mra-package: MRA - Mark Recapture Analysis

In mra: Mark-Recapture Analysis

Description

Description - This package contains analysis functions, and associated routines, to conduct analyses of mark-recapture (capture-recapture) data using individual, time, and individual-time varying covariates. In general, these routines relate vectors of capture histories to vectors of covariates using a regression approach (Amstrup et al. 2005, Ch 9). All capture, survival, transition, etc. parameters are functions of individual and time specific covariates, and the estimated parameters are coefficients in logistic-linear equations.

Relationship to MARK - For the most part, these routines perform a subset of the analyses available in program MARK or via the MARK front-end package, RMark. The most significant difference between this package and MARK is parameterization. The parameterization used here does not utilize triangular "parameter information matrices" (PIMs) as MARK (and RMark) does. Because of this, the "design" matrix utilized by this package is not parallel to the "design" matrix of program MARK. For those new to mark-recapture analysis, this parameterization difference will be inconsequential. The approach taken here provides equivalent modeling flexibility, yet is easier to grasp and visualize, in our opinion. For those already familiar with the PIMs used by program MARK, it is helpful to view the "PIMs" of this package as rectangular matrices of the real parameters. I.e., the "PIMs" of this package are rectangular matrices where cell (i,j) contains the real parameter (capture or survival) for individual i at capture occasion j.

Analyses available here that are not included in program MARK include:

• Estimation of population size from open population CJS models via the Horvitz-Thompson estimator.

• Residuals, goodness of fit tests, and associated plots for assessing model fit in open CJS models.

Future Research - The author of MRA welcome interest in and routines that perform the following analyzes:

• Continuous time models. Especially those that allow inclusion of covariates.

• Band recovery models.

• Baysian models.

• Joint live-dead recovery models.

• MCMC methods or routines that can be applied to exiting models.

• Plotting methods for exiting models.

• Model selection methods for existing models.

• Simulation methods and routines.

Details

Package:	mra
Type:	Package
License:	GNU General Public License

Author(s)

Trent McDonald

Maintainer: Trent McDonald <[e-mail]>

References

Amstrup, S.C., T.L. McDonald, and B.F.J. Manly. 2005. Handbook of Capture-Recapture Analysis, Princeton: Princeton University Press.

mra documentation built on May 1, 2019, 6:50 p.m.