Update docs

DESCRIPTION

@@ -21,7 +21,9 @@ LinkingTo:
     Rcpp,
     RcppArmadillo
 Imports: 
-    Rcpp
+    Rcpp,
+    Rdpack
+RdMacros: Rdpack
 Depends: 
     R (>= 4.1.0)
 Suggests: 

R/compute_sequentially.R

@@ -1,35 +1,72 @@
 #' Compute the Bayesian Mallows model sequentially
 #'
+#' This function performs sequential Bayesian inference for the Mallows model
+#' using the SMC² (Sequential Monte Carlo squared) algorithm. It can handle
+#' both complete/partial rankings and pairwise preference data that arrive
+#' sequentially over time.
 #'
-#' @param data A dataframe containing partial rankings or pairwise preferences.
-#'   If `data` contains complete or partial rankings, it must have the following
-#'   columns:
+#' @param data A data frame containing ranking or preference data with temporal
+#'   structure. The data frame must include `timepoint` and `user` columns.
 #'
-#' \itemize{
-#' \item `timepoint`: a numeric vector denoting the timepoint, starting at 1.
-#' \item `user`: a vector identifying the user.
-#' \item `item1`: ranking of item 1.
-#' \item `item2`: ranking of item 2.
-#' \item etc.
-#' }
+#'   For complete or partial rankings, additional columns should be:
+#'   \itemize{
+#'   \item `timepoint`: Numeric vector denoting the timepoint, starting at 1.
+#'   \item `user`: Vector identifying the user providing the ranking.
+#'   \item `item1`, `item2`, etc.: Rankings of items (use NA for missing items
+#'     in partial rankings).
+#'   }
 #'
-#'   If data contains pairwise preferences, it must have the following
-#'   structure:
+#'   For pairwise preferences, the structure should be:
+#'   \itemize{
+#'   \item `timepoint`: Numeric vector denoting the timepoint, starting at 1.
+#'   \item `user`: Vector identifying the user providing the preference.
+#'   \item `top_item`: Identifier for the preferred item.
+#'   \item `bottom_item`: Identifier for the less preferred item.
+#'   }
 #'
-#' \itemize{
-#' \item `timepoint`: a numeric vector denoting the timepoint, starting at 1.
-#' \item `user`: a vector identifying the user.
-#' \item `top_item`: identifier for the preferred item.
-#' \item `bottom_item`: identifier for the dispreferred item.
-#' }
-#'
-#' @param hyperparameters A list returned from [set_hyperparameters()].
-#' @param smc_options A list returned from [set_smc_options()]
+#' @param hyperparameters A list of hyperparameters returned from
+#'   \code{\link{set_hyperparameters}}. Defines the prior distributions for
+#'   model parameters.
+#' @param smc_options A list of SMC algorithm options returned from
+#'   \code{\link{set_smc_options}}. Controls the behavior of the particle
+#'   filtering algorithm.
 #' @param topological_sorts A list returned from
-#'   [precompute_topological_sorts()]. Only used with preference data, and
-#'   defaults to `NULL`.
+#'   \code{\link{precompute_topological_sorts}}. Required when using pairwise
+#'   preference data, otherwise should be \code{NULL} (default).
+#'
+#' @return An object of class \code{BayesMallowsSMC2} containing the results
+#'   of the sequential inference, including parameter traces, log marginal
+#'   likelihood estimates, and other algorithm diagnostics.
+#'
+#' @examples
+#' # Example with complete rankings
+#' set.seed(123)
+#' n_items <- 4
+#'
+#' # Create synthetic ranking data
+#' ranking_data <- data.frame(
+#'   timepoint = c(1, 1, 2, 2),
+#'   user = c(1, 2, 3, 4),
+#'   item1 = c(1, 2, 1, 3),
+#'   item2 = c(2, 1, 3, 1),
+#'   item3 = c(3, 4, 2, 4),
+#'   item4 = c(4, 3, 4, 2)
+#' )
+#'
+#' # Set up hyperparameters and options
+#' hyper <- set_hyperparameters(n_items = n_items)
+#' opts <- set_smc_options(n_particles = 100, verbose = FALSE)
+#'
+#' # Run sequential inference
+#' result <- compute_sequentially(
+#'   data = ranking_data,
+#'   hyperparameters = hyper,
+#'   smc_options = opts
+#' )
+#'
+#' @references
+#' \insertRef{sorensen2025sequential}{BayesMallowsSMC2}
 #'
-#' @return An object of class BayesMallowsSMC2.
 #' @export
 #'
 compute_sequentially <- function(
@@ -76,7 +113,7 @@ compute_sequentially <- function(
   user_timepoint_combinations <- unique(data[c("user", "timepoint")])
   if(max(table(user_timepoint_combinations$user)) > 1) {
     stop("Each user can only enter the pool once. Users appearing at multiple timepoints: ",
-         paste(names(table(user_timepoint_combinations$user))[table(user_timepoint_combinations$user) > 1], 
+         paste(names(table(user_timepoint_combinations$user))[table(user_timepoint_combinations$user) > 1],
                collapse = ", "))
   }
 

