diff --git a/DESCRIPTION b/DESCRIPTION
index edcc18c..51e137e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -21,7 +21,9 @@ LinkingTo:
     Rcpp,
     RcppArmadillo
 Imports: 
-    Rcpp
+    Rcpp,
+    Rdpack
+RdMacros: Rdpack
 Depends: 
     R (>= 4.1.0)
 Suggests: 
diff --git a/R/compute_sequentially.R b/R/compute_sequentially.R
index 14123fc..4ae8b92 100644
--- a/R/compute_sequentially.R
+++ b/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 = ", "))
   }
 
diff --git a/R/set_hyperparameters.R b/R/set_hyperparameters.R
index 9425524..ea53422 100644
--- a/R/set_hyperparameters.R
+++ b/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(
diff --git a/R/set_smc_options.R b/R/set_smc_options.R
index 425e7d8..01ebcda 100644
--- a/R/set_smc_options.R
+++ b/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(
diff --git a/README.Rmd b/README.Rmd
index b02b155..1171598 100644
--- a/README.Rmd
+++ b/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
 
 
diff --git a/README.md b/README.md
index 935ceb4..6070770 100644
--- a/README.md
+++ b/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>
diff --git a/inst/REFERENCES.bib b/inst/REFERENCES.bib
new file mode 100644
index 0000000..46f9118
--- /dev/null
+++ b/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}
+}
diff --git a/man/compute_sequentially.Rd b/man/compute_sequentially.Rd
index 610a73a..645ad50 100644
--- a/man/compute_sequentially.Rd
+++ b/man/compute_sequentially.Rd
@@ -12,39 +12,75 @@ compute_sequentially(
 )
 }
 \arguments{
-\item{data}{A dataframe containing partial rankings or pairwise preferences.
-If \code{data} contains complete or partial rankings, it must have the following
-columns:
+\item{data}{A data frame containing ranking or preference data with temporal
+structure. The data frame must include \code{timepoint} and \code{user} columns.
 
+For complete or partial rankings, additional columns should be:
 \itemize{
-\item \code{timepoint}: a numeric vector denoting the timepoint, starting at 1.
-\item \code{user}: a vector identifying the user.
-\item \code{item1}: ranking of item 1.
-\item \code{item2}: ranking of item 2.
-\item etc.
+\item \code{timepoint}: Numeric vector denoting the timepoint, starting at 1.
+\item \code{user}: Vector identifying the user providing the ranking.
+\item \code{item1}, \code{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 \code{timepoint}: a numeric vector denoting the timepoint, starting at 1.
-\item \code{user}: a vector identifying the user.
-\item \code{top_item}: identifier for the preferred item.
-\item \code{bottom_item}: identifier for the dispreferred item.
+\item \code{timepoint}: Numeric vector denoting the timepoint, starting at 1.
+\item \code{user}: Vector identifying the user providing the preference.
+\item \code{top_item}: Identifier for the preferred item.
+\item \code{bottom_item}: Identifier for the less preferred item.
 }}
 
-\item{hyperparameters}{A list returned from \code{\link[=set_hyperparameters]{set_hyperparameters()}}.}
+\item{hyperparameters}{A list of hyperparameters returned from
+\code{\link{set_hyperparameters}}. Defines the prior distributions for
+model parameters.}
 
-\item{smc_options}{A list returned from \code{\link[=set_smc_options]{set_smc_options()}}}
+\item{smc_options}{A list of SMC algorithm options returned from
+\code{\link{set_smc_options}}. Controls the behavior of the particle
+filtering algorithm.}
 
 \item{topological_sorts}{A list returned from
-\code{\link[=precompute_topological_sorts]{precompute_topological_sorts()}}. Only used with preference data, and
-defaults to \code{NULL}.}
+\code{\link{precompute_topological_sorts}}. Required when using pairwise
+preference data, otherwise should be \code{NULL} (default).}
 }
 \value{
-An object of class BayesMallowsSMC2.
+An object of class \code{BayesMallowsSMC2} containing the results
+of the sequential inference, including parameter traces, log marginal
+likelihood estimates, and other algorithm diagnostics.
 }
 \description{
-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.
+}
+\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}
 }
diff --git a/man/set_hyperparameters.Rd b/man/set_hyperparameters.Rd
index 79c85bc..6193507 100644
--- a/man/set_hyperparameters.Rd
+++ b/man/set_hyperparameters.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/set_hyperparameters.R
 \name{set_hyperparameters}
 \alias{set_hyperparameters}
-\title{Set hyperparameters}
+\title{Set hyperparameters for Bayesian Mallows model}
 \usage{
 set_hyperparameters(
   n_items,
@@ -13,19 +13,46 @@ set_hyperparameters(
 )
 }
 \arguments{
-\item{n_items}{Integer defining the number of items.}
+\item{n_items}{Integer defining the number of items to be ranked. Must be a
+positive integer.}
 
