Bayesian Hierarchical modelling techniques are widely used in mathematic psychology, however, many existing methods of estimation are restricted to extensions of previous methods. Following a paper by Gunawan et al (2020, JMP), we present a new R package for a novel sampling methodology – Particle Metropolis within Gibbs (PMwG). This method of particle Markov chain Monte-Carlo provides a more efficient and reliable method of hierarchical model estimation. The R package provides simple functionality, allowing models to be built from the ground up by the user, and is easily parallelisable. Further, the method allows the full parameter covariance matrix to be estimated, which is highly useful in joint-modelling applications. Here, we introduce the PMwG methodology, provide a short tutorial for the ready to use R package and highlight several extensions of the method from the original paper.