Package: SANvi 0.1.1
Francesco Denti
SANvi: Fitting Shared Atoms Nested Models via Variational Bayes
An efficient tool for fitting the nested common and shared atoms models using variational Bayes approximate inference for fast computation. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, D’Angelo, Canale, Yu, Guindani (2023) <doi:10.1111/biom.13626>.
Authors:
SANvi_0.1.1.tar.gz
SANvi_0.1.1.tar.gz(r-4.5-noble)SANvi_0.1.1.tar.gz(r-4.4-noble)
SANvi_0.1.1.tgz(r-4.4-emscripten)SANvi_0.1.1.tgz(r-4.3-emscripten)
SANvi.pdf |SANvi.html✨
SANvi/json (API)
NEWS
| # Install 'SANvi' in R: |
| install.packages('SANvi', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/fradenti/sanvi/issues
Last updated 7 months agofrom:65f991746a. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 12 2024 |
| R-4.5-linux-x86_64 | OK | Nov 12 2024 |
Exports:estimate_atoms_weights_viestimate_clustering_viextract_bestvariational_CAMvariational_fiSANvariational_fSANvariational_multistart
Dependencies:clicolorspacefarvergluelabelinglifecyclematrixStatsmunsellR6RColorBrewerRcppRcppArmadillorlangscalesviridisLite
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Estimate the Posterior Atoms and Weights of the Discrete Mixing Distributions | estimate_atoms_weights_vi plot.vi_atoms_weights print.vi_atoms_weights |
| Estimate Posterior Clustering Assignments | estimate_clustering_vi plot.vi_clustering print.vi_clustering |
| Extract the best run from multiple trials | extract_best |
| Plotting the variational inference output | plot.SANvb |
| Print variational inference output | print.SANvb |
| Mean Field Variational Bayes estimation of CAM | variational_CAM |
| Mean Field Variational Bayes estimation of fiSAN | variational_fiSAN |
| Mean Field Variational Bayes estimation of fSAN | variational_fSAN |
| Perform variational inference using multiple starting points. | plot.multistart print.multistart variational_multistart |
