title: phylodyn: an R package for phylodynamic simulation and inference.
abstract: We introduce phylodyn, an r package for phylodynamic analysis based on gene genealogies. The package's main functionality is Bayesian nonparametric estimation of effective population size fluctuations over time. Our implementation includes several Markov chain Monte Carlo-based methods and an integrated nested Laplace approximation-based approach for phylodynamic inference that have been developed in recent years. Genealogical data describe the timed ancestral relationships of individuals sampled from a population of interest. Here, individuals are assumed to be sampled at the same point in time (isochronous sampling) or at different points in time (heterochronous sampling); in addition, sampling events can be modelled with preferential sampling, which means that the intensity of sampling events is allowed to depend on the effective population size trajectory. We assume the coalescent and the sequentially Markov coalescent processes as generative models of genealogies. We include several coalescent simulation functions that are useful for testing our phylodynamics methods via simulation studies. We compare the performance and outputs of various methods implemented in phylodyn and outline their strengths and weaknesses. r package phylodyn is available at https://github.com/mdkarcher/phylodyn.
authors: Department of Statistics, University of Washington, Seattle, WA, USA.
authors: Michael D
authors: MD
authors: Karcher
authors: Department of Statistics, Stanford University, Stanford, CA, USA.|Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
authors: Julia A
authors: JA
authors: Palacios
authors: Department of Statistics, University of Warwick, Coventry, UK.
authors: Shiwei
authors: S
authors: Lan
authors: Department of Statistics, University of Washington, Seattle, WA, USA.|Department of Biology, University of Washington, Seattle, WA, USA.
authors: Vladimir N
authors: VN
authors: Minin
mesh_terms: D056808:Biostatistics / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005828:Genetics, Population / Q000379:methods*; D011157:Population Dynamics; D012984:Software
keywords: Inla; Mcmc; evolutionary theory; population dynamics; population genetics - theoretical