Learn R Programming

DSpat (version 0.1.6)

Spatial Modelling for Distance Sampling Data

Description

Fits inhomogeneous Poisson process spatial models to line transect sampling data and provides estimate of abundance within a region.

Copy Link

Version

Install

install.packages('DSpat')

Monthly Downloads

45

Version

0.1.6

License

GPL (>= 2)

Maintainer

Jeff Laake

Last Published

December 12th, 2014

Functions in DSpat (0.1.6)

DSpat.covariates

Raster covariates study area
integrate.intensity

Integrated intensity of fitted model
dist2line

Compute perpendicular distances and projections onto line
lgcp.correction

Calculate Overdispersion factor for IPP fit via Monte Carlo Integration
sample.points

Sample points within each transect and filter with specified detection function
create.points.by.offset

Create point dataframe offset from line
quadscheme.lt

Create line transect quadrature for spatstat
transect.intensity

Compute expected and observed counts by distance within transect
create.covariate.images

Create a list of covariate images
weeds.lines

Transect lines from Dubbo weed data
LTDataFrame

Creates covariate dataframes
weeds.obs

Observations from Dubbo weed data
dspat

Fits spatial model to distance sampling data
Internal

Internal DSpat functions
project2line

Project points onto line
simCovariates

Simulates covariates for an example in DSpat
simPts

Simulates point process on a rectangular grid
offset.points

Offset points from the line to actual position
weeds.covariates

Covariate grid for Dubbo weed data
DSpat.lines

Example DSpat lines dataframe
DSpat.obs

Observation dataframe for DSpat
create.lines

Create a systematic sample of parallel lines across a grid
simDSpat

Simulate a distance sample from a specified spatial point process
DSpat-package

Spatial modelling package for distance sampling data
lines_to_strips

Convert lines to transects (strips)
weeds

Dubbo weed data
weeds.all

Dubbo weed data with constructed y-coordinate