R/set_hyperparameters.R

@@ -1,12 +1,41 @@
-#' Set hyperparameters
+#' Set hyperparameters for Bayesian Mallows model
 #'
-#' @param n_items Integer defining the number of items.
-#' @param alpha_shape Shape of gamma prior for alpha.
-#' @param alpha_rate Rate of gamma prior for alpha.
-#' @param cluster_concentration Concentration parameter of Dirichlet distribution for cluster probabilities.
-#' @param n_clusters Integer defining the number of clusters.
+#' This function creates a list of hyperparameters for the Bayesian Mallows model
+#' used in sequential Monte Carlo inference. The hyperparameters define the prior
+#' distributions for the model parameters.
 #'
-#' @return A list
+#' @param n_items Integer defining the number of items to be ranked. Must be a
+#'   positive integer.
+#' @param alpha_shape Positive numeric value specifying the shape parameter of 
+#'   the gamma prior distribution for the scale parameter alpha. Default is 1.
+#' @param alpha_rate Positive numeric value specifying the rate parameter of 
+#'   the gamma prior distribution for the scale parameter alpha. Default is 0.5.
+#' @param cluster_concentration Positive numeric value specifying the 
+#'   concentration parameter of the Dirichlet distribution for cluster 
+#'   probabilities. Default is 10.
+#' @param n_clusters Positive integer defining the number of clusters in the 
+#'   mixture model. Default is 1.
+#'
+#' @return A list containing the hyperparameter values with elements:
+#'   \item{n_items}{Number of items}
+#'   \item{alpha_shape}{Shape parameter for alpha prior}
+#'   \item{alpha_rate}{Rate parameter for alpha prior}
+#'   \item{cluster_concentration}{Concentration parameter for cluster probabilities}
+#'   \item{n_clusters}{Number of clusters}
+#'
+#' @examples
+#' # Basic hyperparameters for 5 items
+#' hyper <- set_hyperparameters(n_items = 5)
+#' 
+#' # Custom hyperparameters with multiple clusters
+#' hyper <- set_hyperparameters(
+#'   n_items = 10,
+#'   alpha_shape = 2,
+#'   alpha_rate = 1,
+#'   cluster_concentration = 5,
+#'   n_clusters = 3
+#' )
+#' 
 #' @export
 #'
 set_hyperparameters <- function(

