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sanba  

Fitting Shared Atoms Nested Models via MCMC or Variational Bayes
View on CRAN: Click here


Download and install sanba package within the R console
Install from CRAN:
install.packages("sanba")

Install from Github:
library("remotes")
install_github("cran/sanba")

Install by package version:
library("remotes")
install_version("sanba", "0.0.3")



Attach the package and use:
library("sanba")
Maintained by
Francesco Denti
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-05-23
Latest Update: 2025-07-31
Description:
An efficient tool for fitting nested mixture models based on a shared set of atoms via Markov Chain Monte Carlo and variational inference algorithms. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (similar to D'Angelo et al., 2023), and a hybrid finite-infinite model (D'Angelo and Denti, 2024). All models implement univariate nested mixtures with Gaussian kernels equipped 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>, D’Angelo, Denti (2024) <doi:10.1214/24-BA1458>.
How to cite:
Francesco Denti (2025). sanba: Fitting Shared Atoms Nested Models via MCMC or Variational Bayes. R package version 0.0.3, https://cran.r-project.org/web/packages/sanba. Accessed 04 Jul. 2026.
Previous versions and publish date:
0.0.1 (2025-05-23 20:00), 0.0.2 (2025-07-31 18:30), 0.0.3 (2025-09-24 20:40)
Other packages that cited sanba R package
View sanba citation profile
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Complete documentation for sanba
Functions, R codes and Examples using the sanba R package
Full sanba package functions and examples
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