getting rid of NOTEs

Author: osorensen

NAMESPACE

@@ -10,4 +10,5 @@ export(set_hyperparameters)
 export(set_smc_options)
 export(trace_plot)
 importFrom(Rcpp,sourceCpp)
+importFrom(Rdpack,reprompt)
 useDynLib(BayesMallowsSMC2, .registration = TRUE)

R/BayesMallowsSMC2-package.R

@@ -1,4 +1,5 @@
 ## usethis namespace: start
+#' @importFrom Rdpack reprompt
 #' @importFrom Rcpp sourceCpp
 ## usethis namespace: end
 NULL

R/print.R

@@ -43,37 +43,37 @@ print.BayesMallowsSMC2 <- function(x, ...) {
   if (!inherits(x, "BayesMallowsSMC2")) {
     stop("x must be an object of class 'BayesMallowsSMC2'")
   }
-  
+
   required_fields <- c("alpha", "rho", "ESS", "resampling", "log_marginal_likelihood")
   missing_fields <- setdiff(required_fields, names(x))
   if (length(missing_fields) > 0) {
     stop("Object is missing required fields: ", paste(missing_fields, collapse = ", "))
   }
-  
+
   # Extract dimensions
   n_particles <- ncol(x$alpha)
   n_timepoints <- length(x$ESS)
   n_items <- dim(x$rho)[1]
   n_clusters <- nrow(x$alpha)
-  
+
   # Count resampling events
   n_resampling_events <- sum(x$resampling)
-  
+
   # Create header
   cat("BayesMallowsSMC2 Model\n")
   cat(strrep("=", nchar("BayesMallowsSMC2 Model")), "\n\n", sep = "")
-  
+
   # Display basic information
   cat(sprintf("%-25s %s\n", "Number of particles:", n_particles))
   cat(sprintf("%-25s %s\n", "Number of timepoints:", n_timepoints))
   cat(sprintf("%-25s %s\n", "Number of items:", n_items))
   cat(sprintf("%-25s %s\n\n", "Number of clusters:", n_clusters))
-  
+
   # Display model fit information
   cat(sprintf("%-25s %.2f\n", "Log marginal likelihood:", x$log_marginal_likelihood))
   cat(sprintf("%-25s %.2f\n", "Final ESS:", x$ESS[n_timepoints]))
   cat(sprintf("%-25s %d/%d\n", "Resampling events:", n_resampling_events, n_timepoints))
-  
+
   invisible(x)
 }
 
@@ -121,27 +121,27 @@ summary.BayesMallowsSMC2 <- function(object, ...) {
   if (!inherits(object, "BayesMallowsSMC2")) {
     stop("object must be an object of class 'BayesMallowsSMC2'")
   }
-  
+
   required_fields <- c("alpha", "rho", "ESS", "resampling", "log_marginal_likelihood")
   missing_fields <- setdiff(required_fields, names(object))
   if (length(missing_fields) > 0) {
     stop("Object is missing required fields: ", paste(missing_fields, collapse = ", "))
   }
-  
+
   # Extract dimensions
   n_particles <- ncol(object$alpha)
   n_timepoints <- length(object$ESS)
   n_items <- dim(object$rho)[1]
   n_clusters <- nrow(object$alpha)
-  
+
   # Count resampling events
   n_resampling_events <- sum(object$resampling)
-  
+
   # Compute posterior mean and standard deviation of alpha
   # alpha is a matrix where rows are clusters and columns are particles
   alpha_mean <- rowMeans(object$alpha)
-  alpha_sd <- apply(object$alpha, 1, sd)
-  
+  alpha_sd <- apply(object$alpha, 1, stats::sd)
+
   # Create summary object
   summary_obj <- list(
     n_particles = n_particles,
@@ -154,7 +154,7 @@ summary.BayesMallowsSMC2 <- function(object, ...) {
     alpha_mean = alpha_mean,
     alpha_sd = alpha_sd
   )
-  
+
   class(summary_obj) <- "summary.BayesMallowsSMC2"
   summary_obj
 }
@@ -174,25 +174,25 @@ print.summary.BayesMallowsSMC2 <- function(x, ...) {
   # Create header
   cat("BayesMallowsSMC2 Model Summary\n")
   cat(strrep("=", nchar("BayesMallowsSMC2 Model Summary")), "\n\n", sep = "")
-  
+
   # Display basic information
   cat(sprintf("%-25s %s\n", "Number of particles:", x$n_particles))
   cat(sprintf("%-25s %s\n", "Number of timepoints:", x$n_timepoints))
   cat(sprintf("%-25s %s\n", "Number of items:", x$n_items))
   cat(sprintf("%-25s %s\n\n", "Number of clusters:", x$n_clusters))
-  
+
   # Display model fit information
   cat(sprintf("%-25s %.2f\n", "Log marginal likelihood:", x$log_marginal_likelihood))
   cat(sprintf("%-25s %.2f\n", "Final ESS:", x$final_ess))
   cat(sprintf("%-25s %d/%d\n\n", "Resampling events:", x$n_resampling_events, x$n_timepoints))
-  
+
   # Display posterior statistics for alpha
   cat("Posterior Statistics for Alpha:\n")
   cat(strrep("-", nchar("Posterior Statistics for Alpha:")), "\n", sep = "")
   for (i in seq_along(x$alpha_mean)) {
-    cat(sprintf("Cluster %d: Mean = %.4f, SD = %.4f\n", 
+    cat(sprintf("Cluster %d: Mean = %.4f, SD = %.4f\n",
                 i, x$alpha_mean[i], x$alpha_sd[i]))
   }
-  
+
   invisible(x)
 }

