{"Package":"dga","Type":"Package","Title":"Capture-Recapture Estimation using Bayesian Model Averaging","Version":"2.0.1","Date":"2021-05-04","Authors@R":"c(person(given=\"James\", family=\"Johndrow\", role=c(\"aut\")),\nperson(given=\"Kristian\", family=\"Lum\", email=\"kl@hrdag.org\", role=c(\"aut\")),\nperson(given=\"Patrick\", family=\"Ball\", role=c(\"aut\")),\nperson(given=\"Olivier\", family=\"Binette\", email=\"olivier.binette@gmail.com\", role=c(\"ctb\", \"cre\")))","Description":"Performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.","License":"GPL (>= 2)","LazyLoad":"yes","NeedsCompilation":"yes","ByteCompile":"yes","Imports":"chron, Rcpp","RoxygenNote":"7.1.1","LinkingTo":"Rcpp, RcppArmadillo","Suggests":"testthat","Packaged":"2021-05-04 20:21:44 UTC; ob37","Author":"James Johndrow [aut],\nKristian Lum [aut],\nPatrick Ball [aut],\nOlivier Binette [ctb, cre]","Maintainer":"Olivier Binette ","Repository":"CRAN","Date/Publication":"2021-05-10 16:22:11 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/dga_2.0.1.tar.gz","jsonAuthors":[{"name":"James Johndrow","email":null,"maintainer":false},{"name":"Kristian Lum","email":"kl@hrdag.org","maintainer":false},{"name":"Patrick Ball","email":null,"maintainer":false},{"name":"Olivier Binette","email":"olivier.binette@gmail.com","maintainer":true}],"readme":"[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/dga)](https://cran.r-project.org/package=dga)\n [![R-CMD-check](https://github.com/OlivierBinette/DGA/workflows/R-CMD-check/badge.svg)](https://github.com/OlivierBinette/DGA/actions)\n\nDGA\n===\n\nPerforms capture-recapture estimation by averaging over decomposable graphical models. This implements the approach introduced in Madigan and York (1997).\n\n## Installation\nStable CRAN release:\n```r\ninstall.packages(\"dga\")\n```\n\n## Example usage\n\nFive lists example from Madigan and York (1997):\n\n```r\nlibrary(dga)\n\n# Number of lists and prior hyperparameter\np <- 5\ndata(graphs5) # Decomposable graphical models on 5 lists.\ndelta <- 0.5\nNmissing <- 1:300 # Reasonable range for the number of unobserved individuals.\n\n# Counts corresponding to list inclusion patterns.\nY <- c(0,27,37,19,4,4,1,1,97,22,37,25,2,1,3,5,83,36,34,18,3,5,0,2,30,5,23,8,0,3,0,2)\nY <- array(Y, dim=c(2,2,2,2,2))\nN <- sum(Y) + Nmissing\n\n# Model-wise posterior probaiblities on the total population size.\n# weights[i,j] is the posterior probability for j missing individuals under model graphs5[[j]].\nweights <- bma.cr(Y, Nmissing, delta, graphs5)\n\n# Plot of the posterior distribution.\nplotPosteriorN(weights, N)\n```\n\n\n

\n\n\n## References\n\n- David Madigan and Jeremy C. York. \"Bayesian methods for estimation of the size of a closed population.\" _Biometrika_. Vol. 84, No. 1 (Mar., 1997), pp. 19-31.\n- Mauricio Sadinle (2018) Bayesian propagation of record linkage uncertainty into population size estimation of human rights violations. _Annals of Applied Statistics_ Vol. 12 No. 2 pp. 1013-1038\n\n\nWritten by James Johndrow, Kristian Lum, and Patrick Ball. \n\ncopyright (c) 2015 Human Rights Data Analysis Group (HRDAG)\nhttps://hrdag.org\n","jobInfo":{"package":"dga","version":"2.0.1","parsingStatus":"success","parserVersion":1,"parsedAt":"2021-05-10T18:30:23+0000"}}

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