R: Stochastic Island Biogeography Theory Made Easy

# Stochastic Island Biogeography Theory Made Easy

## Documentation for package ‘island’ version 0.2.10

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

akaikeic | Akaike Information Criterion
akaikeicc | Akaike Information Criterion
all_environmental_fit | Environmental fit for a single dataset
alonso15 | Lakshadweep Archipelago coral fish community reassembly
cetotrans | From rates to probabilities
custom_environmental_fit | Environmental fit for a single dataset
data_generation | Data simulation of colonization-extinction dynamics
greedy_environmental_fit | Environmental fit for a single dataset
ibd_models | Inmigration, birth, death- models
idaho | Mapped plant community time series, Dubois, ID
irregular_multiple_datasets | c/e rates for irregular samplings in multiple datasets
irregular_single_dataset | c/e rates for irregular samplings in a dataset
lakshadweep | Lakshadweep Archipelago coral fish community reassembly data (expanded)
lakshadweepPLUS | Lakshadweep Archipelago coral fish community reassembly data in a single data frame
mss_cedp | Likelihood approach for estimating colonization/extinction with perfect or imperfect detectability
NLL_env | Environmental fit for a single dataset
NLL_imd | c/e rates for irregular samplings in multiple datasets
NLL_isd | c/e rates for irregular samplings in a dataset
NLL_rss | c/e rates for a regular sampling scheme
null_model | Model prediction error
PA_simulation | Data simulation of colonization-extinction dynamics
rates_calculator | Colonization and extinction rates calculator for expressions.
regular_sampling_scheme | c/e rates for a regular sampling scheme
r_squared | Model prediction error
simberloff | Simberloff and Wilson original defaunation experiment data
simulated_model | Model prediction error
sss_cedp | MacKenzie etal (2003) likelihood approach for estimating colonization/extinction parameters (with imperfect detectability)
upgma_model_selection | Model selection function based on a upgma grouping algorithm
weight_of_evidence | Weight of evidence