R/set_smc_options.R

@@ -1,26 +1,62 @@
-#' Set SMC options
+#' Set SMC options for sequential inference
 #'
-#' @param n_particles Number of particles
-#' @param n_particle_filters Initial number of particle filters for each
-#'   particle
-#' @param max_particle_filters Maximum number of particle filters.
-#' @param resampling_threshold Effective sample size threshold for resampling
-#' @param doubling_threshold Threshold for particle filter doubling. If the
-#'   acceptance rate of the rejuvenation step falls below this threshold, the
-#'   number of particle filters is doubled. Defaults to 0.2.
-#' @param max_rejuvenation_steps Maximum number of rejuvenation steps. If the
-#'   number of unique particles has not exceeded half the number of particles
-#'   after this many steps, the rejuvenation is still stopped.
-#' @param metric Metric
-#' @param resampler resampler
-#' @param latent_rank_proposal latent rank proposal
-#' @param verbose Boolean
-#' @param trace Logical specifying whether to save static parameters at each
-#'   timestep.
-#' @param trace_latent Logical specifying whether to sample and save one
-#'   complete set of latent rankings for each particle and each timepoint.
+#' This function creates a list of options for the Sequential Monte Carlo (SMC²)
+#' algorithm used in Bayesian inference for the Mallows model. These options
+#' control the behavior of the particle filtering and resampling procedures.
 #'
-#' @return A list
+#' @param n_particles Positive integer specifying the number of particles to use
+#'   in the SMC algorithm. Default is 1000.
+#' @param n_particle_filters Positive integer specifying the initial number of 
+#'   particle filters for each particle. Default is 50.
+#' @param max_particle_filters Positive integer specifying the maximum number 
+#'   of particle filters allowed. Default is 10000.
+#' @param resampling_threshold Positive numeric value specifying the effective 
+#'   sample size threshold for triggering resampling. Default is n_particles/2.
+#' @param doubling_threshold Numeric value between 0 and 1 specifying the 
+#'   threshold for particle filter doubling. If the acceptance rate of the 
+#'   rejuvenation step falls below this threshold, the number of particle 
+#'   filters is doubled. Default is 0.2.
+#' @param max_rejuvenation_steps Positive integer specifying the maximum number 
+#'   of rejuvenation steps. If the number of unique particles has not exceeded 
+#'   half the number of particles after this many steps, the rejuvenation is 
+#'   stopped. Default is 20.
+#' @param metric Character string specifying the distance metric to use. 
+#'   Options include "footrule", "kendall", "spearman", "cayley", "hamming", 
+#'   and "ulam". Default is "footrule".
+#' @param resampler Character string specifying the resampling method. 
+#'   Options include "multinomial", "residual", "stratified", and "systematic". 
+#'   Default is "multinomial".
+#' @param latent_rank_proposal Character string specifying the proposal 
+#'   distribution for latent rankings. Default is "uniform".
+#' @param verbose Logical value indicating whether to print progress messages 
+#'   during computation. Default is FALSE.
+#' @param trace Logical value specifying whether to save static parameters at 
+#'   each timestep. Default is FALSE.
+#' @param trace_latent Logical value specifying whether to sample and save one 
+#'   complete set of latent rankings for each particle and each timepoint. 
+#'   Default is FALSE.
+#'
+#' @return A list containing all the SMC options with the specified values.
+#'
+#' @examples
+#' # Default SMC options
+#' opts <- set_smc_options()
+#' 
+#' # Custom SMC options with fewer particles and Kendall distance
+#' opts <- set_smc_options(
+#'   n_particles = 500,
+#'   n_particle_filters = 25,
+#'   metric = "kendall",
+#'   verbose = TRUE
+#' )
+#' 
+#' # Options for tracing parameters
+#' opts <- set_smc_options(
+#'   n_particles = 100,
+#'   trace = TRUE,
+#'   trace_latent = TRUE
+#' )
+#' 
 #' @export
 #'
 set_smc_options <- function(

README.Rmd

@@ -1,5 +1,6 @@
 ---
 output: github_document
+bibliography: inst/REFERENCES.bib
 ---
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
@@ -19,7 +20,9 @@ knitr::opts_chunk$set(
 [![R-CMD-check](https://github.com/osorensen/BayesMallowsSMC2/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/osorensen/BayesMallowsSMC2/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-BayesMallowsSMC2 provides functions for performing sequential inference in the Bayesian Mallows model using the SMC$^{2}$ algorithm.
+BayesMallowsSMC2 provides functions for performing sequential inference in the Bayesian Mallows model using the SMC$^{2}$ algorithm. The package implements the methods described in @sorensen2025sequential.
+
+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
 
@@ -32,6 +35,17 @@ 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 https://osf.io/pquk4/.
+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
 
 

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>

inst/REFERENCES.bib

@@ -0,0 +1,9 @@
+@article{sorensen2025sequential,
+  title={Sequential Rank and Preference Learning with the Bayesian Mallows Model},
+  author={S{\o}rensen, {\O}ystein and Frigessi, Arnoldo and Scheel, Ida},
+  journal={Bayesian Analysis},
+  year={2025},
+  publisher={International Society for Bayesian Analysis},
+  doi={10.1214/25-BA1564},
+  url={https://projecteuclid.org/journals/bayesian-analysis/advance-publication/Sequential-Rank-and-Preference-Learning-with-the-Bayesian-Mallows-Model/10.1214/25-BA1564.full}
+}