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Finally the current stable version of mrds is available on CRAN, though this may be up to a month out of date due to CRAN policy.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

2.2.4

License

GPL (>= 2)

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Repository

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

Maintainer

Laura Marshall

Last Published

December 1st, 2020

Functions in mrds (2.2.4)

Numeric Delta Method approximation for the variance-covariance matrix

Akaike's An Information Criterion for detection functions

Average detection function line for plotting

average.line.cond

Average conditional detection function line for plotting

add_df_covar_line

Add covariate levels detection function plots

Compute estimated abundance in covered (sampled) region

assign.default.values

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

Horvitz-Thompson estimates 1/p_i or s_i/p_i

Extract coefficients

Check parameters bounds during optimisations

Extraction and assignment of parameters to vector

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

Mark-Recapture Distance Sampling (MRDS) IO - PI

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

Check that a detection function is monotone

create.varstructure

Creates structures needed to compute abundance and variance

Mark-Recapture Distance Sampling (MRDS) IO - FI

Get the apex for a gamma detection function

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

Goodness of fit tests for distance sampling models

adj.check.order

Check order of adjustment terms

covered.region.dht

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

Fit detection function using key-adjustment functions

CDS/MCDS Distance Detection Function Fitting

CDS function definition

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

Mark-Recapture Distance Sampling (MRDS) Removal - PI

Mark-Recapture Distance Sampling (MRDS) Removal - FI

Distance Detection Function Fitting

Logistic as a function of covariates

Log-likelihood computation for distance sampling data

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

Observation detection tables

Mark-Recapture Analysis of Trial Configuration - FI

Fit detection function using key-adjustment functions

Hessian computation for fitted distance detection function model parameters

Variance and confidence intervals for density and abundance estimates

Density and abundance estimates and variances

Golden-cheeked warbler mark-recapture distance sampling analysis

Find se of average p and N

create.model.frame

Create a model frame for ddf fitting

Create detection function object

Logistic as a function of distance

Parse optimx results and present a nice object

log-likelihood value for a fitted detection function

Layout for plot methods in mrds

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

Tips on optimisation issues in mrds models

Plot histogram line

MCDS function definition

integratedetfct.logistic

Integrate a logistic detection function

Distribution of probabilities of detection

Double-platform detection probability

Compute value of p(0) using a logit formulation

Plot unconditional detection function from distance sampling model

Distance Sampling Functions

Detection functions

Predictions from mrds models

integratelogistic.analytic

Analytically integrate logistic detection function

pdot.dsr.integrate.logistic

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

Plot conditional detection function from distance sampling model

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

Set initial values for detection function based on distance sampling

Average p and average p(0) variance

print.summary.trial

Print summary of distance detection function model object

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

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

Simulated removal observer point count data

print.summary.trial.fi

Print summary of distance detection function model object

Integral of pdf of distances

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.

is.logistic.constant

Is a logit model constant for all observations?

Extraction and assignment of parameters to vector

print.p_dist_table

Print distribution of probabilities of detection

summary.trial.fi

Summary of distance detection function model object

Summary of distance detection function model object

Threshold key function

Threshold key function

Iterative offset GLM/GAM for fitting detection function

logisticdupbyx_fast

Logistic for duplicates as a function of covariates (fast)

Numerically integrate pdf of observed distances over specified ranges

Logistic for duplicates as a function of covariates

Logistic detection function

plot.det.tables

Observation detection tables

print.summary.ds

Print summary of distance detection function model object

Process data for fitting distance sampling detection function

Black-capped vireo mark-recapture distance sampling analysis

is.linear.logistic

Collection of functions for logistic detection functions

Mark-Recapture Distance Sampling (mrds)

Simulated single observer point count data

Wrapper around nlminb

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

Set parameter bounds

Prints density and abundance estimates

print.det.tables

Print results of observer detection tables

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 model

print.summary.rem.fi

Print summary of distance detection function model object

Creates design matrix for covariates in detection function

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

print.summary.rem

Print summary of distance detection function model object

Invert of covariance matrices

Calculate the parameter rescaling for parameters associated with covariates

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

Wooden stake data from 1977 survey

Simple pretty printer for distance sampling analyses

Summary of distance detection function model object

Generate data from a fitted detection function and refit the model

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

Pronghorn aerial survey data from Wyoming

Wooden stake data from 1978 survey

Iterative offset model fitting of mark-recapture with removal model

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

Prints results of goodness of fit tests for detection functions

print.summary.io

Print summary of distance detection function model object

Test validity for histogram breaks(cutpoints)

Simulated dual observer point count data

ptdata.distance

Single observer point count data example from Distance

Summary of distance detection function model object

print.summary.io.fi

Print summary of distance detection function model object

Summary of distance detection function model object

Compute empirical variance of encounter rate