Learn R Programming

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Finally the current stable version of mrds is available on CRAN, though this may be up to a month out of date due to CRAN policy.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).

Copy Link

Version

Install

install.packages('mrds')

Monthly Downloads

1,723

Version

2.3.0

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

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

Maintainer

Last Published

December 18th, 2023

Functions in mrds (2.3.0)

Check that a detection function is monotone

Create detection function object

create.command.file

create.command.file

Goodness of fit tests for distance sampling models

create.model.frame

Create a model frame for ddf fitting

Distance Detection Function Fitting

create.varstructure

Creates structures needed to compute abundance and variance

CDS/MCDS Distance Detection Function Fitting

Mark-Recapture Distance Sampling (MRDS) Removal - PI

Mark-Recapture Distance Sampling (MRDS) IO - PI

Mark-Recapture Distance Sampling (MRDS) IO - FI

Observation detection tables

Variance and confidence intervals for density and abundance estimates

Fit detection function using key-adjustment functions

Fit detection function using key-adjustment functions

Detection functions

Mark-Recapture Distance Sampling (MRDS) Removal - FI

Mark-Recapture Analysis of Trial Configuration - FI

integratedetfct.logistic

Integrate a logistic detection function

Distance Sampling Functions

Plot histogram line

integratelogistic.analytic

Analytically integrate logistic detection function

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

Compute value of p(0) using a logit formulation

Hessian computation for fitted distance detection function model parameters

Integral of pdf of distances

Extraction and assignment of parameters to vector

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

Density and abundance estimates and variances

Two-part normal key function

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

is.logistic.constant

Is a logit model constant for all observations?

Log-likelihood computation for distance sampling data

is.linear.logistic

Collection of functions for logistic detection functions

Numerically integrate pdf of observed distances over specified ranges

Black-capped vireo mark-recapture distance sampling analysis

Threshold key function

Threshold key function

Logistic as a function of covariates

logisticdupbyx_fast

Logistic for duplicates as a function of covariates (fast)

log-likelihood value for a fitted detection function

Iterative offset GLM/GAM for fitting detection function

Logistic as a function of distance

Logistic detection function

Run MCDS.exe as a backend for mrds

MCDS function definition

Golden-cheeked warbler mark-recapture distance sampling analysis

Mark-Recapture Distance Sampling (mrds)

Logistic for duplicates as a function of covariates

Tips on optimisation issues in mrds models

plot.det.tables

Observation detection tables

Wrapper around nlminb

Distribution of probabilities of detection

pdot.dsr.integrate.logistic

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

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

Double-platform detection probability

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

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

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

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

Layout for plot methods in mrds

Parse optimx results and present a nice object

Simple pretty printer for distance sampling analyses

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

Plot unconditional detection function from distance sampling model

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

Plot conditional detection function from distance sampling model

Prints results of goodness of fit tests for detection functions

print.det.tables

Print results of observer detection tables

Predictions from mrds models

print.p_dist_table

Print distribution of probabilities of detection

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

print.summary.ds

Print summary of distance detection function model object

print.summary.rem.fi

Print summary of distance detection function model object

Prints density and abundance estimates

print.summary.trial.fi

Print summary of distance detection function model object

Iterative offset model fitting of mark-recapture with removal model

print.summary.trial

Print summary of distance detection function model object

Average p and average p(0) variance

Calculate the parameter rescaling for parameters associated with covariates

print.summary.io.fi

Print summary of distance detection function model object

ptdata.distance

Single observer point count data example from Distance

print.summary.io

Print summary of distance detection function model object

Simulated dual observer point count data

Pronghorn aerial survey data from Wyoming

Simulated removal observer point count data

Process data for fitting distance sampling detection function

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

Wooden stake data from 1977 survey

Generate data from a fitted detection function and refit the model

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

Simulated single observer point count data

Summary of distance detection function model object

Wooden stake data from 1978 survey

Test validity for histogram breaks(cutpoints)

Set parameter bounds

Summary of distance detection function model object

summary.trial.fi

Summary of distance detection function model object

Compute empirical variance of encounter rate

print.summary.rem

Print summary of distance detection function model object

Summary of distance detection function model object

Summary of distance detection function model object

Creates design matrix for covariates in detection function

Summary of distance detection function model object

Summary of distance detection function model object

survey.region.dht

Extrapolate Horvitz-Thompson abundance estimates to entire surveyed region

Set initial values for detection function based on distance sampling

adj.check.order

Check order of adjustment terms

Get the apex for a gamma detection function

Akaike's An Information Criterion for detection functions

assign.default.values

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

add.df.covar.line

Add covariate levels detection function plots

Compute estimated abundance in covered (sampled) region

Average detection function line for plotting

average.line.cond

Average conditional detection function line for plotting

Extraction and assignment of parameters to vector

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.

Numeric Delta Method approximation for the variance-covariance matrix

Extract coefficients

Check parameters bounds during optimisations

Find se of average p and N

covered.region.dht

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

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

Horvitz-Thompson estimates 1/p_i or s_i/p_i

CDS function definition