DSpat: Spatial Modelling for Distance Sampling Data

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

AuthorDevin Johnson, Jeff Laake, Jay VerHoef
Date of publication2014-12-12 14:39:42
MaintainerJeff Laake <Jeff.Laake@noaa.gov>
LicenseGPL (>= 2)

View on CRAN

Man pages

create.covariate.images: Create a list of covariate images

create.lines: Create a systematic sample of parallel lines across a grid

create.points.by.offset: Create point dataframe offset from line

dist2line: Compute perpendicular distances and projections onto line

dspat: Fits spatial model to distance sampling data

DSpat.covariates: Raster covariates study area

DSpat.lines: Example DSpat lines dataframe

DSpat.obs: Observation dataframe for DSpat

DSpat-package: Spatial modelling package for distance sampling data

integrate.intensity: Integrated intensity of fitted model

internal: Internal DSpat functions

lgcp.correction: Calculate Overdispersion factor for IPP fit via Monte Carlo...

lines.to.strips: Convert lines to transects (strips)

LTDataFrame: Creates covariate dataframes

offset.points: Offset points from the line to actual position

project2line: Project points onto line

quadscheme.lt: Create line transect quadrature for spatstat

sample.points: Sample points within each transect and filter with specified...

simCovariates: Simulates covariates for an example in DSpat

simDSpat: Simulate a distance sample from a specified spatial point...

simPts: Simulates point process on a rectangular grid

transect.intensity: Compute expected and observed counts by distance within...

weeds: Dubbo weed data

weeds.all: Dubbo weed data with constructed y-coordinate

weeds.covariates: Covariate grid for Dubbo weed data

weeds.lines: Transect lines from Dubbo weed data

weeds.obs: Observations from Dubbo weed data

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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