-\item{alpha_shape}{Shape of gamma prior for alpha.}
+\item{alpha_shape}{Positive numeric value specifying the shape parameter of
+the gamma prior distribution for the scale parameter alpha. Default is 1.}
 
-\item{alpha_rate}{Rate of gamma prior for alpha.}
+\item{alpha_rate}{Positive numeric value specifying the rate parameter of
+the gamma prior distribution for the scale parameter alpha. Default is 0.5.}
 
-\item{cluster_concentration}{Concentration parameter of Dirichlet distribution for cluster probabilities.}
+\item{cluster_concentration}{Positive numeric value specifying the
+concentration parameter of the Dirichlet distribution for cluster
+probabilities. Default is 10.}
 
-\item{n_clusters}{Integer defining the number of clusters.}
+\item{n_clusters}{Positive integer defining the number of clusters in the
+mixture model. Default is 1.}
 }
 \value{
-A list
+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}
 }
 \description{
-Set hyperparameters
+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.
+}
+\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
+)
+
 }
diff --git a/man/set_smc_options.Rd b/man/set_smc_options.Rd
index 9eeb81f..0ccb91e 100644
--- a/man/set_smc_options.Rd
+++ b/man/set_smc_options.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/set_smc_options.R
 \name{set_smc_options}
 \alias{set_smc_options}
-\title{Set SMC options}
+\title{Set SMC options for sequential inference}
 \usage{
 set_smc_options(
   n_particles = 1000,
@@ -20,40 +20,74 @@ set_smc_options(
 )
 }
 \arguments{
-\item{n_particles}{Number of particles}
+\item{n_particles}{Positive integer specifying the number of particles to use
+in the SMC algorithm. Default is 1000.}
 
-\item{n_particle_filters}{Initial number of particle filters for each
-particle}
+\item{n_particle_filters}{Positive integer specifying the initial number of
+particle filters for each particle. Default is 50.}
 
-\item{max_particle_filters}{Maximum number of particle filters.}
+\item{max_particle_filters}{Positive integer specifying the maximum number
+of particle filters allowed. Default is 10000.}
 
-\item{resampling_threshold}{Effective sample size threshold for resampling}
+\item{resampling_threshold}{Positive numeric value specifying the effective
+sample size threshold for triggering resampling. Default is n_particles/2.}
 
-\item{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.}
+\item{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.}
 
-\item{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.}
+\item{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.}
 
-\item{metric}{Metric}
+\item{metric}{Character string specifying the distance metric to use.
+Options include "footrule", "kendall", "spearman", "cayley", "hamming",
+and "ulam". Default is "footrule".}
 
-\item{resampler}{resampler}
+\item{resampler}{Character string specifying the resampling method.
+Options include "multinomial", "residual", "stratified", and "systematic".
+Default is "multinomial".}
 
-\item{latent_rank_proposal}{latent rank proposal}
+\item{latent_rank_proposal}{Character string specifying the proposal
+distribution for latent rankings. Default is "uniform".}
 
-\item{verbose}{Boolean}
+\item{verbose}{Logical value indicating whether to print progress messages
+during computation. Default is FALSE.}
 
-\item{trace}{Logical specifying whether to save static parameters at each
-timestep.}
+\item{trace}{Logical value specifying whether to save static parameters at
+each timestep. Default is FALSE.}
 
-\item{trace_latent}{Logical specifying whether to sample and save one
-complete set of latent rankings for each particle and each timepoint.}
+\item{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.}
 }
 \value{
-A list
+A list containing all the SMC options with the specified values.
 }
 \description{
-Set SMC options
+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.
+}
+\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
+)
+
 }