README.Rmd

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# SANple v0.2.0 <img src="man/figures/sanple_draft.png" align="right" width="120" />

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The goal of SANple is to estimate Bayesian nested mixture models via MCMC methods. Specifically, the package implements the common atoms model (Denti et al., 2023), hybrid finite-infinite models (D'Angelo and Denti, 2024). 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.

## Installation

You can install the development version of SANple from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("laura-dangelo/SANple")
```

## Example

This is a basic example which shows you how to solve a common problem:

```{r example}
library(SANple)

## basic example code
set.seed(123)
y <- c(rnorm(50,-5,1), rnorm(170,0,1),rnorm(70,5,1))
g <- c(rep(1,150), rep(2, 140))
plot(density(y[g==1]), xlim = c(-10,10), main = "", xlab = "")
lines(density(y[g==2]), col = "cyan4")
out <- sample_fiSAN(nrep = 3000, burn = 1000, y = y, group = g, beta = 1)
out 
clusters <- estimate_clusters(out)
clusters
plot(out, estimated_clusters = clusters)
```

# References

D’Angelo, L., and Denti, F. (2024). A Finite-Infinite Shared Atoms Nested Model for the Bayesian Analysis of Large Grouped Data Sets. *Bayesian Analysis* \doi{10.1214/24-BA1458}

Denti, F., Camerlenghi, F., Guindani, M., Mira, A., 2023. A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data. *Journal of the American Statistical Association*. 118(541), 405–416. \doi{10.1080/01621459.2021.1933499}