RZigZag: RZigZag
In RZigZag: Zig-Zag Sampler

Implements various piecewise deterministic Monte Carlo methods, including the Zig-Zag Sampler (Bierkens, Fearnhead, Roberts, 2019, https://arxiv.org/abs/1607.03188) and the Bouncy Particle Sampler (BPS, Bouchard-Côté et al., 2017, https://arxiv.org/abs/1510.02451). Typical usage consists of first creating a "skeleton" consisting of "events", which can be used directly for plotting trajectories. The skeleton may be post-processed to extract information, such as as moment and covariance estimates, discrete samples at fixed time intervals along the trajectory, effective sample size and asymptotic variance.

This package currently consists of the following functions for generating skeletons: ZigZagLogistic for logistic regression, ZigZagGaussian for multivariate Gaussian, ZigZagIIDGaussian for a IID Gaussian target using Zig-Zag, ZigZagStudentT for spherically symmetric or factorized Student-t distribution, BPSGaussian for multivariate Gaussian using BPS, BPSIIDGaussian for a IID Gaussian target using BPS, BPSStudentT for BPS applied to a spherically symmetric or factorized Student-t distribution. Furthermore the package contains the following functions for post-processing: EstimateESS (to estimate asymptotic variance and effective sample size for individual coordinates), EstimateMoment, EstimateCovarianceMatrix and DiscreteSamples.