Petersen2025.3.1 package

Estimators for Two-Sample Capture-Recapture Studies

cap_hist_to_n_m_u

Convert capture history data to n, m and u for use in BTSPAS

fit_classes

LP_fit , LP_IS_fit , LP_SPAS_cit , CL_fit , LP_BTSPAS_fit_Diag , LP_BT...

logit

Logit and anti-logit function.

LP_AICc

Create an AIC table comparing multiple LP fits

LP_BTSPAS_est

Extract estimates of abundance after BTSPAS fit

LP_BTSPAS_fit_Diag

Wrapper (*_fit) to call the Time Stratified Petersen Estimator with Di...

LP_BTSPAS_fit_NonDiag

Wrapper (*_fit) to call the Time Stratified Petersen Estimator with NO...

LP_CL_fit

Fit the Chen-Lloyd model to estimate abundance using a non-parametric ...

LP_est_adjust

Estimate abundance after empirical adjustments for various factors.

LP_est

Estimate abundance after the LP conditional likelihood fit.

LP_fit

Fit a Lincoln-Petersen Model using conditional likelihood

LP_for_rev_fit

Fit a combined FORWARD and REVERSE simple Lincoln-Petersen Model using...

LP_IS_est

Estimate abundance after the LP_IS conditional likelihood fit.

LP_IS_fit

Fit a Lincoln-Petersen Model with incomplete stratification

LP_IS_print

Print the results from a fit a Lincoln-Petersen Model with incomplete ...

LP_modavg

Create an table of individual estimates and the model averaged values

LP_SPAS_est

Extract estimates of abundance after SPAS fit

LP_SPAS_fit

Fit a Stratified-Petersen SPAS model.

LP_summary_stats

Compute summary statistics from the capture histories

LP_test_equal_mf

Test for equal marked fractions in LP experiment

LP_test_equal_recap

Test for equal recapture probability in LP experiment

LP_TL_est

Estimate abundance after the LP_TL (tag loss) conditional likelihood f...

LP_TL_fit

Fit a Lincoln-Petersen Model with Tag Loss using conditional likelihoo...

LP_TL_simulate

Simulate data from a Lincoln-Petersen Model with Tag Loss

n1_n2_m2_to_cap_hist

Convert n1, n2, m2 to capture history data for use in estimating. Vect...

split_cap_hist

Split a vector of capture histories into a matrix with one column for ...

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.

  • Maintainer: Carl Schwarz
  • License: GPL-2
  • Last published: 2025-02-24