Imports: Rcpp (>= 0.11.1), ontologyIndex (>= 2.0), ontologySimilarity

Description: 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.

evaluating the probability of association between sets of ontological terms
and binary response vector. A no-association model is compared with one in which
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>.

Title: 'Simulates Univariate and Semi-Competing Risks Data Given
Covariates and Piecewise Exponential Baseline Hazards'
Maintainer: Andrew G Chapple <Andrew.G.Chapple@rice.edu>
Description: Contains two functions for simulating survival data from piecewise exponential hazards with a proportional hazards adjustment for covariates. The first function SimUNIVPiecewise simulates univariate survival data based on a piecewise exponential hazard, covariate matrix and true regression vector. The second function SimSCRPiecewise semi-competing risks data based on three piecewise exponential hazards, three true regression vectors and three matrices of patient covariates (which can be different or the same). This simulates from the Semi-Markov model of Lee et al (2015) given patient covariates, regression parameters, patient frailties and baseline hazard functions.

Title: Nonparametric Simulation of RNA-Seq Data
Description: RNA-Seq analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice.  Methods are often tested by analyzing data that have been simulated according to the assumed model.  This testing strategy can result in an overly optimistic view of the performance of an RNA-Seq analysis method.  Rather than generating data from a questionable parametric model, the SimSeq package provides tools for simulating RNA-Seq data by subsampling from existing datasets.  The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of actual RNA-Seq data.  Users can control the proportion of genes simulated to be differentially expressed (DE) and can adjust the magnitude of differences for DE genes. SimSeq requires a matrix of RNA-Seq read counts with large sample sizes in at least two treatment groups. Although a full version of such a dataset is not distributed with the package, numerous candidate datasets are publicly available.