Functions for performing Bayesian `similarity regression', adapted to regression of binary genotype against HPO encoded phenotype. The purpose is to weigh the evidence for an association between phenotype and genotype. This is done by estimating a Bayes factor comparing a `no association' model with one in which log odds of presenting a particular binary genotype is linked to the semantic similarity between the corresponding phenotype and an estimated characteristic phenotype.
Rcpp (>= 0.11.1), dplyr, parallel, ggplot2, plotrix,
gridExtra, reshape2

Functions for performing Bayesian `similarity regression', adapted to model the association between an HPO-encoded patient phenotype and binary genotype. A `no association' model is compared with one in which the log odds of having a rare genotype is linked to the semantic similarity between patient phenotype and a latent characteristic phenotype. The method estimates the probability of an association together with an HPO-coded phenotype characteristic of the disease.

Functions for performing Bayesian `similarity regression',
specialised for modelling the association between a HPO-encoded phenotype
and binary genotype. A `no association' model is compared with one in which
the log odds of having a rare genotype is linked to the semantic similarity
between patient phenotype and a latent characteristic phenotype. The method
estimates the probability of an association together with an HPO-coded phenotype
characteristic of the disease.
Rcpp (>= 0.11.1), ontologyIndex, ontologySimilarity,
ontologyPlot, plotrix

Rcpp (>= 0.11.1), ontologyIndex (>= 2.0), ontologySimilarity
(>= 2.0), ontologyPlot

Functions for performing Bayesian similarity regression,
and evaluating the probability of association between sets of ontological terms
and binary response vector. A random model is compared with one in which
the log odds of a true response is linked to the semantic similarity
between terms and a latent characteristic ontological profile.

Similarity regression,
evaluating the probability of association between sets of ontological terms
and binary response vector. A no-association model is compared with one in which
the log odds of a true response is linked to the semantic similarity
between terms and a latent characteristic ontological profile - 'Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases', Greene et al 2016 <doi:10.1016/j.ajhg.2016.01.008>.