Functions to estimate density from mark-recapture data using MCMC methods and JAGS.

1 2 3 |

`capthist` |
a |

`buffer` |
scalar mask buffer radius (default 100 m) |

`start` |
an optional object of class |

`nAug` |
number of individuals in the augmented population; if NA, a suitable default is chosen based on the object passed to |

`chains` |
the number of Markov chains to run. |

`sample` |
the total number of values to return. The number of values calculated per chain is |

`burnin` |
the number of values to discard at the beginning of each chain. |

`thin` |
the thinning rate. If set to n > 1, n values are calculated for each value returned. |

`adapt` |
the number of iterations to run in the JAGS adaptive phase. |

`priorOnly` |
if TRUE, the function produces random draws from the appropriate |

`seed` |
set a seed for the random number generators. |

`parallel` |
if TRUE or NULL and sufficient cores are available, the MCMC chains are run in parallel; if TRUE and insufficent cores are available, a warning is given. |

`Bsecr0`

implements an intercept-only model (D ~ 1, g0 ~ 1, sigma ~ 1).

Returns an object of class `Bwiqid`

, data frame with one column for each parameter, ie. D, lam0 and sigma.

There are print, plot, and window methods for `Bwiqid`

.

Mike Meredith

Borchers & Efford (2008) Spatially explicit maximum likelihood methods for capture-recapture studies *Biometrics* 64, 377-385

Royle & Dorazio (2008) *Hierarchical modeling and inference in ecology*. Academic Press

The function `secr.fit`

in package `secr`

.

1 2 3 4 5 6 7 8 9 10 | ```
# The stoats data set in 'secr'
require(secr)
data(stoatDNA)
# This takes ca 10 mins on a multicore machine:
Bout <- Bsecr0(stoatCH, buffer=1000)
Bout
plot(Bout)
# look at diagnostic plots to see if D is constrained by nAug:
tracePlot(Bout)
densityPlot(Bout) # Upper values of D doesn't look constrained.
``` |

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