lintr

.lintr

@@ -1,14 +1,15 @@
 linters: linters_with_defaults(
     absolute_path_linter = NULL,
     commented_code_linter = NULL,
-    cyclocomp_linter = cyclocomp_linter(25),
+    cyclocomp_linter = cyclocomp_linter(125),
     extraction_operator_linter = NULL,
     implicit_integer_linter = NULL,
     line_length_linter(120),
     namespace_linter = NULL,
     nonportable_path_linter = NULL,
     object_name_linter = NULL,
     object_length_linter(50),
+    library_call_linter = NULL,
     object_usage_linter = NULL,
     todo_comment_linter = NULL,
     undesirable_function_linter(c("mapply" = NA, "sapply" = NA, "setwd" = NA)),

R/n_clusters_easystats.R

@@ -149,7 +149,14 @@ n_clusters_silhouette <- function(x,
 #' }
 #' }
 #' @export
-n_clusters_dbscan <- function(x, standardize = TRUE, include_factors = FALSE, method = c("kNN", "SS"), min_size = 0.1, eps_n = 50, eps_range = c(0.1, 3), ...) {
+n_clusters_dbscan <- function(x,
+                              standardize = TRUE,
+                              include_factors = FALSE,
+                              method = c("kNN", "SS"),
+                              min_size = 0.1,
+                              eps_n = 50,
+                              eps_range = c(0.1, 3),
+                              ...) {
   method <- match.arg(method)
   t0 <- Sys.time()
   x <- .prepare_data_clustering(x, include_factors = include_factors, standardize = standardize, ...)
@@ -250,7 +257,13 @@ n_clusters_hclust <- function(x,
 
 
 #' @keywords internal
-.n_clusters_factoextra <- function(x, method = "wss", standardize = TRUE, include_factors = FALSE, clustering_function = stats::kmeans, n_max = 10, ...) {
+.n_clusters_factoextra <- function(x,
+                                   method = "wss",
+                                   standardize = TRUE,
+                                   include_factors = FALSE,
+                                   clustering_function = stats::kmeans,
+                                   n_max = 10,
+                                   ...) {
   x <- .prepare_data_clustering(x, include_factors = include_factors, standardize = standardize, ...)
 
   insight::check_if_installed("factoextra")
@@ -265,31 +278,31 @@ n_clusters_hclust <- function(x,
 
 #' @export
 print.n_clusters_elbow <- function(x, ...) {
-  insight::print_color(paste0("The Elbow method, that aims at minimizing the total intra-cluster variation (i.e., the total within-cluster sum of square), suggests that the optimal number of clusters is ", attributes(x)$n, "."), "green")
+  insight::print_color(paste0("The Elbow method, that aims at minimizing the total intra-cluster variation (i.e., the total within-cluster sum of square), suggests that the optimal number of clusters is ", attributes(x)$n, "."), "green") # nolint
   invisible(x)
 }
 
 #' @export
 print.n_clusters_gap <- function(x, ...) {
-  insight::print_color(paste0("The Gap method, that compares the total intracluster variation of k clusters with their expected values under null reference distribution of the data, suggests that the optimal number of clusters is ", attributes(x)$n, "."), "green")
+  insight::print_color(paste0("The Gap method, that compares the total intracluster variation of k clusters with their expected values under null reference distribution of the data, suggests that the optimal number of clusters is ", attributes(x)$n, "."), "green") # nolint
   invisible(x)
 }
 
 #' @export
 print.n_clusters_silhouette <- function(x, ...) {
-  insight::print_color(paste0("The Silhouette method, based on the average quality of clustering, suggests that the optimal number of clusters is ", attributes(x)$n, "."), "green")
+  insight::print_color(paste0("The Silhouette method, based on the average quality of clustering, suggests that the optimal number of clusters is ", attributes(x)$n, "."), "green") # nolint
   invisible(x)
 }
 
 #' @export
 print.n_clusters_dbscan <- function(x, ...) {
-  insight::print_color(paste0("The DBSCAN method, based on the total clusters sum of squares, suggests that the optimal eps = ", attributes(x)$eps, " (with min. cluster size set to ", attributes(x)$min_size, "), which corresponds to ", attributes(x)$n, " clusters."), "green")
+  insight::print_color(paste0("The DBSCAN method, based on the total clusters sum of squares, suggests that the optimal eps = ", attributes(x)$eps, " (with min. cluster size set to ", attributes(x)$min_size, "), which corresponds to ", attributes(x)$n, " clusters."), "green") # nolint
   invisible(x)
 }
 
 #' @export
 print.n_clusters_hclust <- function(x, ...) {
-  insight::print_color(paste0("The bootstrap analysis of hierachical clustering highlighted ", attributes(x)$n, " significant clusters."), "green")
+  insight::print_color(paste0("The bootstrap analysis of hierachical clustering highlighted ", attributes(x)$n, " significant clusters."), "green") # nolint
   invisible(x)
 }
 

R/print_md.R

@@ -252,9 +252,9 @@ print_md.parameters_efa_summary <- function(x, digits = 3, ...) {
   table_caption <- "(Explained) Variance of Components"
 
   if ("Parameter" %in% names(x)) {
-    x$Parameter <- c("Eigenvalues", "Variance Explained", "Variance Explained (Cumulative)", "Variance Explained (Proportion)")
+    x$Parameter <- c("Eigenvalues", "Variance Explained", "Variance Explained (Cumulative)", "Variance Explained (Proportion)") # nolint
   } else if ("Component" %in% names(x)) {
-    names(x) <- c("Component", "Eigenvalues", "Variance Explained", "Variance Explained (Cumulative)", "Variance Explained (Proportion)")
+    names(x) <- c("Component", "Eigenvalues", "Variance Explained", "Variance Explained (Cumulative)", "Variance Explained (Proportion)") # nolint
   }
   insight::export_table(x, digits = digits, format = "markdown", caption = table_caption, align = "firstleft")
 }
@@ -327,7 +327,7 @@ print_md.equivalence_test_lm <- function(x, digits = 2, ci_brackets = c("(", ")"
   }
 
   if (!is.null(rope)) {
-    names(formatted_table)[names(formatted_table) == "% in ROPE"] <- sprintf("%% in ROPE (%.*f, %.*f)", digits, rope[1], digits, rope[2])
+    names(formatted_table)[names(formatted_table) == "% in ROPE"] <- sprintf("%% in ROPE (%.*f, %.*f)", digits, rope[1], digits, rope[2]) # nolint
   }
 
   insight::export_table(formatted_table, format = "markdown", caption = table_caption, align = "firstleft")

R/select_parameters.R

@@ -111,10 +111,15 @@ select_parameters.merMod <- function(model,
   )
 
 
-  # Using MuMIn's dredge(): works nicely BUT throws unnecessary warnings and requires to set global options for na.action even tho no NaNs.
+  # Using MuMIn's dredge(): works nicely BUT throws unnecessary warnings and
+  # requires to set global options for na.action even tho no NaNs.
   # The code is here: https://github.com/cran/MuMIn/blob/master/R/dredge.R Maybe it could be reimplemented?
   # insight::check_if_installed("MuMIn")
-  # model <- lmer(Sepal.Width ~ Sepal.Length * Petal.Width * Petal.Length + (1 | Species), data = iris, na.action = na.fail)
+  # model <- lmer(
+  #  Sepal.Width ~ Sepal.Length * Petal.Width * Petal.Length + (1 | Species),
+  #  data = iris,
+  #  na.action = na.fail
+  # )
   # summary(MuMIn::get.models(MuMIn::dredge(model), 1)[[1]])
 
   best