additional fixes to docs, fix roxygen meta

Author: santikka

NEWS.md

@@ -1,6 +1,6 @@
 # tna 0.4.1
 
-* The `detailed` argument of `estimate_centrality_stability()` has been implemented. Previously this argument had no effect on the output of the function.
+* The `detailed` argument of `estimate_centrality_stability()` has been removed. Previously this argument had no effect on the output of the function.
 * Removed several duplicated entries in the documentation.
 
 # tna 0.4.0

R/centralities.R

@@ -204,9 +204,6 @@ estimate_centrality_stability <- estimate_cs
 #' calculating the CS-coefficient. The default is 0.7.
 #' @param certainty A `numeric` value specifying the desired level of certainty
 #' for the CS-coefficient. Default is 0.95.
-#' @param detailed A `logical` value specifying whether to return detailed
-#' sampling results. If `TRUE`, detailed results are included in the output.
-#' The default is `FALSE`.
 #' @param progressbar A `logical` value. If `TRUE`, a progress bar is displayed
 #' Defaults to `FALSE`
 #' @param ... Ignored.
@@ -218,8 +215,6 @@ estimate_centrality_stability <- estimate_cs
 #'   of the measure.
 #' * `correlations`: A `matrix` of correlations between the original
 #'   centrality and the resampled centralities for each drop proportion.
-#' * `detailed_results`: A detailed data frame of the sampled correlations,
-#'   returned only if `return_detailed = TRUE`
 #'
 #' If `x` is a `group_tna` object, a `group_tna_stability` object is returned
 #' instead, which is a `list` of `tna_stability` objects.
@@ -239,14 +234,12 @@ estimate_cs.tna <- function(x, loops = FALSE, normalize = FALSE,
                               "InStrength", "OutStrength", "Betweenness"
                             ), iter = 1000, method = "pearson",
                             drop_prop = seq(0.1, 0.9, by = 0.1),
-                            threshold = 0.7,
-                            certainty = 0.95, detailed = FALSE,
+                            threshold = 0.7, certainty = 0.95,
                             progressbar = FALSE, ...) {
   check_tna_seq(x)
   check_flag(loops)
   check_flag(normalize)
   check_flag(progressbar)
-  check_flag(detailed)
   check_values(iter, strict = TRUE)
   check_range(threshold)
   check_range(certainty)
@@ -429,7 +422,6 @@ wcc <- function(mat) {
 # Clusters ----------------------------------------------------------------
 
 #' @export
-#' @family clusters
 #' @rdname centralities
 centralities.group_tna <- function(x, loops = FALSE,
                                    normalize = FALSE, measures, ...) {
@@ -465,15 +457,14 @@ centralities.group_tna <- function(x, loops = FALSE,
 }
 
 #' @export
-#' @family clusters
 #' @rdname estimate_centrality_stability
 estimate_cs.group_tna <- function(x, loops = FALSE, normalize = FALSE,
                                   measures = c(
                                     "InStrength", "OutStrength", "Betweenness"
                                   ), iter = 1000, method = "pearson",
                                   drop_prop = seq(0.1, 0.9, by = 0.1),
                                   threshold = 0.7, certainty = 0.95,
-                                  detailed = FALSE, progressbar = FALSE, ...) {
+                                  progressbar = FALSE, ...) {
   check_missing(x)
   check_class(x, "group_tna")
   structure(
@@ -490,7 +481,6 @@ estimate_cs.group_tna <- function(x, loops = FALSE, normalize = FALSE,
           drop_prop = drop_prop,
           threshold = threshold,
           certainty = certainty,
-          detailed = detailed,
           progressbar = progressbar,
           ...
         )
@@ -501,7 +491,6 @@ estimate_cs.group_tna <- function(x, loops = FALSE, normalize = FALSE,
 }
 
 #' @export
-#' @family clusters
 #' @rdname estimate_centrality_stability
 estimate_centrality_stability.group_tna <- estimate_cs.group_tna
 

R/clusters.R

@@ -1,4 +1,4 @@
-#' Build a grouped Transition Network Analysis Model
+#' Build a Grouped Transition Network Analysis Model
 #'
 #' This function constructs a transition network analysis (TNA) model for
 #' each cluster from a given sequence, wide-format dataframe,
@@ -181,7 +181,7 @@ group_ctna <- function(x, ...) {
   group_model(x = x, type = "co-occurrence", ...)
 }
 
-#' Retrieve statistics from a mixture Markov model (MMM)
+#' Retrieve Statistics from a Mixture Markov Model (MMM)
 #'
 #' @export
 #' @family clusters

R/permutation.R

@@ -1,4 +1,4 @@
-#' Compare Two Networks from Sequence Data Using Permutation Tests
+#' Compare Two Networks from Sequence Data using a Permutation Test
 #'
 #' This function compares two networks built from sequence data using
 #' permutation tests. The function builds Markov models for two sequence

R/plot.R

@@ -305,7 +305,7 @@ plot.tna_communities <- function(x, colors, method = "spinglass", ...) {
   plot(y, colors = map_to_color(x$assignment[[method]], colors), ...)
 }
 
-#' Plot the results of comparing two `tna` models or matrices
+#' Plot the Comparison of Two TNA Models or Matrices
 #'
 #' @export
 #' @family comparison
@@ -770,7 +770,7 @@ plot_centralities_multiple <- function(x, reorder, ncol,
     )
 }
 
-#' Plot the difference network between two models
+#' Plot the Difference Network Between Two Models
 #'
 #' Plots the difference network between model `x` and model `y`. The edges are
 #' computed from subtracting the two models. The pie chart is the difference in
@@ -1047,7 +1047,7 @@ hist.group_tna <- function(x, ...) {
   invisible(lapply(x, hist.tna, ...))
 }
 
