MasterBayes is an R package that uses Markov chain Monte Carlo methods for estimating the joint posterior distribution of a pedigree and the parameters that predict its structure using genetic and phenotypic data (Hadfield 2006). Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree, and these parameters can be associated with covariates of fecundity such as a sexually selected trait or age, or can be associated with spatial or heritable traits that relate parents to specific offspring. Cases where some of the parents have not been sampled can also be handled and the size of the unsampled population estimated. The genetic markers can be dominant or codominant and genotyping errors are accommodated and their rate of occurrence can be estimated. Much of MasterBayes is written in compiled C++ using the Scythe Statistical Library for speed.

  • Hadfield JD, Richardson DS & Burke T (2006) Towards unbiased parentage assignment: combining genetic, behavioural and spatial data in a Bayesian framework Molecular Ecology 15 12 3715-3730


    MCMCglmm is an R package for fitting Generalised Linear Mixed Models using Markov chain Monte Carlo techniques (Hadfield 2010). Most commonly used distributions like the normal and the Poisson are supported together with some useful but less popular ones like the zero-inflated Poisson and the multinomial. Missing values and left, right and interval censoring are accommodated for all traits. The package also supports multi-trait models where the multiple responses can follow different types of distribution. The package allows various residual and random-effect variance structures to be specified including heterogeneous variances, unstructured covariance matrices and random regression (e.g. random slope models). Three special types of variance structure that can be specified are those associated with pedigrees (animal models), phylogenies (the comparative method) and measurement error (meta-analysis). The package makes heavy use of results in Sorensen & Gianola (2002) and Davis (2006) and much of it is written in compiled C/C++, which taken together result in what is hopefully a fast and efficient routine. The implementaion for reduced phyloegentic models detailed in Hadfield (2015) can be downloaded from here.

  • Hadfield JD (2010) MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package Journal of Statistical Software 33 2 1-22
  • Sorensen D & Gianola D (2002) Likelihood, Bayesian and MCMC Methods in Quantitative Genetics Springer
  • Davis T (2006) Direct Methods for Sparse Linear Systems SIAM
  • Hadfield JD (2015) Increasing the Efficiency of MCMC for Hierarchical Phylogenetic Models of Categorical Traits using Reduced Mixed Models Method in Ecology & Evolution in press.