Other packages > Find by keyword >

parallelMCMCcombine  

Combining Subset MCMC Samples to Estimate a Posterior Density
View on CRAN: Click here


Download and install parallelMCMCcombine package within the R console
Install from CRAN:
install.packages("parallelMCMCcombine")

Install from Github:
library("remotes")
install_github("cran/parallelMCMCcombine")

Install by package version:
library("remotes")
install_version("parallelMCMCcombine", "2.0")



Attach the package and use:
library("parallelMCMCcombine")
Maintained by
Erin Conlon
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-06-20
Latest Update: 2021-06-23
Description:
See Miroshnikov and Conlon (2014) . Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.
How to cite:
Erin Conlon (2014). parallelMCMCcombine: Combining Subset MCMC Samples to Estimate a Posterior Density. R package version 2.0, https://cran.r-project.org/web/packages/parallelMCMCcombine. Accessed 18 Sep. 2026.
Previous versions and publish date:
(2026-07-09 06:39), 1.0 (2014-06-20 08:03)
Other packages that cited parallelMCMCcombine R package
View parallelMCMCcombine citation profile
Other R packages that parallelMCMCcombine depends, imports, suggests or enhances
Complete documentation for parallelMCMCcombine
Functions, R codes and Examples using the parallelMCMCcombine R package
Some associated functions: consensusMCcov . consensusMCindep . parallelMCMCcombine-package . sampleAvg . semiparamDPE . 
Some associated R codes: consensusMCcov.R . consensusMCindep.R . sampleAvg.R . semiparamDPE.R .  Full parallelMCMCcombine package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

hmeasure  
The H-Measure and Other Scalar Classification Performance Metrics
Classification performance metrics that are derived from the ROC curve of a classifier. The package ...
Download / Learn more Package Citations See dependency  
data360r  
Wrapper for 'TCdata360' and 'Govdata360' API
Makes it easy to engage with the Application Program Interface (API) of the 'TCdata360' and 'Govdat ...
Download / Learn more Package Citations See dependency  
downlit  
Syntax Highlighting and Automatic Linking
Syntax highlighting of R code, specifically designed for the needs of 'RMarkdown' packages like 'pk ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  
injectoR  
R Dependency Injection
R dependency injection framework. Dependency injection allows a program design to follow the depend ...
Download / Learn more Package Citations See dependency  
eyelinker  
Import ASC Files from EyeLink Eye Trackers
Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for ...
Download / Learn more Package Citations See dependency  

28,565

R Packages

239,283

Dependencies

75,677

Author Associations

28,566

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA