 4)

Citing

If you use CoexistenceHoles for academic research, please cite the following paper.

Paper Citation

Developers

• Marco Tulio
• Aaron Kelley