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dsm (version 2.1.3)

Density surface modelling of distance sampling data

Description

This library implements density surface modelling of line transect data, based on the methods of Hedley et al. (2004). Some recent developments in the literature have also be incorporated. Outputs are point and interval estimates of population abundance and density. Please note that this version of dsm WILL NOT work with the Windows package DISTANCE. For a version that works with DISTANCE, please go to https://github.com/lenthomas/dsm-distance-6.1. For the latest version of dsm, please use the github version at the URL listed below. Miller, D. L., M. L. Burt, E. Rexstad and L. Thomas. 2013. Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution Hedley, S.L., S.T. Buckland and D.L. Borchers. 2004. "Spatial distance sampling methods" pp 48-70 in Advanced Distance Sampling, Buckland, S.T. et al. (eds). Oxford University Press.

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Install

install.packages('dsm')

Monthly Downloads

1,037

Version

2.1.3

License

GPL (>= 2)

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Maintainer

David Lawrence Miller

Last Published

August 20th, 2013

Functions in dsm (2.1.3)

check.cols

Check column names exist
plot.dsm

Plot a density surface model.
plot.dsm.var

Create plots of abundance uncertainty
dsm.var.gam

Variance estimation via Bayesian results
summary.dsm.var

Summarize the variance of a density surface model
mexdolphins

Pan-tropical spotted dolphins
dsm-package

Density surface modelling
matrixnotposdef.handler

Handler to suppress the "matrix not positive definite" warning
print.dsm.var

Print a description of a density surface model variance object
summary.dsm

Summarize a fitted density surface model
print.dsm

Print a description of a density surface model object
dsm-data

Data format for DSM
block.info.per.su

Find the block information
make.soapgrid

Create a knot grid for the internal part of a soap film smoother.
dsm

Fit a density surface model to segment-specific estimates of abundance or density.
dsm.var.movblk

Variance estimation via parametric moving block bootstrap
offsets

Offsets
generate.ds.uncertainty

Generate data from a fitted detection function
latlong2km

Convert latitude and longitude to Northings and Eastings
generate.mb.sample

Generate a vector of residuals to be mapped back onto the data
predict.dsm

Predict from a fitted density surface model
dsm.var.prop

Variance propogation for DSM models
print.summary.dsm.var

Print summary of density surface model variance object
dsm.cor

Check for autocorrelation in residuals
trim.var

Trimmed variance