Package: Petersen 2025.3.1
Carl Schwarz
Petersen: Estimators for Two-Sample Capture-Recapture Studies
A comprehensive implementation of Petersen-type estimators and its many variants for two-sample capture-recapture studies. A conditional likelihood approach is used that allows for tag loss; non reporting of tags; reward tags; categorical, geographical and temporal stratification; partial stratification; reverse capture-recapture; and continuous variables in modeling the probability of capture. Many examples from fisheries management are presented.
Authors:
Petersen_2025.3.1.tar.gz
Petersen_2025.3.1.tar.gz(r-4.5-noble)Petersen_2025.3.1.tar.gz(r-4.4-noble)
Petersen_2025.3.1.tgz(r-4.4-emscripten)Petersen_2025.3.1.tgz(r-4.3-emscripten)
Petersen.pdf |Petersen.html✨
Petersen/json (API)
NEWS
| # Install 'Petersen' in R: |
| install.packages('Petersen', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/cschwarz-stat-sfu-ca/petersen/issues3 issues
- data_NorthernPike - Capture-recapture experiment on Northern Pike in Mille Lacs, MN, in 2005.
- data_NorthernPike_tagloss - Capture-recapture experiment on Northern Pike in Mille Lacs, MN, in 2005 with tagloss information.
- data_btspas_diag1 - Estimating abundance of outgoing smolt - BTSPAS - diagonal case
- data_btspas_nondiag1 - Estimating abundance of salmon - BTSPAS - non-diagonal case
- data_kokanee_tagloss - Capture-recapture on Kokanee in Metolius River with tag loss
- data_lfc_reverse - Lower Fraser Coho for Reverse Capture-Recapture with geographic stratification.
- data_rodli - Capture-recapture experiment at Rodli Tarn.
- data_sim_reward - Simulated data for reward tags used to estimate reporting rate
- data_sim_tagloss_t2perm - Simulated data for tag loss with second permanent tag.
- data_sim_tagloss_twoD - Simulated data for tag loss with 2 distinguishable tags.
- data_spas_harrison - Estimating abundance of salmon - SPAS - Harrison River
- data_wae_is_long - Walleye data with incomplete stratification with length covariate
- data_wae_is_short - Walleye data with incomplete stratification with no covariates and condensed
- data_yukon_reverse - Yukon River data used for Reverse Capture-Recapture example.
Last updated 1 months agofrom:1f402dcd12. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 26 2025 |
| R-4.5-linux | OK | Mar 26 2025 |
| R-4.4-linux | OK | Mar 26 2025 |
Exports:cap_hist_to_n_m_uexpitfit_classeslogitLP_AICcLP_BTSPAS_estLP_BTSPAS_fit_DiagLP_BTSPAS_fit_NonDiagLP_CL_fitLP_estLP_est_adjustLP_fitLP_for_rev_fitLP_IS_estLP_IS_fitLP_IS_printLP_modavgLP_SPAS_estLP_SPAS_fitLP_summary_statsLP_test_equal_mfLP_test_equal_recapLP_TL_estLP_TL_fitLP_TL_simulaten1_n2_m2_to_cap_histsplit_cap_hist
Dependencies:abindactuarAICcmodavgbackportsbbmlebdsmatrixbootBTSPAScheckmateclicodacolorspacecpp11data.tabledplyrexpintexpmfansifarverformula.toolsgenericsggforceggplot2gluegridExtragtableisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvmsmmunsellmvtnormnlmenumDerivoperator.toolspillarpkgconfigplyrpolyclippurrrR2jagsR2WinBUGSR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rjagsrlangscalesSPASstringistringrsurvivalsystemfontstibbletidyrtidyselectTMBtweenrunmarkedutf8vctrsVGAMviridisLitewithrxtable
Citation
To cite the Petersen package in publications use:
Schwarz, C. J (2023). The Petersen-Estimator (and many more) for Two-Sample Capture-Recapture Studies with Applications to Fisheries Management. R Package.
Corresponding BibTeX entry:
@Manual{,
title = {The Petersen-Estimator (and many more) for Two-Sample
Capture-Recapture Studies with Applications to Fisheries
Management {R} {Petersen} Package},
author = {Carl James Schwarz},
year = {2023},
}
Readme and manuals
Petersen
Use of the Petersen Capture-Recapture estimates in fisheries management.
Versions and installation
-
CRAN Download the Petersen package
-
Github To install the latest development version from Github, install the newest version of the devtools package; then run
devtools::install_github("cschwarz-stat-sfu-ca/Petersen", dependencies = TRUE,
build_vignettes = TRUE)
Features
Extensive user manual available at:
https://github.com/cschwarz-stat-sfu-ca/Petersen/tree/master/PetersenMonograph
The Petersen-method is the simplest of more general capture-recapture methods which are extensively reviewed in Williams et al. (2002). Despite the Petersen method's simplicity, many of the properties of the estimator, and the effects of violations of assumptions are similar to these more complex capture-recapture studies. Consequently, a firm understanding of the basic principles learned from studying this method are extremely useful to develop an intuitive understanding of the larger class of studies.
The purpose of this R package is to bring together a wide body of older and newer literature on the design and analysis of the "simple" two-sample capture-recapture study. This monograph builds upon the comprehensive summaries found in Ricker (1975), Seber (1982), and William et AL (2002), and incorporates newer works that have not yet summarized. While the primary emphasis is on the application to fisheries management, the methods are directly applicable to many other studies.
The core of the package is the use of conditional likelihood estimation that allows for covariates which are not observed on animals not handled.
The packages includes functions for the analysis of
- simple studies with no covariates or strata
- simple stratified-Petersen studies or fixed continuous covariates
- incompletely stratified studies where only a sub-sample is stratified to save costs
- geographically stratified studies (wrapper to SPAS)
- temporally-stratified studies (wrapper to BTSPAS)
- double tagging studies including reward tagging studies
- multiple-Petersen studies (call RMark/MARK and use mark-resight methods therein)
- forward and reverse-Petersen studies and their combination
References
Petersen, C. G. J. (1896). The Yearly Immigration of Young Plaice into the Limfjord from the German Sea, Etc. Report Danish Biological Station 6, 1--48.
Seber, G. A. F. (1982). The Estimation of Animal Abundance and Related Parameters. 2nd ed. London: Griffin.
Williams, B. K., J. D. Nichols, and M. J. Conroy. (2002). Analysis and Management of Animal Populations. New York: Academic Press.
