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Finally the current stable version of mrds is available on CRAN, though this may be up to a month out of date due to CRAN policy.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

2.2.8

License

GPL (>= 2)

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Repository

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

Maintainer

Laura Marshall

Last Published

November 16th, 2022

Functions in mrds (2.2.8)

Akaike's An Information Criterion for detection functions

Average detection function line for plotting

add.df.covar.line

Add covariate levels detection function plots

adj.check.order

Check order of adjustment terms

Get the apex for a gamma detection function

assign.default.values

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

average.line.cond

Average conditional detection function line for plotting

Compute estimated abundance in covered (sampled) region

Numeric Delta Method approximation for the variance-covariance matrix

Extraction and assignment of parameters to vector

Extract coefficients

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

Find se of average p and N

Check parameters bounds during optimisations

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

CDS function definition

covered.region.dht

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

Check that a detection function is monotone

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

Horvitz-Thompson estimates 1/p_i or s_i/p_i

Goodness of fit tests for distance sampling models

create.model.frame

Create a model frame for ddf fitting

create.varstructure

Creates structures needed to compute abundance and variance

CDS/MCDS Distance Detection Function Fitting

Mark-Recapture Distance Sampling (MRDS) IO - PI

Mark-Recapture Distance Sampling (MRDS) Removal - PI

Mark-Recapture Distance Sampling (MRDS) IO - FI

Create detection function object

Mark-Recapture Distance Sampling (MRDS) Removal - FI

Distance Detection Function Fitting

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

Observation detection tables

Detection functions

Variance and confidence intervals for density and abundance estimates

Density and abundance estimates and variances

Distance Sampling Functions

Fit detection function using key-adjustment functions

Fit detection function using key-adjustment functions

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

integratelogistic.analytic

Analytically integrate logistic detection function

Mark-Recapture Analysis of Trial Configuration - FI

Plot histogram line

integratedetfct.logistic

Integrate a logistic detection function

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

Compute value of p(0) using a logit formulation

Integral of pdf of distances

Hessian computation for fitted distance detection function model parameters

Log-likelihood computation for distance sampling data

Extraction and assignment of parameters to vector

Numerically integrate pdf of observed distances over specified ranges

Golden-cheeked warbler mark-recapture distance sampling analysis

Logistic as a function of covariates

log-likelihood value for a fitted detection function

Threshold key function

Two-part normal key function

Threshold key function

is.logistic.constant

Is a logit model constant for all observations?

Black-capped vireo mark-recapture distance sampling analysis

Iterative offset GLM/GAM for fitting detection function

is.linear.logistic

Collection of functions for logistic detection functions

Double-platform detection probability

logisticdupbyx_fast

Logistic for duplicates as a function of covariates (fast)

Tips on optimisation issues in mrds models

Logistic for duplicates as a function of covariates

Logistic detection function

Wrapper around nlminb

MCDS function definition

Logistic as a function of distance

Mark-Recapture Distance Sampling (mrds)

Parse optimx results and present a nice object

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

Distribution of probabilities of detection

pdot.dsr.integrate.logistic

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

plot.det.tables

Observation detection tables

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 independent observer (io) model

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

Layout for plot methods in mrds

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

Simple pretty printer for distance sampling analyses

Prints density and abundance estimates

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

Predictions from mrds models

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

Prints results of goodness of fit tests for detection functions

Plot unconditional detection function from distance sampling model

Plot conditional detection function from distance sampling model

print.det.tables

Print results of observer detection tables

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

print.summary.rem.fi

Print summary of distance detection function model object

Process data for fitting distance sampling detection function

print.summary.io

Print summary of distance detection function model object

print.summary.rem

Print summary of distance detection function model object

print.summary.trial.fi

Print summary of distance detection function model object

print.summary.io.fi

Print summary of distance detection function model object

Average p and average p(0) variance

print.summary.ds

Print summary of distance detection function model object

print.summary.trial

Print summary of distance detection function model object

Simulated single observer point count data

Iterative offset model fitting of mark-recapture with removal model

Calculate the parameter rescaling for parameters associated with covariates

Generate data from a fitted detection function and refit the model

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

Simulated dual observer point count data

ptdata.distance

Single observer point count data example from Distance

Pronghorn aerial survey data from Wyoming

Set parameter bounds

Simulated removal observer point count data

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.

Summary of distance detection function model object

Set initial values for detection function based on distance sampling

Invert of covariance matrices

Wooden stake data from 1977 survey

Summary of distance detection function model object

Summary of distance detection function model object

Wooden stake data from 1978 survey

Summary of distance detection function model object

Creates design matrix for covariates in detection function

Test validity for histogram breaks(cutpoints)

Summary of distance detection function model object

Summary of distance detection function model object

Compute empirical variance of encounter rate

survey.region.dht

Extrapolate Horvitz-Thompson abundance estimates to entire surveyed region

summary.trial.fi

Summary of distance detection function model object