Package: SANple 0.1.1
SANple: Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo
Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. 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 analyzing 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:
SANple_0.1.1.tar.gz
SANple_0.1.1.tar.gz(r-4.5-noble)SANple_0.1.1.tar.gz(r-4.4-noble)
SANple_0.1.1.tgz(r-4.4-emscripten)SANple_0.1.1.tgz(r-4.3-emscripten)
SANple.pdf |SANple.html✨
SANple/json (API)
NEWS
# Install 'SANple' in R: |
install.packages('SANple', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/laura-dangelo/sanple/issues
Last updated 6 months agofrom:bf4a4adeb8. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-linux-x86_64 | OK | Oct 31 2024 |
Exports:estimate_clusterssample_CAMsample_fiSANsample_fiSAN_sparsemixsample_fSANsample_fSAN_sparsemixtraceplot
Dependencies:clicolorspacefarvergluelabelinglifecyclemunsellR6RColorBrewerRcppRcppArmadilloRcppProgressrlangsalsoscalesviridisLite
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Estimate observational and distributional clusters | estimate_clusters |
Plotting MCMC output | plot.SANmcmc |
Print cluster summary | print.SANclusters |
Print MCMC output | print.SANmcmc |
Sample CAM | sample_CAM |
Sample fiSAN | sample_fiSAN |
Sample fiSAN with sparse mixtures | sample_fiSAN_sparsemix |
Sample fSAN | sample_fSAN |
Sample fSAN with sparse mixtures | sample_fSAN_sparsemix |
Traceplot: plot MCMC chains | traceplot |