Package: waspr 1.0.1

Jolien Cremers

waspr: Wasserstein Barycenters of Subset Posteriors

Functions to compute Wasserstein barycenters of subset posteriors using the swapping algorithm developed by Puccetti, Rüschendorf and Vanduffel (2020) <doi:10.1016/j.jmaa.2017.02.003>. The Wasserstein barycenter is a geometric approach for combining subset posteriors. It allows for parallel and distributed computation of the posterior in case of complex models and/or big datasets, thereby increasing computational speed tremendously.

Authors:Jolien Cremers [aut, cre]

waspr_1.0.1.tar.gz
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waspr_1.0.1.tgz(r-4.4-emscripten)waspr_1.0.1.tgz(r-4.3-emscripten)
waspr.pdf |waspr.html✨
waspr/json (API)
NEWS

# Install 'waspr' in R:
install.packages('waspr', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/joliencremers/waspr/issues0 issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

openblascpp

2.70 score 109 downloads 4 exports 3 dependencies

Last updated 2 years agofrom:21ee033d1d. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 24 2025
R-4.5-linux-x86_64OKMar 24 2025
R-4.4-linux-x86_64OKMar 24 2025

Exports:hpd_estmode_estsummarywasp

Dependencies:BHRcppRcppArmadillo

Tutorial

Rendered fromTutorial.Rmdusingknitr::rmarkdownon Mar 24 2025.

Last update: 2020-07-24
Started: 2020-07-24

Citation

To cite package ‘waspr’ in publications use:

Cremers J (2023). waspr: Wasserstein Barycenters of Subset Posteriors. R package version 1.0.1, https://CRAN.R-project.org/package=waspr.

Corresponding BibTeX entry: 

  @Manual{,
    title = {waspr: Wasserstein Barycenters of Subset Posteriors},
    author = {Jolien Cremers},
    year = {2023},
    note = {R package version 1.0.1},
    url = {https://CRAN.R-project.org/package=waspr},
  }

Readme and manuals

waspr

The goal of waspr is to compute Wasserstein barycenters of subset posteriors.

Installation

The R-package waspr can be installed from CRAN as follows:

install.packages("waspr")

You can install a beta-version of waspr from github with:

install.packages("devtools")
devtools::install_github("joliencremers/waspr")

Citation

To cite the package ‘waspr’ in publications use:

Jolien Cremers (2020). waspr: Wasserstein Barycenters of Subset Posteriors. R package version 1.0.1. https://CRAN.R-project.org/package=waspr

or

Jolien Cremers (2020). waspr: Wasserstein Barycenters of Subset Posteriors. Zenodo, doi: 10.5281/zenodo.3971910

Example

This is a basic example which shows you how to compute the Wasserstein barycenter from a set of MCMC outputs for several data subsets. A more extensive explanation of the usage of the package can be found in the Tutorial vignette.

library(waspr)
#> 
#> Attaching package: 'waspr'
#> The following object is masked from 'package:base':
#> 
#>     summary

wasp(pois_logistic,
     par.names = c("beta_s", "alpha_l", "beta_l",
                   "baseline_sigma", "baseline_mu",
                   "correlation", "sigma_s", "sigma_l"))
#> 
#> 
#> WASP 
#> 
#> Call: 
#> wasp(mcmc = pois_logistic, par.names = c("beta_s", "alpha_l", 
#>     "beta_l", "baseline_sigma", "baseline_mu", "correlation", 
#>     "sigma_s", "sigma_l"))
#> 
#> Swapping algorithm: 
#> iter = 10
#> acc = 0.001
#> 
#> MCMC: 
#> subsets = 8
#> parameters = 8
#> samples = 450
#> 
#> Posterior summary of the Wasserstein Barycenter: 
#>                      mean       mode         sd      LB HPD     UB HPD
#> beta_s          0.5527601  0.5518034 0.10988949  0.36598187  0.7896041
#> alpha_l         2.6811079  2.6959176 0.19199304  2.30380675  3.0295802
#> beta_l          0.7508520  0.7339988 0.21631011  0.37281283  1.1740767
#> baseline_sigma  0.3563222  0.3811609 0.06859910  0.21910807  0.4870079
#> baseline_mu    -0.8008872 -0.7516167 0.10867533 -1.01168299 -0.5944583
#> correlation     0.1732170  0.1392670 0.07437737  0.02824474  0.3059979
#> sigma_s         1.7225455  1.7535499 0.17920847  1.40126462  2.0610585
#> sigma_l         1.2190297  1.2612822 0.07558163  1.06768047  1.3569757