An R package for nonparametric inference on dynamic populations with infinitely many types

Abstract

Fleming-Viot diffusions are widely used stochastic models for population dynamics which extend the celebrated Wright-Fisher diffusions. They describe the temporal evolution of the relative frequencies of the allelic types in an ideally infinite panmictic population, whose individuals undergo random genetic drift and at birth can mutate to a new allelic type drawn from a possibly infinite potential pool, independently of their parent. Recently, Bayesian nonparametric inference has been considered for this model when a finite sample of individuals is drawn from the population at several discrete time points. Previous works have fully described the relevant estimators for this problem, but current software is available only for the Wright-Fisher finite-dimensional case. Here we provide software for the general case, overcoming some non trivial computational challenges posed by this setting. The R package FVDDPpkg efficiently approximates the filtering and smoothing distribution for Fleming-Viot diffusions, given finite samples of individuals collected at different times. A suitable Monte Carlo approximation is also introduced in order to reduce the computational cost.

arXiv commentTo appear on Journal of Computational Biology

References in corpus (7)

• Exact simulation of the Wright-Fisher diffusion
• Optimal filtering and the dual process
• Conjugacy properties of time-evolving Dirichlet and gamma random measures
• A Fleming--Viot process and Bayesian nonparametrics
• Smoothing distributions for conditional Fleming-Viot and Dawson-Watanabe diffusions
• Approximate filtering via discrete dual processes
• EWF : simulating exact paths of the Wright--Fisher diffusion

Cited by in corpus (2)

• Dual process in the two-parameter Poisson-Dirichlet diffusion
• Filtering coupled Wright-Fisher diffusions