This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.

Computes a novel metric of affinity between two entities based on their co-occurrence 
  (using binary presence/absence data). The metric and its MLE, alpha hat, were advanced in 
  Mainali, Slud, et al, 2021 <doi:10.1126/sciadv.abj9204>. Various types of confidence intervals and median interval
  were developed in Mainali and Slud, 2022 <doi:10.1101/2022.11.01.514801>.

Generate project files and directories following a pre-made template.
  You can specify variables to customize file names and content, and
  flexibly adapt the template to your needs. 'cookiecutter' for 'R'
  implements a subset of the excellent 'cookiecutter' package for the
  'Python' programming language (<https://github.com/cookiecutter/>), and
  aims to be largely compatible with the original 'cookiecutter' template
  format.

A convenient tool to store and format browser cookies and use them 
    in 'HTTP' requests (for example, through 'httr2', 'httr' or 'curl').

Browser cookies are name-value pairs that are saved in a
    user's browser by a website. Cookies allow websites to persist
    information about the user and their use of the website. Here we
    provide tools for working with cookies in 'shiny' apps, in part by
    wrapping the 'js-cookie' JavaScript library
    <https://github.com/js-cookie/js-cookie>.

Implementing the computational phase of the Causes of Outcome Learning approach as described in Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology <doi:10.1093/ije/dyac078>. The optional 'ggtree' package can be obtained through Bioconductor.