MARK

Program MARK: Survival Estimation from Populations of Marked Animals. MARK provides parameter estimates from marked animals when they are re-encountered at a later time as dead recoveries, or live recaptures or re-sightings. The time intervals between re-encounters do not have to be equal. More than one attribute group of animals can be modelled. The basic input to MARK is the encounter history for each animal. MARK can also estimate the size of closed populations. Parameters can be constrained to be the same across re-encounter occasions, or by age, or group, using the parameter index matrix. A set of common models for initial screening of data are provided. Time effects, group effects, time x group effects and a null model of none of the above, are provided for each parameter. Besides the logit function to link the design matrix to the parameters of the model, other link functions include the log—log, complimentary log—log, sine, log, and identity. The estimates of model parameters are computed via numerical maximum likelihood techniques. The number of parameters that are estimable in the model are determined numerically and used to compute the quasi-likelihood AIC value for the model. Both the input data, and outputs for various models that the user has built, are stored in the Results database which contains a complete description of the model building process. It is viewed and manipulated in a Results Browser window. Summaries available from this window include viewing and printing model output, deviance residuals from the model, likelihood ratio and analysis of deviance between models, and adjustments for over dispersion. Models can also be retrieved and modified to create additional models. These capabilities are implemented in a Microsoft Windows 95 interface. The online help system has been developed to provide all necessary program documentation.


References in zbMATH (referenced in 23 articles )

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  1. Renato Valladares Panaro: spsurv: An R package for semi-parametric survival analysis (2020) arXiv
  2. Steiner, Ulrich K.; Tuljapurkar, Shripad: Drivers of diversity in individual life courses: sensitivity of the population entropy of a Markov chain (2020)
  3. Yauck, Mamadou; Rivest, Louis-Paul: On the estimation of population sizes in capture-recapture experiments (2019)
  4. Conn, Paul B.; Alisauskas, Ray T.: Simultaneous modelling of movement, measurement error, and observer dependence in mark-recapture distance sampling: an application to arctic bird surveys (2018)
  5. Lahoz-Monfort, José J.; Harris, Michael P.; Wanless, Sarah; Freeman, Stephen N.; Morgan, Byron J. T.: Bringing it all together: multi-species integrated population modelling of a breeding community (2017)
  6. Oosterhuis, Hannah E. M.; van der Ark, L. Andries; Sijtsma, Klaas: Standard errors and confidence intervals of norm statistics for educational and psychological tests (2017)
  7. Bonner, Simon J.; Schofield, Matthew R.; Noren, Patrik; Price, Steven J.: Extending the latent multinomial model with complex error processes and dynamic Markov bases (2016)
  8. Worthington, Hannah; King, Ruth; Buckland, Stephen T.: Analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation (2015)
  9. Laake, Jeffrey L.; Johnson, Devin S.; Diefenbach, Duane R.; Ternent, Mark A.: Hidden Markov model for dependent mark loss and survival estimation (2014)
  10. McClintock, Brett T.; Bailey, Larissa L.; Dreher, Brian P.; Link, William A.: Probit models for capture-recapture data subject to imperfect detection, individual heterogeneity and misidentification (2014)
  11. Higgs, Megan D.; Link, William A.; White, Gary C.; Haroldson, Mark A.; Bjornlie, Daniel D.: Insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year (2013)
  12. Stoklosa, Jakub; Dann, Peter; Huggins, Richard: Inference on partially observed quasi-stationary Markov chains with applications to multistate population models (2012)
  13. Stoklosa, Jakub; Huggins, Richard M.: Cormack-Jolly-Seber model with environmental covariates: a P-spline approach (2012)
  14. Ian Fiske; Richard Chandler: unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance (2011) not zbMATH
  15. Laake, J. L.; Collier, B. A.; Morrison, M. L.; Wilkins, R. N.: Point-based mark-recapture distance sampling (2011)
  16. Macbeth, Gilbert M.; Broderick, Damien; Ovenden, Jennifer R.; Buckworth, Rik C.: Likelihood-based genetic mark-recapture estimates when genotype samples are incomplete and contain typing errors (2011)
  17. Litt, Andrea R.; Steidl, Robert J.: Improving estimates of abundance by aggregating sparse capture-recapture data (2010)
  18. Mazzetta, Chiara: Age specificity in conditional ring-recovery models (2010)
  19. Mccrea, R. S.; Morgan, B. J. T.; Gimenez, O.; Besbeas, P.; Lebreton, J.-D.; Bregnballe, T.: Multi-site integrated population modelling (2010)
  20. Borchers, D. L.; Efford, M. G.: Spatially explicit maximum likelihood methods for capture-recapture studies (2008)

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