Inferring the structure of bipartite (e.g. pollination, frugivory, or herbivory) networks from field (observational) data is a challenging task. Interaction data are hard to collect and require typically large sampling efforts, particularly to characterize infrequent interactions.
Slides discussing variable selection, information criteria (AIC, BIC, DIC), and other statistical modelling issues, with a focus on modelling ecological data.
Some notes on statistical model selection and comparison, balanced model complexity and predictive accuracy. An overview of different information criteria (AIC, BIC, DIC, WAIC) and cross validation. Mostly taken from Gelman et al.'s recent paper: …
It has long been acknowledged that the set of methods, standards and hypotheses (a.k.a. paradigm) that guide research in any scientific discipline strongly influences their ability to make progress. While new methodologies or theories can boost new …
My very first R package, from 2012!