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mrds - Mark-Recapture Distance Sampling

What is `mrds`?

This package for R analyzes single or double observer distance sampling data for line or point sampling. It is used in program DISTANCE as one of the analysis engines. Supported double observer configurations include independent, trial and removal. Not all options in mrds are fully supported via DISTANCE.

If you only wish to perform a conventional or multiple covariate distance sampling analysis (CDS/MCDS) (as opposed to a double observer analysis), you may want to try the Distance R package, which has a simplified interface and is available from https://github.com/DistanceDevelopment/Distance.

Getting `mrds`

The easiest way to ensure you have the latest version of mrds, is to install using the remotes package:

  install.packages("remotes")

then install mrds from github:

  library(remotes)
  install_github("DistanceDevelopment/mrds")

Otherwise:

One can download a Windows package binary using the "Releases" tab in github. To install in R, from the R menu, use "Packages\Install from Local Zip file" and browse to location of downloaded zip.
Or, download package source files.
Finally the current stable version of mrds is available on CRAN, though this may be up to a month out of date due to CRAN policy.

References

The following are references for the methods used in the package.

Burt, M. L., D. L. Borchers, K. J. Jenkins and T. A. Marques. (2014). "Using mark-recapture distance sampling methods on line transect surveys." Methods in Ecology and Evolution 5: 1180-1191.

Buckland, S. T., J. Laake, et al. (2010). "Double observer line transect methods: levels of independence." Biometrics 66: 169-177.

Borchers, D. L., J. L. Laake, et al. (2006). "Accommodating unmodeled heterogeneity in double-observer distance sampling surveys." Biometrics 62(2): 372-378.

Buckland, S. T., D. R. Anderson, et al., Eds. (2004). Advanced distance sampling: estimating abundance of biological populations. Oxford, UK; New York, Oxford University Press. (see chapter 6).

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Install

install.packages('mrds')

Monthly Downloads

1,385

Version

3.0.1

License

GPL (>= 2)

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Repository

https://github.com/DistanceDevelopment/mrds/

Maintainer

Laura Marshall

Last Published

July 5th, 2025

Functions in mrds (3.0.1)

assign.default.values

Assign default values to list elements that have not been already assigned

Find se of average p and N

Cumulative distribution function (cdf) for fitted distance sampling detection function

Golf tee data used in chapter 6 of Advanced Distance Sampling examples

Create bins from a set of binned distances and a set of cutpoints.

Extraction and assignment of parameters to vector

covered.region.dht

Covered region estimate of abundance from Horvitz-Thompson-like estimator

Check parameters bounds during optimisations

Check that a detection function is monotone

create.command.file

create.command.file

Mark-Recapture Distance Sampling (MRDS) Removal - FI

Mark-Recapture Distance Sampling (MRDS) Trial Configuration - PI

Extract coefficients

Create detection function object

Goodness of fit tests for distance sampling models

CDS function definition

Distance Detection Function Fitting

Observation detection tables

Gradient of the negative log likelihood function

CDS/MCDS Distance Detection Function Fitting

Compute value of p(0) using a logit formulation

Fit detection function using key-adjustment functions

create.model.frame

Create a model frame for ddf fitting

Horvitz-Thompson estimates 1/p_i or s_i/p_i

Variance and confidence intervals for density and abundance estimates

Density and abundance estimates and variances

Mark-Recapture Distance Sampling (MRDS) Removal - PI

Fit detection function using key-adjustment functions

integratelogistic.analytic

Analytically integrate logistic detection function

Mark-Recapture Analysis of Trial Configuration - FI

Mark-Recapture Distance Sampling (MRDS) IO - FI

Mark-Recapture Distance Sampling (MRDS) IO - PI

log-likelihood value for a fitted detection function

pdot.dsr.integrate.logistic

Compute probability that a object was detected by at least one observer

Hessian computation for fitted distance detection function model parameters

Computes abundance estimates at specified parameter values using Horvitz-Thompson-like estimator

create.varstructure

Creates structures needed to compute abundance and variance

Golden-cheeked warbler mark-recapture distance sampling analysis

Extraction and assignment of parameters to vector

Threshold key function

Run MCDS.exe as a backend for mrds

Threshold key function

MCDS function definition

Numerically integrate pdf of observed distances over specified ranges

Log-likelihood computation for distance sampling data

Plot fit of detection functions and histograms of data from distance sampling model

Plot fit of detection functions and histograms of data from distance sampling independent observer (io) model

flnl.constr.grad.neg

(Negative) gradients of constraint function

The gradient of the half-normal key function

integratepdf.grad

Numerically integrates the non-normalised pdf or the detection function of observed distances over specified ranges.

