Authors: Dan Pagendam, CSIRO Data61 (dan.pagendam@csiro.au)
Contributors: Brendan Trewin, CSIRO Health & Biosecurity (brendan.trewin@csiro.au)
This package was developed as a simple alternative to using temporally-static dispersion kernels for the analysis of Mark-Release-Recapture experiments. The approach uses a Gaussian dispersion kernel that evolves through time in a manner analogous to isotropic diffusion in 2D space. The approach also allows for the inclusion of reduced trap catches over time as a result of death and for the specification of different distributional models for the trap counts. The mean of the trap count distribution is modelled as being proportional to the time-integral of the diffusion kernel at the trap location over the time interval that the trap was catching for.
To install the package from GitHub, you will first need to install the devtools package in R using the command:
install.packages("devtools")
Once installed, you will need to load the devtools R package and install the MRRk R package using:
library(devtools)
install_github("dpagendam/MRRk")
To use MRRk with the packaged example data, try:
library(MRRk)
data(exampleData)
params = rep(NA, 2)
names(params) = c("K", "sigma")
params[1] = 1000
params[2] = 100
mle <- optim(params, trapLogLikelihood, method = "L-BFGS-B", lower = c(1, 10), upper = c(1E20, 1000), control = list(fnscale = -1), trapLocations = list(trapLocations), releaseLocations = list(releaseLocations), trapData = list(trapData), distributionType = "poisson", includeDrift = FALSE, includeDeath = FALSE)
The package currently only contains two functions and help can be accessed for these within R by typing:
?trapLogLikelihood
?densityIntegral
For further support, please email :-)