Man pages for SamplerCompare
A Framework for Comparing the Performance of MCMC Samplers
| adaptive.metropolis.sample | Adaptive Metropolis |
| ar.act | Compute the autocorrelation time of a chain |
| arms.sample | Adaptive Rejection Metropolis Sampler |
| check.dist.gradient | Test a gradient function |
| chud | Cholesky Update/Downdate |
| compare.samplers | Compare MCMC samplers on distributions |
| comparison.plot | Plot the results of compare.samplers |
| compounded.sampler | Build a sampler from transition functions |
| cov.match.sample | Sample with covariance-matching slice sampling |
| dist-class | A class representing a probability distribution |
| funnel.dist | Funnel distribution object |
| hyperrectangle.sample | Multivariate slice samplers |
| make.c.dist | Define a probability distribution object with C log-density |
| make.cone.dist | Create a cone distribution object |
| make.dist | Define a probability distribution object |
| make.gaussian | Gaussian distribution objects |
| make.multimodal.dist | Create a distribution object for a random mixture of... |
| make.mv.gamma.dist | Create a distribution object for a set of uncorrelated Gamma... |
| multivariate.metropolis.sample | Metropolis samplers |
| nonadaptive.crumb.sample | Sample with nonadaptive-crumb slice sampling |
| oblique.hyperrect.sample | Eigendecomposition-based hyperrectangle method |
| raw.symbol | Locate a symbol |
| schools.dist | Eight schools distribution object |
| shrinking.rank.sample | Sample with shrinking-rank slice sampling |
| simulation.result | Summarize one MCMC chain |
| stepout.slice.sample | Univariate slice samplers |
| twonorm | Euclidean norm of a vector |
| univar.eigen.sample | Eigendecomposition-based slice samplers |
| wrap.c.sampler | Create an R stub function for a sampler implemented in C |
