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

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Documentation for package ‘forestecology’ version 0.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

add_buffer_variable	Identify trees in the buffer region
autoplot.comp_bayes_lm	Plot Bayesian model parameters
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
compute_buffer_region	Compute buffer to a region.
compute_growth	Compute growth of trees
comp_bayes_lm	Fit Bayesian competition model
comp_bayes_lm_ex	Example bayesian competition model fit
create_bayes_lm_data	Create input data frame for Bayesian regression
create_focal_vs_comp	Create focal versus competitor trees data frame
focal_vs_comp_distance	Return all pairwise distances between two data frames of trees
focal_vs_comp_ex	Example focal versus comp data frame
forestecology	'forestecology' package
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()'
predict.comp_bayes_lm	Make predictions based on fitted Bayesian model
run_cv	Run the bayesian model with spatial cross validation
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