Rcapture

Rcapture: Loglinear Models for Capture-Recapture in R. This article introduces Rcapture, an R package for capture-recapture experiments. The data for analysis consists of the frequencies of the observable capture histories over the t capture occasions of the experiment. A capture history is a vector of zeros and ones where one stands for a capture and zero for a miss. Rcapture can fit three types of models. With a closed population model, the goal of the analysis is to estimate the size N of the population which is assumed to be constant throughout the experiment. The estimator depends on the way in which the capture probabilities of the animals vary. Rcapture features several models for these capture probabilities that lead to different estimators for N. In an open population model, immigration and death occur between sampling periods. The estimation of survival rates is of primary interest. Rcapture can fit the basic Cormack-Jolly-Seber and Jolly-Seber model to such data. The third type of models fitted by Rcapture are robust design models. It features two levels of sampling; closed population models apply within primary periods and an open population model applies between periods. Most models in Rcapture have a loglinear form; they are fitted by carrying out a Poisson regression with the R function glm. Estimates of the demographic parameters of interest are derived from the loglinear parameter estimates; their variances are obtained by linearization. The novel feature of this package is the provision of several new options for modeling capture probabilities heterogeneity between animals in both closed population models and the primary periods of a robust design. It also implements many of the techniques developed by R. M. Cormack for open population models.

This software is also peer reviewed by journal JSS.


References in zbMATH (referenced in 11 articles , 1 standard article )

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  1. Farcomeni, Alessio; Dotto, Francesco: A correction to make Chao estimator conservative when the number of sampling occasions is finite (2021)
  2. Hwang, Wen-Han; Heinze, Dean; Stoklosa, Jakub: A weighted partial likelihood approach for zero-truncated models (2019)
  3. Yauck, Mamadou; Rivest, Louis-Paul; Rothman, Greg: Capture-recapture methods for data on the activation of applications on mobile phones (2019)
  4. Pek, Jolynn; Wu, Hao: Profile likelihood-based confidence intervals and regions for structural equation models (2015)
  5. Thomas Yee; Jakub Stoklosa; Richard Huggins: The VGAM Package for Capture-Recapture Data Using the Conditional Likelihood (2015) not zbMATH
  6. Farcomeni, Alessio; Scacciatelli, Daria: Heterogeneity and behavioral response in continuous time capture-recapture, with application to street cannabis use in Italy (2013)
  7. Huggins, Richard; Hwang, Wen-Han: A review of the use of conditional likelihood in capture-recapture experiments (2011)
  8. Rivest, Louis-Paul: Why a time effect often has a limited impact on capture-recapture estimates in closed populations (2008)
  9. Chris Stubben; Brook Milligan: Estimating and Analyzing Demographic Models Using the popbio Package in R (2007) not zbMATH
  10. Rivest, Louis-Paul; Baillargeon, Sophie: Applications and extensions of Chao’s moment estimator for the size of a closed population (2007)
  11. Sophie Baillargeon; Louis-Paul Rivest: Rcapture: Loglinear Models for Capture-Recapture in R (2007) not zbMATH