forestecology 0.2.1: Development version

forestecology 0.2.0: Ecology and Evolution

Added Ecology & Evolution paper to paper/ https://doi.org/10.1002/ece3.8129
Made choice of competitor explanatory variable more flexible by adding create_focal_vs_comp(comp_x_var = ...) argument.

Switched CI from travis to GitHub actions
Refactored spatial cross-validation in run_cv() to use purrr::map_dfr() using the fit_one_fold() function
Transition modeling, prediction, and plotting functions from generics to S3 methods for the comp_bayes_lm class.
Aligned inputs and outputs of modeling and prediction functions with S3 modeling conventions and tidy data principles. Namely,
- The comp_bayes_lm() modeling function takes in a data frame at a level of observation equivalent to that which the model is actually fit to: each row is a unique focal observation/tree rather than focal-competitor observation pairs/trees. The function outputs a model object with several associated modeling S3 methods.
- predict.comp_bayes_lm() takes in a model object as its first argument and input data as its second argument. The output, an unnamed vector, has length equal to the input data.
Added argument checks.

Completed “bad first draft” of paper on package itself, including Michigan Big Woods & SCBI running examples
Further refactoring of alpha-version of forestecology package code

Got Smithsonian Conservation Biology Institute (loaded as CSV’s directly from SCBI GitHub) example model working
Got Michigan Big Woods data (data from University of Michigan Deep Blue Data repository pre-loaded in package) example model working
Go toy example model working in README
Second pass at clean-up of package