R: Robust Markov Chain Monte Carlo Methods

# Robust Markov Chain Monte Carlo Methods

## Documentation for package ‘rmcmc’ version 0.1.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

barker_proposal | Create a new Barker proposal object.
bimodal_barker_proposal | Create a new Barker proposal object with bimodal noise distribution.
chain_state | Construct a new chain state.
covariance_shape_adapter | Create object to adapt proposal with shape based on estimate of target distribution covariance matrix.
dual_averaging_scale_adapter | Create object to adapt proposal scale to coerce average acceptance rate using dual averaging scheme of Nesterov (2009) and Hoffman and Gelman (2014).
example_gaussian_stan_model | Construct an example BridgeStan 'StanModel' object for a Gaussian model.
hamiltonian_proposal | Create a new Hamiltonian proposal object.
langevin_proposal | Create a new Langevin proposal object.
random_walk_proposal | Create a new (Gaussian) random walk proposal object.
robust_shape_adapter | Create object to adapt proposal shape (and scale) using robust adaptive Metropolis algorithm of Vihola (2012).
sample_chain | Sample a Markov chain
scale_adapter | Create object to adapt proposal scale to coerce average acceptance rate.
shape_adapter | Create object to adapt proposal shape.
stochastic_approximation_scale_adapter | Create object to adapt proposal scale to coerce average acceptance rate using a Robbins and Monro (1951) scheme.
target_distribution_from_log_density_formula | Construct target distribution from a formula specifying log density.
target_distribution_from_stan_model | Construct target distribution from a BridgeStan 'StanModel' object.
variance_shape_adapter | Create object to adapt proposal with per dimension scales based on estimates of target distribution variances.