Package: forestecology 0.2.0.9000

forestecology: Fitting and Assessing Neighborhood Models of the Effect of Interspecific Competition on the Growth of Trees

Code for fitting and assessing models for the growth of trees. In particular for the Bayesian neighborhood competition linear regression model of Allen (2020): methods for model fitting and generating fitted/predicted values, evaluating the effect of competitor species identity using permutation tests, and evaluating model performance using spatial cross-validation.

Authors:Albert Y. Kim [aut, cre], David Allen [aut], Simon Couch [aut]

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forestecology.pdf |forestecology.html✨
forestecology/json (API)
NEWS

# Install 'forestecology' in R:

install.packages('forestecology', repos = c('https://rudeboybert.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rudeboybert/forestecology/issues

Datasets:

blocks_ex - Example cross validation grid
census_1_ex - Example input census data for package use
census_2008_bw - Michigan Big Woods research plot data
census_2014_bw - Michigan Big Woods research plot data
census_2_ex - Example input census data for package use
comp_bayes_lm_ex - Example bayesian competition model fit
focal_vs_comp_ex - Example focal versus comp data frame
growth_ex - Example growth data frame for small example
growth_spatial_ex - Example growth data frame with spatial data for small example
growth_toy - Example input data for 'create_focal_vs_comp()'
species_bw - Phylogenic groupings and trait based clustering of various tree species
study_region_bw - Bigwoods forest study region boundary
study_region_ex - Study region for example data

On CRAN:

forestecology tidyverse

13 exports 11 stars 1.65 score 59 dependencies 11 scripts 289 downloads

Last updated 3 years agofrom:cbae4d9715. Checks:OK: 1 NOTE: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 04 2024
R-4.5-win	NOTE	Sep 04 2024
R-4.5-linux	NOTE	Sep 04 2024
R-4.4-win	NOTE	Sep 04 2024
R-4.4-mac	NOTE	Sep 04 2024
R-4.3-win	NOTE	Sep 04 2024
R-4.3-mac	NOTE	Sep 04 2024

Exports:%>%add_buffer_variable autoplot comp_bayes_lm compute_buffer_region compute_growth create_bayes_lm_data create_focal_vs_comp focal_vs_comp_distance rmse run_cv sf_polygon to_any_case

Dependencies:BH blockCV class classInt cli colorspace cpp11 DBI dplyr e1071 fansi farver forcats generics geometries ggplot2 ggridges glue gtable hardhat isoband KernSmooth labeling lattice lifecycle magrittr MASS Matrix mgcv munsell mvnfast nlme patchwork pillar pkgconfig proxy purrr R6 RColorBrewer Rcpp RcppArmadillo rlang s2 scales sf sfheaders snakecase stringi stringr tibble tidyr tidyselect units utf8 vctrs viridisLite withr wk yardstick

The forestecology R package for fitting and assessing neighborhood models of the effect of interspecific competition on the growth of trees

Rendered fromforestecology.Rmdusingknitr::rmarkdownon Sep 04 2024.

Last update: 2021-08-04
Started: 2021-03-11

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Identify trees in the buffer region	add_buffer_variable
Plot Bayesian model parameters	autoplot.comp_bayes_lm
Example cross validation grid	blocks_ex
Example input census data for package use	census_1_ex
Example input census data for package use	census_2_ex
Michigan Big Woods research plot data	census_2008_bw
Michigan Big Woods research plot data	census_2014_bw
Fit Bayesian competition model	comp_bayes_lm
Example bayesian competition model fit	comp_bayes_lm_ex
Compute buffer to a region.	compute_buffer_region
Compute growth of trees	compute_growth
Create input data frame for Bayesian regression	create_bayes_lm_data
Create focal versus competitor trees data frame	create_focal_vs_comp
Return all pairwise distances between two data frames of trees	focal_vs_comp_distance
Example focal versus comp data frame	focal_vs_comp_ex
'forestecology' package	forestecology
Example growth data frame for small example	growth_ex
Example growth data frame with spatial data for small example	growth_spatial_ex
Example input data for 'create_focal_vs_comp()'	growth_toy
Make predictions based on fitted Bayesian model	predict.comp_bayes_lm
Run the bayesian model with spatial cross validation	run_cv
Phylogenic groupings and trait based clustering of various tree species	species_bw
Bigwoods forest study region boundary	study_region_bw
Study region for example data	study_region_ex