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https://github.com/gavinsimpson/coenocliner
An R package to simulate species occurences and abundances (counts) along gradients
https://github.com/gavinsimpson/coenocliner
Last synced: 23 days ago
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An R package to simulate species occurences and abundances (counts) along gradients
- Host: GitHub
- URL: https://github.com/gavinsimpson/coenocliner
- Owner: gavinsimpson
- License: gpl-2.0
- Created: 2014-03-15T23:32:12.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2021-02-08T19:34:59.000Z (over 3 years ago)
- Last Synced: 2024-10-07T05:08:43.650Z (about 1 month ago)
- Language: R
- Homepage:
- Size: 255 KB
- Stars: 12
- Watchers: 5
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
coenocliner
===========
#### Released version
[](https://cran.r-project.org/package=coenocliner) [](https://cran.r-project.org/package=coenocliner)
#### Build status
[](https://travis-ci.org/gavinsimpson/coenocliner) [](https://ci.appveyor.com/project/gavinsimpson/coenocliner/branch/master)
## What is coenocliner?
An R package to simulate species abundances (counts) along gradients
One of the key ways quantitative ecologists attempt to understand
the properties and behaviour of the methods they use or dream up is
through the use of simulated data. There are a number of computer
programmes for simulating ecological data along gradients, such as
Peter Minchin's COMPAS, but none (that I am aware of) that are
available for R on CRAN. Dave Robert's
[coenoflex](https://cran.r-project.org/package=coenoflex) package for
R would be a useful alternative but currently is archived on CRAN
because of some problems in the Fortran code underlying the package.
Rather than have to reinvent the wheel each time I wanted to simulate
some new data for a paper or to work on a new approach, I decided to
start my own R package to contain a range of simulators encapsulating
different response models, numbers of gradients, etc.
At the moment, coenocliner is limited in what it can do practically.
There is a single response model, the Gaussian response, which is a
symmetric model of the parameters; the optimum, tolerance and height
of the response curve. Count data can be generated from this model
from either a Poisson or negative binomial distribution, using the
parameterised Gaussian response as the expectation or mean of the
distribution.
Additional response models include:
1. The generalised beta response function
A further feature of **coenocliner** that I hope to develop is to
include simulation wrapper functions that replicate the simulation
methods used in research papers. A working example is `simJamil`,
which produces simlations from a Gaussian logit response following
the scheme described in Jamil & ter Braak (2013).
## Development
I would like to see coenocliner be as inclusive as possible; if you
have code to simulate ecological species or community data that is
just sitting around, consider adding it to coenocliner. In the
meantime, I'm happy just having something tangible for my own use
without having to remember the expressions for some of the response
models.
Currently coenocliner is licensed under the GPL v2, but I'm happy to
reconsider this if you want to contribute code under a more permissive
licence.
## Installation
No binary packages are currently available for coenocliner. If you
have the correct development tools you can compile the package
yourself after downloading the source code from github. Once I work
out how to link git with svn I'll start a project on
[R-forge](http://r-forge.r-project.org) which will host binary
packages of coenocliner.
If you use Hadley Wickham's **devtools** package then you
can install coenocliner directly from github using functions that
devtools provides. To do this, install **devtools** from CRAN via
install.packages("devtools")
then run
devtools::install_github("gavinsimpson/coenocliner")
### References
Jamil and ter Braak (2013) Generalized linear mixed models can
detect unimodal species-environment relationships. *PeerJ* **1:e95**;
[DOI 10.7717/peerj.95](http://doi.org/10.7717/peerj.95)