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This function returns a list of all the observable partial capture histories which can be recorded in a discrete-time capture-recapture setting with t consecutive trapping occasions. The observable partial capture histories are 2^t-1
● Data Source:
CranContrib
● Keywords:
● Alias: list.historylabels
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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
● Data Source:
CranContrib
● Keywords: Bayesian inference, Behavioural models, Capture-recapture, Conditional MLE, Likelihood failure, Unconditional MLE
● Alias: BBRecapture-package
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BBRecap.custom.part
(Package: BBRecapture) :
Bayesian inference for behavioural effect models based on a partition of the set of all partial capture histories
Bayesian inference for a general model framework based on the capture probabilities conditioned on each possible partial capture history. As suggested in Alunni Fegatelli and Tardella (2012) the conditional approach originally proposed in Farcomeni (2011) [saturated reparameterization] is reviewed in terms of partitions into equivalence classes of conditional probabilities. In this function the user can directly provide the model as a partition.
● Data Source:
CranContrib
● Keywords: Bayesian inference, Behavioural models
● Alias: BBRecap.custom.part
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LBRecap
(Package: BBRecapture) :
Unconditional (complete) likelihood inference for capture-recapture analysis with emphasis on behavioural effect modelling
Unconditional (complete) likelihood inference for a large class of discrete-time capture-recapture models under closed population with special emphasis on behavioural effect modelling including also the meaningful behavioral covariate approach proposed in Alunni Fegatelli (2013) [PhD thesis]. Many of the standard classical models such as M_0, M_b, M_{c_1}, M_t or M_{bt} can be regarded as particular instances of the aforementioned approach. Other flexible alternatives can be fitted through a careful choice of a meaningful behavioural covariate and a possible partition of its admissible range
● Data Source:
CranContrib
● Keywords: Behavioural models, Unconditional MLE
● Alias: LBRecap
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It returns (up to normalizing constant) the mass assigned to each positive integer or a vector of integers by Rissanen's universal prior for positive integers
● Data Source:
CranContrib
● Keywords:
● Alias: rissanen
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All the possible partial capture histories observable during a capture-recapture experiment with t sampling occasions can be partitioned according to numerical values corresponding to some meaningful covariate (quantification of binary sequences corresponding to partial capture histories). Each subset of the partition corresponds to all partial capture histories which returns numerical values of the quantification within one of the intervals represented by two consecutive values in the optional argument vector breaks.
● Data Source:
CranContrib
● Keywords:
● Alias: partition.ch
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pch is used to obtain all the observed partial capture histories corresponding to an observed binary data matrix.
● Data Source:
CranContrib
● Keywords: Datasets
● Alias: pch
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Mouse (Microtus Pennsylvanicus) Dataset
● Data Source:
CranContrib
● Keywords: Datasets
● Alias: mouse
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BBRecap
(Package: BBRecapture) :
Bayesian inference for capture-recapture analysis with emphasis on behavioural effect modelling
Bayesian inference for a large class of discrete-time capture-recapture models under closed population with special emphasis on behavioural effect modelling including also the meaningful behavioral covariate approach proposed in Alunni Fegatelli (2013) [PhD thesis]. Many of the standard classical models such as M_0, M_b, M_{c_1}, M_t or M_{bt} can be regarded as particular instances of the aforementioned approach. Other flexible alternatives can be fitted through a careful choice of a meaningful behavioural covariate and a possible partition of its admissible range
● Data Source:
CranContrib
● Keywords: Bayesian inference, Behavioural models
● Alias: BBRecap
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The quant.binary family of functions allow to quantify binary capture histories (partial or complete) in terms of a meaningful quantity which can be interpreted as a possibly meaningful behavioral covariate (like memory persistence of previous capture history)
● Data Source:
CranContrib
● Keywords: Partial capture history quantification
● Alias: quant.binary, quant.binary.counts, quant.binary.counts.integer, quant.binary.integer, quant.binary.markov
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providing 
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