An MCMC Sampler Using the t-Walk Algorithm
Calculate MCMC diagnostics
Generate a proposal for the Blow kernel
Generate a proposal for the Hop kernel
Generate a proposal for the Traverse kernel
Generate a proposal for the Walk kernel
Calculate the log of the proposal density for the Blow kernel
Calculate the log of the proposal density for the Hop kernel
Simulate the beta parameter for the Traverse kernel
Run a single t-walk move (step)
Methods for objects of class 'twalk'
Run the t-walk MCMC Algorithm
Visualize MCMC results
Implements the t-walk algorithm, a general-purpose, self-adjusting Markov Chain Monte Carlo (MCMC) sampler for continuous distributions as described by Christen & Fox (2010) <doi:10.1214/10-BA603>. The t-walk requires no tuning and is robust for a wide range of target distributions, including high-dimensional and multimodal problems. This implementation includes an option for running multiple chains in parallel to accelerate sampling and facilitate convergence diagnostics.