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sourceR Package

Fits a Non-Parametric Bayesian Source Attribution Model using a Dirichlet Process

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.

Version:

0.1.0

Date:

2016-04-22

Author: Poppy Miller, email = p dot miller at lancaster dot ac dot uk

License: GPL-3

Imports:

gtools (>= 3.4.1),
Rlab (>= 2.15.1),
SPIn (>= 1.1),
inline (>= 0.3.13),
Rcpp (>= 0.11.3),
methods

Depends:

R (>= 3.1.2)

Suggests:

cluster (>= 1.15.3),
ggplot2 (>= 1.1.0),
gplots (>= 2.16.0)

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Version

Install

install.packages('sourceR')

Monthly Downloads

35

Version

0.2.0

License

GPL-3

Maintainer

Poppy Miller

Last Published

June 21st, 2016

Functions in sourceR (0.2.0)

flatten

sim_SA_true

summary.source_attribution

sim_SA

Simulated data
campy

Campylobacter source attribution data set from the Manawatu, New Zealand.
sourceR-package

Fits a non-parametric source attribution model
subset_posterior

heatmap_types

Type effect dendrogram