compiling docs

Author: osorensen

R/RcppExports.R

@@ -21,14 +21,14 @@
 #'
 #' @export
 #' @examples
-#' # Create simple preference data: item 1 > item 2 > item 3
-#' prefs <- matrix(c(1, 2, 2, 3), ncol = 2, byrow = TRUE)
-#' colnames(prefs) <- c("top_item", "bottom_item")
+#' # Extract preferences from user 1 in the included example data.
+#' prefs <- pairwise_preferences[
+#'  pairwise_preferences$user == 1, c("top_item", "bottom_item"), drop = FALSE]
 #'
 #' # Generate all topological sorts, but don't save them:
 #' sorts <- precompute_topological_sorts(
-#'   prefs = prefs,
-#'   n_items = 3,
+#'   prefs = as.matrix(prefs),
+#'   n_items = 5,
 #'   save_frac = 0
 #' )
 #' # Number of sorts
@@ -38,8 +38,8 @@
 #'
 #' # Generate all topological sorts and save them:
 #' sorts <- precompute_topological_sorts(
-#'   prefs = prefs,
-#'   n_items = 3,
+#'   prefs = as.matrix(prefs),
+#'   n_items = 5,
 #'   save_frac = 1
 #' )
 #' # Number of sorts
@@ -51,19 +51,6 @@ precompute_topological_sorts <- function(prefs, n_items, save_frac) {
     .Call(`_BayesMallowsSMC2_precompute_topological_sorts`, prefs, n_items, save_frac)
 }
 
-#' Run Sequential Monte Carlo Algorithm (Internal Function)
-#'
-#' This is an internal function that implements the core SMC² algorithm.
-#' Users should typically use \code{\link{compute_sequentially}} instead.
-#'
-#' @param input_timeseries List of time series data.
-#' @param input_prior List of prior parameters.
-#' @param input_options List of algorithm options.
-#' @param input_sort_matrices List of precomputed sort matrices.
-#' @param input_sort_counts List of precomputed sort counts.
-#'
-#' @return List containing SMC algorithm results.
-#' @keywords internal
 run_smc <- function(input_timeseries, input_prior, input_options, input_sort_matrices, input_sort_counts) {
     .Call(`_BayesMallowsSMC2_run_smc`, input_timeseries, input_prior, input_options, input_sort_matrices, input_sort_counts)
 }

README.md

@@ -9,7 +9,15 @@
 <!-- badges: end -->
 
 BayesMallowsSMC2 provides functions for performing sequential inference
-in the Bayesian Mallows model using the SMC$^{2}$ algorithm.
+in the Bayesian Mallows model using the SMC$^{2}$ algorithm. The package
+implements the methods described in Sørensen, Frigessi, and Scheel
+(2025).
+
+The Bayesian Mallows model is a probabilistic framework for analyzing
+ranking data, and this package extends it to handle sequential learning
+scenarios where rankings arrive over time. The SMC$^{2}$ (Sequential
+Monte Carlo squared) algorithm enables efficient Bayesian inference by
+combining particle filtering with MCMC methods.
 
 ## Installation
 
@@ -23,6 +31,31 @@ devtools::install_github("osorensen/BayesMallowsSMC2")
 
 ## Usage
 
-This package is under development, and is not yet well documented. For
-examples on how to use it, see the code in the OSF repository
+This package implements sequential Bayesian inference for ranking data
+using the Mallows model. The main function is `compute_sequentially()`,
+which performs SMC$^{2}$ inference as rankings arrive over time.
+
+``` r
+library(BayesMallowsSMC2)
+
+# Example usage (see vignettes for detailed examples)
+# result <- compute_sequentially(data, hyperparameters, smc_options)
+```
+
+For detailed examples and reproducible code, see the OSF repository at
 <https://osf.io/pquk4/>.
+
+## References
+
+<div id="refs" class="references csl-bib-body hanging-indent"
+entry-spacing="0">
+
+<div id="ref-sorensen2025sequential" class="csl-entry">
+
+Sørensen, Øystein, Arnoldo Frigessi, and Ida Scheel. 2025. “Sequential
+Rank and Preference Learning with the Bayesian Mallows Model.” *Bayesian
+Analysis*. <https://doi.org/10.1214/25-BA1564>.
+
+</div>
+
+</div>