{ "_id": "6a3990604c3da884a92894fe", "Package": "imuGAP", "Title": "Immunity: Geographic and Age-Based Projection", "Version": "0.1.0", "Authors@R": "c(person(\"Carl\", \"Pearson\", email = \"carl.ab.pearson@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-0701-7860\")),\nperson(\"Claire Perrin\", \"Smith\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-1069-9460\")),\nperson(\"Kelly\", \"Zhen\", email = \"zhen717605@gmail.com\", role = \"ctb\"),\nperson(\"Weston\", \"Voglesonger\", email = \"westonvogle@gmail.com\", role = \"ctb\"),\nperson(\"Joshua\", \"Chen\", email = \"joshuazchen1@gmail.com\", role = \"ctb\"),\nperson(\"Minjae\", \"Kung\", email = \"minjaekung@gmail.com\", role = \"ctb\"))", "Description": "Fits Bayesian hierarchical models of vaccine coverage by\nlocation, birth cohort, and age. Observations of vaccination\nstatus (which may span cohorts, ages, and doses) are used to\nfit a latent survival-style process model that decomposes\ncoverage into a lifetime propensity to vaccinate and a\ntime-varying force of vaccination. Hierarchical spatial\nstructure (e.g., state, county, school) supports partial\npooling via random effects. Models are implemented in 'Stan'\nand fit via 'rstan'. 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