postpack: Utilities for Processing Posterior Samples Stored in...
In postpack: Utilities for Processing Posterior Samples Stored in 'mcmc.lists'
postpack R Documentation
Utilities for Processing Posterior Samples Stored in mcmc.lists
Description
The aim of 'postpack' is to provide the infrastructure for a standardized workflow for mcmc.list objects.
These objects can be used to store output from models fitted with Bayesian inference using
JAGS, Win/OpenBUGS, NIMBLE, Stan, or even custom MCMC algorithms (see post_convert() for converting samples to
mcmc.list format). Although the 'coda' package provides some methods for these objects,
it is somewhat limited in easily performing post-processing tasks for particular nodes.
Models are ever increasing in their complexity and the number of tracked nodes, and oftentimes
a user may wish to summarize/diagnose sampling behavior for only a small subset of nodes at a time for a particular question or figure.
Thus, many 'postpack' functions support performing tasks on a subset of nodes, where the subset is specified with regular expressions.
The functions in this package streamline the extraction, summarization, and diagnostics of particular nodes monitored after model fitting.
Further, because there is rarely only ever one model under consideration, 'postpack' scales efficiently to perform the same tasks on output from multiple models
simultaneously, facilitating rapid assessment of model sensitivity to changes in assumptions.
postpack documentation built on Dec. 28, 2022, 1:23 a.m.
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| postpack | R Documentation |
Utilities for Processing Posterior Samples Stored in mcmc.lists
Description
The aim of 'postpack' is to provide the infrastructure for a standardized workflow for mcmc.list objects.
These objects can be used to store output from models fitted with Bayesian inference using
JAGS, Win/OpenBUGS, NIMBLE, Stan, or even custom MCMC algorithms (see post_convert() for converting samples to
mcmc.list format). Although the 'coda' package provides some methods for these objects,
it is somewhat limited in easily performing post-processing tasks for particular nodes.
Models are ever increasing in their complexity and the number of tracked nodes, and oftentimes
a user may wish to summarize/diagnose sampling behavior for only a small subset of nodes at a time for a particular question or figure.
Thus, many 'postpack' functions support performing tasks on a subset of nodes, where the subset is specified with regular expressions.
The functions in this package streamline the extraction, summarization, and diagnostics of particular nodes monitored after model fitting.
Further, because there is rarely only ever one model under consideration, 'postpack' scales efficiently to perform the same tasks on output from multiple models
simultaneously, facilitating rapid assessment of model sensitivity to changes in assumptions.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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