README.md

In cjcarlson/codependent: Bipartite network rarefaction for affiliate species richness

:bee: codependent :blossom:

An R package for network-based estimation of affiliate species richness

An R package for estimating affiliate species richness based on power law scaling with host diversity, using rarefaction on bipartite species association networks. Because some things just go together.

Use the function copredict to extrapolate power law curves out to a higher value. Use copredict.ci to fit a series of models to only half of the total curve, and see what happens (for an overestimated confidence bound).

Author

Colin J. Carlson ([e-mail])

Contributions by Tad Dallas (LSU) and Laura Ward Alexander (UC Berkeley)

Installing the package

Install directly from Github:

``` {r, setup, echo = FALSE, message = FALSE}

If you don't have devtools:

install.packages("devtools")

devtools::install_github("cjcarlson/codependent")

What can you do with codependent?
----------------------

Load the package

``` {r dataset, message=FALSE}
library(codependent)

You can build and fit nested curves using binera, for bipartite network rarefaction:

``` {r dataset, message=FALSE} data(rob1929) head(rob1929) binera(rob1929, iter=10, plots=TRUE)

You can EXTRAPOLATE to a higher number of hosts using `copredict`:

``` {r dataset, message=FALSE}
copredict(rob1929, n.indep=500, iter=10, plot=TRUE)

Multigroup extrapolation is coming soon!

Coextinction curves

Coextinction curves in the style of Koh et al., 2004 are done with coextinct:

{r dataset, message=FALSE} coextinct(rob1929, iter=10, plots=TRUE)

I'll be adding new functionality for that part of the package soon.

cjcarlson/codependent documentation built on July 26, 2019, 2:56 a.m.