multimark2.1.7 package

Capture-Mark-Recapture Analysis using Multiple Non-Invasive Marks

getdensityClosedSCR

Calculate population density estimates

getprobsCJS

Calculate posterior capture and survival probabilities

getprobsClosed

Calculate posterior capture and recapture probabilities

getprobsClosedSCR

Calculate posterior capture and recapture probabilities

markCJS

Fit open population survival models for ``traditional'' capture-mark-r...

markClosed

Fit closed population abundance models for ``traditional'' capture-mar...

markClosedSCR

Fit spatial population abundance models for ``traditional'' capture-ma...

multimarkCJS

Fit open population survival models for capture-mark-recapture data co...

multimarkClosed

Fit closed population abundance models for capture-mark-recapture data...

multimarkClosedSCR

Fit spatially-explicit population abundance models for capture-mark-re...

multimarkSCRsetup-class

Class "multimarkSCRsetup"

multimarksetup-class

Class "multimarksetup"

multimodelCJS

Multimodel inference for 'multimark' open population survival models

multimodelClosed

Multimodel inference for 'multimark' closed population abundance model...

multimodelClosedSCR

Multimodel inference for 'multimark' spatial population abundance mode...

plotSpatialData

Plot spatial capture-mark-recapture data

processdata

Generate model inputs for fitting 'multimark' models

processdataSCR

Generate model inputs for fitting spatial 'multimark' models

simdataCJS

Simulate open population capture-mark-recapture data arising from mult...

simdataClosed

Simulate closed population capture-mark-recapture data arising from mu...

simdataClosedSCR

Simulate spatially-explicit capture-mark-recapture data from a (demogr...

Traditional and spatial capture-mark-recapture analysis with multiple non-invasive marks. The models implemented in 'multimark' combine encounter history data arising from two different non-invasive "marks", such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, 'multimark' also provides a user-friendly interface for performing Bayesian multimodel inference using non-spatial or spatial capture-recapture data consisting of a single conventional mark or multiple non-invasive marks. See McClintock (2015) <doi:10.1002/ece3.1676> and Maronde et al. (2020) <doi:10.1002/ece3.6990>.

  • Maintainer: Brett T. McClintock
  • License: GPL-2
  • Last published: 2025-11-25