| Type: | Package | 
| Title: | Multi-Visit Closed Population Mark-Recapture Estimates | 
| Version: | 0.1.0 | 
| Description: | Compute bootstrap confidence intervals for the adjusted Schnabel and Schumacher-Eschmeyer multi-visit mark-recapture estimators based on Dettloff (2023) <doi:10.1016/j.fishres.2023.106756>. | 
| License: | MIT + file LICENSE | 
| Encoding: | UTF-8 | 
| RoxygenNote: | 7.2.3 | 
| Imports: | stats | 
| URL: | https://github.com/k-dettloff/mRc | 
| BugReports: | https://github.com/k-dettloff/mRc/issues | 
| NeedsCompilation: | no | 
| Packaged: | 2023-08-25 18:27:33 UTC; kyle.dettloff | 
| Author: | Kyle Dettloff [aut, cre, cph] | 
| Maintainer: | Kyle Dettloff <kyle.dettloff@noaa.gov> | 
| Repository: | CRAN | 
| Date/Publication: | 2023-08-28 11:20:06 UTC | 
Multi-visit closed population mark-recapture estimates
Description
Calculate adjusted Schnabel and Schumacher-Eschmeyer estimates with confidence intervals.
Usage
closedCI(
  marked,
  caught,
  recaptured,
  newmarks = NULL,
  alpha = 0.05,
  ndraws = 1e+05
)
Arguments
| marked | number of animals marked on first visit (M2) | 
| caught | vector of catch on subsequent visits (nk) | 
| recaptured | vector of recaptures on subsequent visits (mk) | 
| newmarks | vector of newly marked animals on subsequent visits (default: nk-mk) | 
| alpha | type I error rate for confidence intervals (default: 0.05) | 
| ndraws | number of bootstrap draws (default: 10,000) | 
Details
Bias adjusted estimators are based on Dettloff (2023). Bootstrap confidence intervals are computed using a beta-binomial distribution with n = nk, alpha = mk, beta = nk-mk.
Value
Matrix containing population size estimates with confidence intervals for each method
References
Dettloff, K. (2023). Assessment of bias and precision among simple closed population mark-recapture estimators. Fisheries Research 265, 106756. doi: <https://doi.org/10.1016/j.fishres.2023.106756>
Examples
M2 = 2
n = c(232, 524, 152, 98, 353)
m = c(0, 5, 8, 6, 13)
set.seed(123)
closedCI(M2, n, m, ndraws = 1000)