person("Michael", "Sumner", email = "mdsumner@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-2471-7511"))
Build a map of path-based geometry, this is a simple description of the number
of parts in an object and their basic structure. Translation and restructuring operations for
planar shapes and other hierarchical types require a data model with a record of the underlying
relationships between elements. The gibble() function creates a geometry map, a simple record of
the underlying structure in path-based hierarchical types. There are methods for the planar shape
types in the 'sf' and 'sp' packages and for types in the 'trip' and 'silicate' packages.

Naive Gibbs Sampling with Metropolis Steps
This package provides two generic functions for performing Markov chain sampling in a naive way for a user-defined target distribution, which involves only continuous variables. The function "gibbs_met" performs Gibbs sampling with each 1-dimensional distribution sampled with Metropolis update using Gaussian proposal distribution centered at the previous state. The function "met_gaussian" updates the whole state with Metropolis method using independent Gaussian proposal distribution centered at the previous state. The sampling is carried out without considering any special tricks for improving efficiency. This package is aimed at only routine applications in moderate-dimensional problems.

This package provides two generic functions for performing
Markov chain sampling in a naive way for a user-defined target
distribution, which involves only continuous variables. The
function "gibbs_met" performs Gibbs sampling with each
1-dimensional distribution sampled with Metropolis update using
Gaussian proposal distribution centered at the previous state.
The function "met_gaussian" updates the whole state with
Metropolis method using independent Gaussian proposal
distribution centered at the previous state. The sampling is
carried out without considering any special tricks for
improving efficiency. This package is aimed at only routine
applications of MCMC in moderate-dimensional problems.