# CoexistenceHoles

See full documentation here, including tutorials and examples.

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
If you already have `R`

installed then you’ll need
to download install `julia`

. 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`

use `JuliaCall`

is used to interface between languages. For function summaries see this document.
However studying these functions is not necessary since `CoexistenceHoles`

’s
shows the proper functions to use from `JuliaCall`

in the tutorial and examples.

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")
```

## Example code for both `Julia`

and `R`

<table width=100%>

Julia R ```julia using CoexistenceHoles N = 8 # number of species in our ecosystem # create a random community matrix σA = 0.1 # standard deviation 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) # create assembly and disassembly hypergraph reg = 0 H = assembly_hypergraph_GLV(A, r; method = "localstability", regularization = reg) R = 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 = 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 max_dim = 4 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 = max_dim) ```</table>

## Citing

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

Paper Citation

## Developers

- Marco Tulio
- Aaron Kelley