Parse optimx results and present a nice object

Distribution of probabilities of detection

Detection functions

Integral of pdf of distances

The gradient of the hazard-rate key function

Compute chi-square goodness-of-fit test for ds models

Iterative offset GLM/GAM for fitting detection function

plot.det.tables

Observation detection tables

logisticdupbyx_fast

Logistic for duplicates as a function of covariates (fast)

Layout for plot methods in mrds

print.summary.trial

Print summary of distance detection function model object

print.summary.rem.fi

Print summary of distance detection function model object

Plot unconditional detection function from distance sampling model

Simulated single observer point count data

Quantile-quantile plot and goodness of fit tests for detection functions

Creates design matrix for covariates in detection function

Gradient of the non-normalised pdf of distances or the detection function for the distances.

Plot histogram line

integratedetfct.logistic

Integrate a logistic detection function

Distance Sampling Functions

Two-part normal key function

Wrapper around nlminb

Logistic detection function

Logistic for duplicates as a function of covariates

Tips on optimisation issues in mrds models

Mark-Recapture Distance Sampling (mrds)

Double-platform detection probability

print.p_dist_table

Print distribution of probabilities of detection

Plot fit of detection functions and histograms of data from distance sampling trial observer model

Simple pretty printer for distance sampling analyses

Predictions from mrds models

Prints results of goodness of fit tests for detection functions

Black-capped vireo mark-recapture distance sampling analysis

print.summary.trial.fi

Print summary of distance detection function model object

Plot fit of detection functions and histograms of data from distance sampling trial observer model

Plot fit of detection functions and histograms of data from removal distance sampling model

Prints density and abundance estimates

Plot fit of detection functions and histograms of data from distance sampling independent observer model with full independence (io.fi)

Simulated dual observer point count data

print.det.tables

Print results of observer detection tables

Average p and average p(0) variance

Set parameter bounds

Generate data from a fitted detection function and refit the model

Process data for fitting distance sampling detection function

Summary of distance detection function model object

Plot fit of detection functions and histograms of data from removal distance sampling model

Derivatives for variance of average p and average p(0) variance

Set initial values for detection function based on distance sampling

summary.trial.fi

Summary of distance detection function model object

survey.region.dht

Extrapolate Horvitz-Thompson abundance estimates to entire surveyed region

Wooden stake data from 1978 survey

Wooden stake data from 1977 survey

Simulated removal observer point count data

Summary of distance detection function model object

Plot conditional detection function from distance sampling model

print.summary.ds

Print summary of distance detection function model object

Simulation of distance sampling data via mixture models Allows one to simulate line transect distance sampling data using a mixture of half-normal detection functions.

Invert of covariance matrices

print.summary.io

Print summary of distance detection function model object

Summary of distance detection function model object

Summary of distance detection function model object

Summary of distance detection function model object

Logistic as a function of covariates

is.logistic.constant

Is a logit model constant for all observations?

Logistic as a function of distance

print.summary.io.fi

Print summary of distance detection function model object

print.summary.rem

Print summary of distance detection function model object

Summary of distance detection function model object

is.linear.logistic

Collection of functions for logistic detection functions

Pronghorn aerial survey data from Wyoming

ptdata.distance

Single observer point count data example from Distance

Iterative offset model fitting of mark-recapture with removal model

Calculate the parameter rescaling for parameters associated with covariates

Test validity for histogram breaks(cutpoints)

Compute empirical variance of encounter rate

adj.check.order

Check order of adjustment terms

add.df.covar.line

Add covariate levels detection function plots

Akaike's An Information Criterion for detection functions

adj.series.grad.cos

Series of the gradient of the cosine adjustment series w.r.t. the scaled distance.

Cosine adjustment term, not the series.

adj.series.grad.herm

Series of the gradient of the Hermite polynomial adjustment series w.r.t. the scaled distance.

Hermite polynomial adjustment term, not the series.

Simple polynomial adjustment term, not the series.

Get the apex for a gamma detection function

adj.series.grad.poly

Series of the gradient of the simple polynomial adjustment series w.r.t. the scaled distance.

Compute estimated abundance in covered (sampled) region

Average detection function line for plotting

Numeric Delta Method approximation for the variance-covariance matrix

average.line.cond

Average conditional detection function line for plotting