-#' Plot a grouped Transition Network Analysis Model
+#' Plot a Grouped Transition Network Analysis Model
 #'
 #' Plots a transition network of each cluster using `qgraph`.
 #'
@@ -1125,7 +1125,7 @@ plot.group_tna_cliques <- function(x, title, ...) {
   )
 }
 
-#' Plot Found Communities
+#' Plot Detected Communities
 #'
 #' @export
 #' @family communities
@@ -1216,7 +1216,7 @@ plot_compare.group_tna <- function(x, i = 1L, j = 2L, ...) {
   plot_compare(x = x[[i]], y = x[[j]], ...)
 }
 
-#' Plot state frequencies as a mosaic between two groups
+#' Plot State Frequencies as a Mosaic Between Two Groups
 #'
 #' @export
 #' @family basic

R/print.R

@@ -32,7 +32,7 @@ print.summary.tna_bootstrap <- function(x, ...) {
   NextMethod(generic = "print", object = x, ...)
 }
 
-#' Print a `tna` object
+#' Print a `tna` Object
 #'
 #' @export
 #' @family basic
@@ -238,7 +238,7 @@ print.tna_cliques <- function(x, n = 6, first = 1,
   invisible(x)
 }
 
-#' Print a TNA data object
+#' Print a TNA Data Object
 #'
 #' @export
 #' @family basic
@@ -376,7 +376,7 @@ print.group_tna_bootstrap <- function(x, ...) {
   invisible(x)
 }
 
-#' Print the summary of a grouped Transition Network Analysis Model
+#' Print a Summary of a Grouped Transition Network Analysis Model
 #'
 #' @export
 #' @family basic
@@ -401,7 +401,7 @@ print.summary.group_tna <- function(x, ...) {
   invisible(x)
 }
 
-#' Print Bootstrap Summary for a Grouped Transition Network
+#' Print a Bootstrap Summary for a Grouped Transition Network Model
 #'
 #' @export
 #' @family validation

R/pruning.R

@@ -1,4 +1,4 @@
-#' Prune a `tna` network based on transition probabilities
+#' Prune a Transition Network based on Transition Probabilities
 #'
 #' Prunes a network represented by a `tna` object by removing
 #' edges based on a specified threshold, lowest percent of non-zero edge
@@ -14,7 +14,7 @@ prune <- function(x, ...) {
 }
 
 
-#' Prune a `tna` network based on transition probabilities
+#' Prune a Transition Network based on Transition Probabilities
 #'
 #' Prunes a network represented by a `tna` object by removing
 #' edges based on a specified threshold, lowest percent of non-zero edge
@@ -309,12 +309,11 @@ disparity_filter <- function(mat, level) {
   sig
 }
 
-# Clusters ----------------------------------------------------------------
 
+# Clusters ----------------------------------------------------------------
 
 
 #' @export
-#' @family clusters
 #' @rdname prune
 prune.group_tna <- function(x, ...) {
   check_missing(x)
@@ -329,7 +328,6 @@ prune.group_tna <- function(x, ...) {
 }
 
 #' @export
-#' @family clusters
 #' @rdname pruning_details
 pruning_details.group_tna <- function(x, ...) {
   check_missing(x)
@@ -345,7 +343,6 @@ pruning_details.group_tna <- function(x, ...) {
 }
 
 #' @export
-#' @family clusters
 #' @rdname deprune
 deprune.group_tna <- function(x, ...) {
   check_missing(x)
@@ -360,7 +357,6 @@ deprune.group_tna <- function(x, ...) {
 }
 
 #' @export
-#' @family clusters
 #' @rdname reprune
 reprune.group_tna <- function(x, ...) {
   check_missing(x)

R/tna-package.R

@@ -1,4 +1,4 @@
-#' The `tna` package.
+#' The `tna` Package.
 #'
 #' @name tna-package
 #' @family basic
@@ -32,7 +32,7 @@
 #'
 "_PACKAGE"
 
-#' Example data on student engagement
+#' Example Data on Student Engagement
 #'
 #' Students' engagement states (Active / Average / Disengaged)
 #' throughout a whole study program. The data was generated synthetically
@@ -45,15 +45,15 @@
 #' @format A `stslist` object (sequence data).
 "engagement"
 
-#' Example mixed Markov model fitted to the `engagement` data
+#' Example Mixed Markov Model Fitted to the `engagement` Data
 #'
 #' @family datasets
 #' @source The data was generated via `mixed_markov_model.R` in
 #' <https://github.com/sonsoleslp/tna/tree/main/data-raw/>
 #' @format A `mhmm` object.
 "engagement_mmm"
 
-#' Example data on group regulation
+#' Example Data on Group Regulation
 #'
 #' Students' regulation during collaborative learning. Students' interactions
 #' were coded as:  "adapt", "cohesion", "consensus", "coregulate", "discuss",

R/utilities.R

@@ -70,7 +70,7 @@ is_tna_centralities <- function(x) {
 #' @export
 igraph::as.igraph
 
-#' Coerce a `tna` object into an `igraph` object.
+#' Coerce a `tna` Object into an `igraph` Object.
 #'
 #' @export
 #' @family helpers
@@ -86,7 +86,7 @@ as.igraph.tna <- function(x, ...) {
   )
 }
 
-#' Coerce a weight matrix into an `igraph` object.
+#' Coerce a Weight Matrix into an `igraph` Object.
 #'
 #' @export
 #' @family helpers
@@ -105,7 +105,7 @@ as.igraph.matrix <- function(x, directed = TRUE, ...) {
   )
 }
 
-#' Coerce  a specific group from a `group_tna` object into an `igraph` object.
+#' Coerce a Specific Group from a `group_tna` Object into an `igraph` Object.
 #'
 #' @export
 #' @family helpers