# Bayesian Behavioural Capture-Recapture Models

### Description

Model fitting of flexible behavioural recapture models based on conditional probability reparameterization and meaningful partial capture history quantification also referred to as
*meaningful behavioural covariate*

### Details

Package: | BBRecapture |

Type: | Package |

Version: | 0.1 |

Date: | 2013-12-18 |

License: | GPL-2 |

This BBRecap package has been built up to help researchers to fit some relevant classes of capture-recapture models within the framework of Bayesian inference. Special emphasis is given on recently developed tools to take into account flexible behavioral response to capture. The main function developed in the package relies on the generalized linear model framework in the spirit of Huggins (1989) and Alho (1990) for regressing the capture occurrence on previous partial capture histories although shortcuts have been embedded to reduce computational complexity whenever possible. There are also some functions which fit the same class of models maximizing the unconditional likelihood as opposed to the most frequently used approach based on the conditional likelihood (Huggins and Hwang, 2011). There are theoretical arguments related to the so-called likelihood failure (Alunni Fegatelli and Tardella, 2013; Carle and Strub, 1978) which support the use of a Bayesian approach for the estimation of the unknown population size in the presence of behavioral response to capture. Some simulation studies have been also carried out in Alunni Fegatelli (2013) to highlight the occurrence of the likelihood failure pathology and the loss of inferential performance of the conditional likelihood approach even in the absence of failure. In the same circumstances the unconditional likelihood approach should be preferred to the conditional likelihood but both of them are in any case outperformed by the Bayesian approach. Functions in the package are designed to allow minimal efforts by the researcher although optional arguments often allow for a more customized and refined model building.

### Author(s)

Luca Tardella and Danilo Alunni Fegatelli

Maintainer: Danilo Alunni Fegatelli <danilo.alunnifegatelli@uniroma1.it>

### References

Alho, J.M. (1990). Logistic regression in capture-recapture models. Biometrics, 46, 623–635.

Carle, F.L. and Strub, M.R. (1978) A new method for estimating population size from removal data. Biometrics, 34, 621–630.

Huggins, R.M. (1989) On the statistical analysis of capture experiments. Biometrika, 76, 133–140.

Huggins, R. and Hwang, HW (2011) A review of the use of conditional likelihood in capture-recapture experiments. International Statistical Review, 79, 385–400

Farcomeni, A. (2011) Recapture models under equality constraints for the conditional capture probabilities. Biometrika, 98, 237–242

Alunni Fegatelli, D. (2013) New methods for capture-recapture modelling with behavioural response and individual heterogeneity. PhD Thesis. http://padis.uniroma1.it/bitstream/10805/2085/1/TesiDottorato-AlunniFegatelliDanilo.pdf

Alunni Fegatelli, D. and Tardella, L. (2012) Improved inference on capture recapture models with behavioural effects. Statistical Methods & Applications, 22:45-66 (DOI: 10.1007/S10260-012-0221-4)

### Examples

1 2 3 4 5 | ```
data(greatcopper)
out=BBRecap(greatcopper,mod="Mb")
print(out)
``` |

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