{ "_id": "6a10436bacfb0bcc41c9def9", "Package": "GPRMortality", "Type": "Package", "Title": "Gaussian Process Regression for Mortality Rates", "Version": "0.1.0", "Author": "Parinaz Mehdipour [aut], Ali Ghanbari\n [cre,aut] , Iman Navidi\n [aut], Farshad Farzadfar\n [cph]", "Maintainer": "Ali Ghanbari ", "Description": "A Bayesian statistical model for estimating child\n(under-five age group) and adult (15-60 age group) mortality.\nThe main challenge is how to combine and integrate these\ndifferent time series and how to produce unified estimates of\nmortality rates during a specified time span. GPR is a Bayesian\nstatistical model for estimating child and adult mortality\nrates which its data likelihood is mortality rates from\ndifferent data sources such as: Death Registration System,\nCensuses or surveys. There are also various hyper-parameters\nfor completeness of DRS, mean, covariance functions and\nvariances as priors. This function produces estimations and\nuncertainty (95% or any desirable percentiles) based on\nsampling and non-sampling errors due to variation in data\nsources. The GP model utilizes Bayesian inference to update\npredicted mortality rates as a posterior in Bayes rule by\ncombining data and a prior probability distribution over\nparameters in mean, covariance function, and the regression\nmodel. This package uses Markov Chain Monte Carlo (MCMC) to\nsample from posterior probability distribution by 'rstan'\npackage in R. Details are given in Wang H, Dwyer-Lindgren L,\nLofgren KT, et al. (2012) ,\nWang H, Liddell CA, Coates MM, et al. (2014)\n and Mohammadi, Parsaeian,\nMehdipour et al. 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