Package: nonparametric.bayes 0.0.1

nonparametric.bayes: Project Code - Nonparametric Bayes

Basic implementation of a Gibbs sampler for a Chinese Restaurant Process along with some visual aids to help understand how the sampling works. This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute.

Authors:Erik-Cristian Seulean [aut, cre]

nonparametric.bayes_0.0.1.tar.gz
nonparametric.bayes_0.0.1.zip(r-4.5)nonparametric.bayes_0.0.1.zip(r-4.4)nonparametric.bayes_0.0.1.zip(r-4.3)
nonparametric.bayes_0.0.1.tgz(r-4.4-any)nonparametric.bayes_0.0.1.tgz(r-4.3-any)
nonparametric.bayes_0.0.1.tar.gz(r-4.5-noble)nonparametric.bayes_0.0.1.tar.gz(r-4.4-noble)
nonparametric.bayes_0.0.1.tgz(r-4.4-emscripten)nonparametric.bayes_0.0.1.tgz(r-4.3-emscripten)
nonparametric.bayes.pdf |nonparametric.bayes.html✨
nonparametric.bayes/json (API)

# Install 'nonparametric.bayes' in R:
install.packages('nonparametric.bayes', repos = c('https://erikseulean.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

6 exports 0.00 score 12 dependencies 190 downloads

Last updated 3 years agofrom:058a8abe6f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 16 2024
R-4.5-winOKSep 16 2024
R-4.5-linuxOKSep 16 2024
R-4.4-winOKSep 16 2024
R-4.4-macOKSep 16 2024
R-4.3-winOKSep 16 2024
R-4.3-macOKSep 16 2024

Exports:cluster_datapointsgenerate_dirichlet_clustersgenerate_dirichlet_clusters_with_sampled_pointsrdirichletrDPMrDPM_visual

Dependencies:clicrayongluehmslifecyclemvtnormpkgconfigprettyunitsprogressR6rlangvctrs

Readme and manuals

Help Manual

Help pageTopics
Gibbs sampling for the Chinese Restaurant Process Implementation details can be found in the associated paper The algorithm stops at every 1000th iteration and prints the current cluster configuration.cluster_datapoints
Draws from a Dirichlet distribution and shows the clusters that were generated by this draw. Varying alpha, will put more or less mass in the first clusters compared to higher clusters (rhos).generate_dirichlet_clusters
Draws from a Dirichlet distribution and shows the clusters that were generated by this draw. Additionally, adds points to these clusters and shows which clusters are occupiedgenerate_dirichlet_clusters_with_sampled_points
Generates a dataset used to exemplify clustering The cluster centers are set relatively far away to see how well the algorithm performs in simple scenariosgenerate_split_data
Generate a sample from a Dirichlet distirbution Using: https://en.wikipedia.org/wiki/Dirichlet_distribution#Random_number_generationrdirichlet
Sequentially generate draws from a Dirichlet process mixture model, by showing step by step the iterations taken. The plot is centered at 0, with x and y from -5 to 5. The mixture draws the centres for clusters from a Normal distribution with mean mu and standard deviation sigma_0 Additional to plotting the points, it also returns the points sampled.rDPM
Sequentially generate draws from a Dirichlet process mixture model, by showing step by step the iterations taken. The plot is centered at 0, with x and y from -5 to 5. The mixture draws the centres for clusters from a Normal distribution with mean mu and standard deviation sigma_0rDPM_visual