NEWS
forestecology 0.2.1.9000
forestecology 0.2.1 (2025-08-27)
- Moved blockCV to SUGGESTS
- Other minor maintenance
forestecology 0.2.0 (2021-10-02)
- 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.
forestecology 0.1.0 (2021-03-12)
- 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.
forestecology 0.0.0.9003
- 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
forestecology 0.0.0.9002
- 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
forestecology 0.0.0.9001
forestecology 0.0.0.9000
- Launched alpha-version of
forestecology package