README.Rmd

---
output: github_document
bibliography: inst/REFERENCES.bib
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# BayesMallowsSMC2

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BayesMallowsSMC2 provides functions for performing sequential inference in the Bayesian Mallows model using the SMC2 algorithm [@10.1214/25-BA1564].

## Installation

You can install the released version of BayesMallowsSMC2 from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("BayesMallowsSMC2")
```

Or install the development version of BayesMallowsSMC2 from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("osorensen/BayesMallowsSMC2")
```

## Example

Here is a basic example using the included `complete_rankings` dataset:

```{r example}
library(BayesMallowsSMC2)

# Fit the model with complete rankings
set.seed(123)
mod <- compute_sequentially(
  complete_rankings,
  hyperparameters = set_hyperparameters(n_items = 5),
  smc_options = set_smc_options(n_particles = 100, n_particle_filters = 1)
)

# Show model info
mod
```

### Posterior Summaries

We can visualize the posterior distributions of the parameters:

```{r posterior-alpha}
# Posterior distribution of alpha (dispersion parameter)
plot(mod, parameter = "alpha")
```

```{r posterior-rho}
# Posterior distribution of rho (ranking positions)
plot(mod, parameter = "rho", items = c(1, 2, 3))
```

## References