gRaven - Bayes Nets: 'RHugin' Emulation with 'gRain'

Wrappers for functions in the 'gRain' package to emulate some 'RHugin' functionality, allowing the building of Bayesian networks consisting on discrete chance nodes incrementally, through adding nodes, edges and conditional probability tables, the setting of evidence, both 'hard' (boolean) or 'soft' (likelihoods), querying marginal probabilities and normalizing constants, and generating sets of high-probability configurations. Computations will typically not be so fast as they are with 'RHugin', but this package should assist users without access to 'Hugin' to use code written to use 'RHugin'.

Last updated 1 months ago

2.48 score 2 packages 2 scripts 386 downloads

KinMixLite - Inference About Relationships from DNA Mixtures

Methods for inference about relationships between contributors to a DNA mixture and other individuals of known genotype: a basic example would be testing whether a contributor to a mixture is the father of a child of known genotype. This provides most of the functionality of the 'KinMix' package, but with some loss of efficiency and restriction on problem size, as the latter uses 'RHugin' as the Bayes net engine, while this package uses 'gRain'. The package implements the methods introduced in Green, P. J. and Mortera, J. (2017) <doi:10.1016/j.fsigen.2017.02.001> and Green, P. J. and Mortera, J. (2021) <doi:10.1111/rssc.12498>.

Last updated 2 months ago

1.00 score 338 downloads

Nmix - Bayesian Inference on Univariate Normal Mixtures

A program for Bayesian analysis of univariate normal mixtures with an unknown number of components, following the approach of Richardson and Green (1997) <doi:10.1111/1467-9868.00095>. This makes use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter sub-spaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution.

Last updated 1 years ago

1.00 score 321 downloads