Introduction | How the package works | Model specification through the specify_model function | Data generation with the generate_data function | Sampler specification throuth the specify_sampler function | MCMC inference using the nrMCmix function | Posterior summaries with the functions print, summary and plot | Practical examples | Galaxy dataset | Iris dataset | Simulated dataset | Bibliography
Package: nrMCmix 0.3
Paolo Manildo
nrMCmix: Non-Reversible Markov Chains for Bayesian Mixture Models
Provides Bayesian inference for finite and infinite mixture models using Markov chain Monte Carlo (MCMC) methods. Supports Gaussian, multivariate Gaussian, Poisson, Binomial, and multivariate Bernoulli mixture kernels with different covariance structures and flexible prior specifications. Provides tools for data generation, posterior density evaluation, clustering and similarity analysis, and advanced MCMC sampling strategies, including reversible and non-reversible samplers: Ascolani et al. (2026) <doi:10.48550/arXiv.2510.03226>, informed proposals: Zanella (2020) <doi:10.1080/01621459.2019.1585255>, collapsed Gibbs sampling: Diebolt and Robert (1994) <doi:10.1111/j.2517-6161.1994.tb01985.x>, and split-and-merge samplers: Dahl and Newcomb (2021) <doi:10.1080/00949655.2021.1998502>.
Authors:
nrMCmix_0.3.tar.gz
nrMCmix_0.3.zip(r-4.7-x86_64)nrMCmix_0.3.zip(r-4.7-arm64)nrMCmix_0.3.zip(r-4.6-x86_64)nrMCmix_0.3.zip(r-4.6-arm64)
nrMCmix_0.3.tar.gz(r-4.7-arm64)nrMCmix_0.3.tar.gz(r-4.7-x86_64)nrMCmix_0.3.tar.gz(r-4.6-arm64)nrMCmix_0.3.tar.gz(r-4.6-x86_64)
nrMCmix_0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
nrMCmix/json (API)
| # Install 'nrMCmix' in R: |
| install.packages('nrMCmix', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Uses libs:
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:4c7dcc5d81. Checks:10 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 155 | ||
| linux-devel-x86_64 | OK | 165 | ||
| source / vignettes | OK | 335 | ||
| linux-release-arm64 | OK | 135 | ||
| linux-release-x86_64 | OK | 168 | ||
| windows-devel-arm64 | OK | 251 | ||
| windows-devel-x86_64 | OK | 198 | ||
| windows-release-arm64 | OK | 256 | ||
| windows-release-x86_64 | OK | 210 | ||
| wasm-release | OK | 138 |
Exports:compute_similarity_matrixextract_paramsgenerate_datanrMCmixplot.nrMCmixposterior_density1posterior_density2print.nrMCmixprint.summary.nrMCmixpriorKspecify_modelspecify_samplersummary.nrMCmix
Dependencies:clicodacpp11dotCall64farverfieldsggplot2gluegtableisobandlabelinglatticelifecyclemapsR6rbibutilsRColorBrewerRcppRcppArmadilloRdpackrlangS7salsoscalesspamvctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Non-Reversible Markov Chains for Bayesian Mixture Models | nrMCmix-package |
| Compute the posterior similarity matrix | compute_similarity_matrix |
| Extract a Parameter from a 'nrMCmix' object. | extract_params |
| Simulate data from a Bayesian mixture-model generative process. | generate_data |
| MCMC inference for Bayesian mixture models | nrMCmix |
| Plot diagnostics for an 'nrMCmix' model | plot.nrMCmix |
| Evaluate the posterior predictive density | posterior_density1 |
| Evaluate the bivariate posterior predictive density | posterior_density2 |
| Print an 'nrMCmix' model fit | print.nrMCmix |
| Print an 'nrMCmix' model summary | print.summary.nrMCmix |
| Prior distribution of the number of clusters | priorK |
| Specify the model and prior distributions | specify_model |
| Specify the sampling algorithm | specify_sampler |
| Summarize an nrMCmix model fit | summary.nrMCmix |