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Rdistance (version 4.1.1)

Density and Abundance from Distance-Sampling Surveys

Description

Distance-sampling () is a field survey and analytical method that estimates density and abundance of survey targets (e.g., animals) when detection probability declines with observation distance. Distance-sampling is popular in ecology, especially when survey targets are observed from aerial platforms (e.g., airplane or drone), surface vessels (e.g., boat or truck), or along walking transects. Analysis involves fitting smooth (parametric) curves to histograms of observation distances and using those functions to adjust density estimates for missed targets. Routines included here fit curves to observation distance histograms, estimate effective sampling area, density of targets in surveyed areas, and the abundance of targets in a surrounding study area. Confidence interval estimation uses built-in bootstrap resampling. Help files are extensive and have been vetted by multiple authors. Many tutorials are available on the package's website (URL below).

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Version

Install

install.packages('Rdistance')

Monthly Downloads

439

Version

4.1.1

License

GNU General Public License

Maintainer

Trent McDonald

Last Published

December 3rd, 2025

Functions in Rdistance (4.1.1)

cosine.expansion

Cosine expansion terms
checkNEvalPts

Check number of numeric integration intervals
bspline.expansion

B-spline expansion terms
bcCI

Bias corrected bootstraps
dE.single

Estimate single-observer line-transect distance function
colorize

Add color to result if terminal accepts it
coef.dfunc

Coefficients of an estimated detection function
checkUnits

Check for the presence of units
dE.multi

Estimate multiple-observer line-transect distance functions
autoDistSamp

Automated classical distance analysis
differentiableLikelihoods

Differentiable likelihoods in Rdistance
errDataUnk

Unknown error message
estimateN

Abundance point estimates
dfuncEstimErrMessage

dfuncEstim error messages
distances

Observation distances
expansionTerms

Distance function expansion terms
effectiveDistance

Effective sampling distances
groupSizes

Group Sizes
dfuncEstim

Estimate a distance-based detection function
effort

Effort information
insertOneStepBreaks

Insert oneStep Likelihood breaks
integrateHalfnormLines

Integrate Half-normal line surveys
halfnorm.start.limits

Start and limit values for halfnorm distance function
hazrate.start.limits

Start and limit values for hazrate distance function
integrateHazrateLines

Integrate Hazard-rate line survey distance functions
hazrate.like

Hazard rate likelihood
hermite.expansion

Hermite expansion factors
gxEstim

Estimate g(0) or g(x)
integrateHalfnormPoints

Integrate Half-normal Point transects
halfnorm.like

Half-normal distance function
integrateNegexpLines

Integrate Negative exponential
is.Unitless

Test whether object is unitless
integrateKey

Compute and print distance function integration
intercept.only

Detect intercept-only distance function
integrateOneStepNumeric

Numeric Integration of One-step Function
integrateOneStepPoints

Integrate Point-survey One-step function
is.RdistDf

Check RdistDf data frames
integrateNegexpPoints

Integrate Negative exponential point surveys
integrateNumeric

Numeric Integration
integrateOneStepLines

Integrate Line-transect One-step function
model.matrix.dfunc

Rdistance model matrix
lines.dfunc

lines.dfunc - Line plotting method for distance functions
negexp.like

Negative exponential likelihood
likeParamNames

Likelihood parameter names
is.smoothed

Tests for smoothed distance functions
nLL

Negative log likelihood of distances
maximize.g

Find coordinate of function maximum
mlEstimates

Distance function maximum likelihood estimates
negexp.start.limits

Start and limit values for negexp distance function
nCovars

Number of covariates
is.points

Tests for point surveys
oneStep.start.limits

oneStep likelihood start and limit values
plot.dfunc.para

Plot parametric distance functions
perpDists

Compute off-transect distances from sighting distances and angles
oneBsIter

Computations for one bootstrap iteration
oneStep.like

Mixture of two uniforms likelihood
parseModel

Parse Rdistance model
observationType

Type of observations
plot.dfunc

Plot method for distance (detection) functions
predDensity

Density on transects
predict.dfunc

Predict distance functions
simple.expansion

Simple polynomial expansion factors
predDfuncs

Predict distance functions
predLikelihood

Distance function values at observations
print.dfunc

Print method for distance function object
print.abund

Print abundance estimates
sine.expansion

Sine expansion terms
sparrowDetectionData

Brewer's Sparrow detection data
simpsonCoefs

Simpson numerical integration coefficients
secondDeriv

Numeric second derivatives
summary.abund

Summarize abundance estimates
sparrowDf

Brewer's Sparrow detection data frame in Rdistance >4.0.0 format.
sparrowDfuncObserver

Brewer's Sparrow detection function
thrasherSiteData

Sage Thrasher site data.
thrasherDf

Sage Thrasher detection data frame in Rdistance >4.0.0 format
sparrowSiteData

Brewer's Sparrow site data
startLimits

Distance function starting values and limits
thrasherDetectionData

Sage Thrasher detection data
transectType

Type of transects
%#%

Unit assignment helpers
summary.rowwise_df

Summary method for Rdistance data frames
unnest

Unnest an RdistDf data frame
summary.dfunc

Summarize a distance function object
varcovarEstim

Estimate variance-covariance
abundEstim

Distance Sampling Abundance Estimates
Nlminb

'nlminb' optimizer
Optim

'optim' optimizer
AIC.dfunc

AIC-related fit statistics for detection functions
ESW

Effective Strip Width (ESW) for line transects
HookeJeeves

'nlminb' optimizer
RdistanceControls

Rdistance optimization control parameters.
Rdistance-package

Rdistance - Distance Sampling Analyses for Abundance Estimation
EDR

Effective Detection Radius (EDR) for point transects
RdistDf

Construct Rdistance nested data frames