LCMCR (version 0.4.14)

LCMCR-package: Capture-Recapture Estimation using Bayesian Nonparametric latent-class models

Description

This package implements a fully Bayesian multiple-recapture method for estimating the unknown size of a population using non-parametric latent class models. This is an implementation of the method described in Manrique-Vallier (2016). The estimation algorithm is based on Markov Chain Monte Carlo sampling.

Author

Daniel Manrique-Vallier

Details

Package:	LCMCR
Type:	Package
Version:	0.4.14
Date:	2023-12-13
License:	GPL >= 2

References

Manrique-Vallier, D. (2016) "Bayesian Population Size Estimation Using Dirichlet Process Mixtures", Biometrics.

Examples

library('LCMCR')

###Using Kosovo data.###
data(kosovo_aggregate)

###Example 1: Capture-Recapture estimation using convenience functions###
#Create and initialize an LCMCR object for MCMC sampling#
if (FALSE) {
sampler <- lcmCR(captures = kosovo_aggregate, tabular = FALSE, in_list_label = '1',
      not_in_list_label = '0', K = 10, a_alpha = 0.25, b_alpha = 0.25, 
      seed = 'auto', buffer_size = 10000, thinning = 100)
#Obtain 1000 samples from the posterior distribution of N#
N <- lcmCR_PostSampl(sampler, burnin = 10000, samples = 1000, thinning = 100, output = FALSE)

#Posterior quantiles#
quantile(N, c(0.025, 0.5, 0.975))

###Example 2: Capture-Recapture estimation using the lcm_CR_Basic object directly###
#Create and initialize an LCMCR object for MCMC sampling#
sampler <- lcmCR(captures = kosovo_aggregate, tabular = FALSE, in_list_label = '1',
                 not_in_list_label = '0', K = 10, a_alpha = 0.25, b_alpha = 0.25, 
                 seed = 'auto', buffer_size = 1000, thinning = 100)

#Run 10000 iterations as burn-in
sampler$Update(10000, output = FALSE)

#List all parameters from the model
sampler$Get_Param_List()

#Set parameter 'n0' for tracing
sampler$Set_Trace('n0')

#List currently traced parameters.
sampler$Get_Trace_List()

#Activate tracing
sampler$Activate_Tracing()

#Run the sampler 100000 times
sampler$Update(100000, output = FALSE)

#Get the 1000 samples from the posterior distribution of N
N <- sampler$Get_Trace('n0') + sampler$n

#Plot the trace of N
plot(N, type = 'l')

#Compute posterior quantiles
quantile(N, c(0.025, 0.5, 0.975))
}