A simple way of fitting detection functions to distance sampling data for both line and point transects. Adjustment term selection, left and right truncation as well as monotonicity constraints and binning are supported. Abundance and density estimates can also be calculated (via a Horvitz-Thompson-like estimator) if survey area information is provided.
|Author||David Lawrence Miller|
|Date of publication||2016-08-10 22:41:57|
|Maintainer||Laura Marshall <firstname.lastname@example.org>|
|License||GPL (>= 2)|
AIC.dsmodel: Akaike's An Information Criterion for detection functions
amakihi: Amakihi (Hemignathus virens) point transect data
checkdata: Check that the data supplied to 'ds' is correct
create.bins: Create bins from a set of binned distances and a set of...
Distance-package: Distance sampling
ds: Fit detection functions and calculate abundance from line or...
ds.gof: Goodness of fit tests for distance sampling models
flatfile: The flatfile data format
gof_ds: Goodness of fit testing and quantile-quantile plots
logLik.dsmodel: log-likelihood value for a fitted detection function
minke: Simulated minke whale data
plot.dsmodel: Plot a fitted detection function
print.dsmodel: Simple pretty printer for distance sampling analyses
print.summary.dsmodel: Print summary of distance detection function model object
summarize_ds_models: Make a table of summary statistics for detection function...
summary.dsmodel: Summary of distance sampling analysis