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sourceR (version 1.0.1)

Fits a Non-Parametric Bayesian Source Attribution Model

Description

Implements a non-parametric source attribution model to attribute cases of disease to sources in Bayesian framework with source and type effects. Type effects are clustered using a Dirichlet Process. Multiple times and locations are supported.

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Install

install.packages('sourceR')

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Version

1.0.1

License

GPL-3

Maintainer

Poppy Miller

Last Published

May 9th, 2017

Functions in sourceR (1.0.1)

DirichletProcessNode

FormulaNode

PoissonNode

Constructs prevalence data

AdaptiveMultiMRW

Alpha

Constructs alpha prior

Constructs initial values for q

StochasticNode

campy

Human cases of campylobacteriosis and numbers of source samples positive for

sim_SA

Simulated data: Human cases of campylobacteriosis and numbers of source samples positive for

Constructs source data

Constructs disease count data

sliceTensor

Slices a tensorA::tensor

sourceR

sourceR: A package for fitting Bayesian non-parametric source attribution models.

Alpha_

Alpha prior hyperparameter class

DPModel_impl

Builds the source attribution model. Is not intended to be used by a regular user.

GammaNode

HaldDP

Builds a HaldDP source attribution model