R/trace_plot.R

@@ -55,31 +55,31 @@
 trace_plot <- function(x, parameter = "alpha", ...) {
   # Validate parameter
   parameter <- match.arg(parameter, c("alpha", "tau"))
-  
+
   # Basic validation
   if (!inherits(x, "BayesMallowsSMC2")) {
     stop("x must be an object of class 'BayesMallowsSMC2'")
   }
-  
+
   # Check if trace was enabled
   trace_field <- paste0(parameter, "_traces")
   if (!trace_field %in% names(x)) {
     stop("Trace data not found. Please run compute_sequentially with trace = TRUE in set_smc_options().")
   }
-  
+
   traces <- x[[trace_field]]
-  
+
   if (length(traces) == 0) {
     stop("Trace data not found. Please run compute_sequentially with trace = TRUE in set_smc_options().")
   }
-  
+
   # Check for importance weights trace
   if (!"log_importance_weights_traces" %in% names(x)) {
     stop("Importance weights trace not found. This should not happen if trace = TRUE was used.")
   }
-  
+
   log_weights_traces <- x$log_importance_weights_traces
-  
+
   if (parameter == "alpha") {
     plot_trace_alpha_tau(traces, log_weights_traces, parameter_name = "alpha",
                          parameter_label = expression(alpha))
@@ -101,16 +101,16 @@ utils::globalVariables(c("timepoint", "mean", "lower", "upper", "cluster"))
 plot_trace_alpha_tau <- function(traces, log_weights_traces, parameter_name,
                                  parameter_label) {
   n_timepoints <- length(traces)
-  
+
   # Get dimensions from first trace
   # Need to infer n_clusters and n_particles from the trace
   first_trace <- traces[[1]]
-  
+
   # If trace is a vector, need to infer dimensions
   if (is.vector(first_trace)) {
     # Get n_particles from log_weights
     n_particles <- length(log_weights_traces[[1]])
-    
+
     # If trace is a vector, infer n_clusters from its length
     trace_length <- length(first_trace)
     if (trace_length %% n_particles == 0) {
@@ -120,10 +120,10 @@ plot_trace_alpha_tau <- function(traces, log_weights_traces, parameter_name,
                    trace_length, n_particles),
            "This indicates inconsistent dimensions in the trace data.")
     }
-    
+
     # Convert all traces to matrices [n_clusters x n_particles]
     # The C++ code stores traces as: alpha is [n_clusters x n_particles] matrix per timepoint
-    # When passed to R as vector, elements are in column-major order: 
+    # When passed to R as vector, elements are in column-major order:
     # cluster1_particle1, cluster2_particle1, cluster1_particle2, cluster2_particle2, ...
     traces <- lapply(traces, function(t) {
       matrix(t, nrow = n_clusters, ncol = n_particles, byrow = FALSE)
@@ -135,27 +135,27 @@ plot_trace_alpha_tau <- function(traces, log_weights_traces, parameter_name,
   } else {
     stop("Trace elements must be vectors or matrices")
   }
-  
+
   # Create data frame for plotting
   plot_data_list <- vector("list", n_timepoints * n_clusters)
   idx <- 1
-  
+
   for (t in seq_len(n_timepoints)) {
     param_matrix <- traces[[t]]
     log_weights <- log_weights_traces[[t]]
-    
+
     # Normalize weights
     weights <- exp(log_weights - max(log_weights))
     weights <- weights / sum(weights)
-    
+
     for (cluster in seq_len(n_clusters)) {
       param_values <- param_matrix[cluster, ]
-      
+
       # Compute weighted statistics
-      weighted_mean <- weighted.mean(param_values, weights)
-      weighted_quantiles <- weighted_quantile(param_values, weights, 
+      weighted_mean <- stats::weighted.mean(param_values, weights)
+      weighted_quantiles <- weighted_quantile(param_values, weights,
                                               probs = c(0.025, 0.975))
-      
+
       plot_data_list[[idx]] <- data.frame(
         timepoint = t,
         mean = weighted_mean,
@@ -166,12 +166,12 @@ plot_trace_alpha_tau <- function(traces, log_weights_traces, parameter_name,
       idx <- idx + 1
     }
   }
-  
+
   plot_data <- do.call(rbind, plot_data_list)
-  
+
   # Create line plot with credible interval
   p <- ggplot2::ggplot(plot_data, ggplot2::aes(x = timepoint, y = mean)) +
-    ggplot2::geom_ribbon(ggplot2::aes(ymin = lower, ymax = upper), 
+    ggplot2::geom_ribbon(ggplot2::aes(ymin = lower, ymax = upper),
                         alpha = 0.3, fill = "steelblue") +
     ggplot2::geom_line(color = "darkblue", linewidth = 1) +
     ggplot2::xlab("Timepoint") +
@@ -180,12 +180,12 @@ plot_trace_alpha_tau <- function(traces, log_weights_traces, parameter_name,
     ggplot2::theme(
       panel.grid.minor = ggplot2::element_blank()
     )
-  
+
   # Add faceting if multiple clusters
   if (n_clusters > 1) {
     p <- p + ggplot2::facet_wrap(~ cluster, scales = "free_y")
   }
-  
+
   p
 }
 
@@ -200,10 +200,10 @@ weighted_quantile <- function(x, weights, probs) {
   ord <- order(x)
   x_sorted <- x[ord]
   weights_sorted <- weights[ord]
-  
+
   # Compute cumulative weights
   cum_weights <- cumsum(weights_sorted) / sum(weights_sorted)
-  
+
   # Find quantiles
   quantiles <- numeric(length(probs))
   for (i in seq_along(probs)) {
@@ -214,6 +214,6 @@ weighted_quantile <- function(x, weights, probs) {
     }
     quantiles[i] <- x_sorted[idx]
   }
-  
+
   quantiles
 }