Density and Abundance from Distance-Sampling Surveys
hazrate.start.limits - Start and limit values for hazrate distance fun...
Calculation of Hermite expansion for detection function likelihoods
intercept.only - Detect intercept-only distance function
is.points - Tests for point surveys
checkRdistDf - Check RdistDf data frames
is.smoothed - Tests for smoothed distance functions
is.Unitless - Test whether object is unitless
Likelihood parameter names
abundEstim - Distance Sampling Abundance Estimates
AIC.dfunc - AIC-related fit statistics for detection functions
autoDistSamp - Automated classical distance analysis
bcCI - Bias corrected bootstraps
checkNEvalPts - Check number of numeric integration intervals
checkUnits - Check for the presence of units
coef.dfunc - Coefficients of an estimated detection function
colorize - Add color to result if terminal accepts it
cosine.expansion - Cosine expansion terms
dE.multi - Estimate multiple-observer line-transect distance functions
dE.single - Estimate single-observer line-transect distance function
dfuncEstim - Estimate a distance-based detection function
dfuncEstimErrMessage - dfuncEstim error messages
distances - Observation distances
EDR - Effective Detection Radius (EDR) for point transects
effectiveDistance - Effective sampling distances
effort - Effort information
errDataUnk - Unknown error message
estimateN - Abundance point estimates
ESW - Effective Strip Width (ESW) for line transects
expansionTerms - Distance function expansion terms
groupSizes - Group Sizes
gxEstim - Estimate g(0) or g(x)
halfnorm.like - Half-normal distance function
halfnorm.start.limits - Start and limit values for halfnorm distance f...
hazrate.like - Hazard rate likelihood
lines.dfunc - Line plotting method for distance functions
maximize.g - Find coordinate of function maximum
mlEstimates - Distance function maximum likelihood estimates
model.matrix - Rdistance model matrix
nCovars - Number of covariates
negexp.like - Negative exponential likelihood
negexp.start.limits - Start and limit values for negexp distance funct...
nLL - Negative log likelihood of distances
observationType - Type of observations
oneBsIter - Computations for one bootstrap iteration
parseModel - Parse Rdistance model
Compute off-transect distances from sighting distances and angles
plot.dfunc.para - Plot parametric distance functions
plot.dfunc - Plot method for distance (detection) functions
predDensity - Density on transects
predDfuncs - Predict distance functions
predict.dfunc - Predict distance functions
predLikelihood - Distance function values at observations
Print abundance estimates
print.dfunc - Print method for distance function object
Rdistance - Distance Sampling Analyses for Abundance Estimation
Rdistance optimization control parameters.
RdistDf - Construct Rdistance nested data frames
Numeric second derivatives
Calculate simple polynomial expansion for detection function likelihoo...
startLimits - Distance function starting values and limits
Summarize abundance estimates
Summarize a distance function object
summary.rowwise_df - Summary method for Rdistance data frames
transectType - Type of transects
unnest - Unnest an RdistDf data frame
Distance-sampling (<doi:10.1007/978-3-319-19219-2>) estimates density and abundance of survey targets (e.g., animals) when detection probability declines with 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. Distance-sampling includes line-transect studies that measure observation distances as the closest approach of the sample route (transect) to the target (i.e., perpendicular off-transect distance), and point-transect studies that measure observation distances from stationary observers to the target (i.e., radial distance). The routines included here fit smooth (parametric) curves to histograms of observation distances and use those functions to compute effective sampling distances, density of targets in the surveyed area, and abundance of targets in a surrounding study area. Curve shapes include the half-normal, hazard rate, and negative exponential functions. Physical measurement units are required and used throughout to ensure density is reported correctly. The help files are extensive and have been vetted by multiple authors.
Useful links