diff --git a/.github/workflows/update-to-latest-easystats.yaml b/.github/workflows/update-to-latest-easystats.yaml
new file mode 100644
index 000000000..7718d763a
--- /dev/null
+++ b/.github/workflows/update-to-latest-easystats.yaml
@@ -0,0 +1,10 @@
+on:
+  schedule:
+    # Check for dependency updates once a month
+    - cron: "0 0 1 * *"
+
+name: update-to-latest-easystats
+
+jobs:
+  update-to-latest-easystats:
+    uses: easystats/workflows/.github/workflows/update-to-latest-easystats.yaml@main
diff --git a/DESCRIPTION b/DESCRIPTION
index fa29f37d4..3b95351d9 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.10.5.2
+Version: 0.10.5.4
 Authors@R: 
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -69,9 +69,9 @@ BugReports: https://github.com/easystats/performance/issues
 Depends:
     R (>= 3.6)
 Imports:
-    bayestestR (>= 0.13.0),
-    insight (>= 0.19.4),
-    datawizard (>= 0.7.0),
+    bayestestR (>= 0.13.1),
+    insight (>= 0.19.5),
+    datawizard (>= 0.9.0),
     methods,
     stats,
     utils
diff --git a/NEWS.md b/NEWS.md
index 857924e29..40e57d346 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,12 @@
 # performance (development version)
 
+## Changes to functions
+
+* `check_outliers()` for method `"ics"` now detects number of available cores
+  for parallel computing via the `"mc.cores"` option. This is more robust than
+  the previous method, which used `parallel::detectCores()`. Now you should
+  set the number of cores via `options(mc.cores = 4)`.
+
 # performance 0.10.5
 
 ## Changes to functions
diff --git a/R/check_outliers.R b/R/check_outliers.R
index c1af3bb1f..e98e037c0 100644
--- a/R/check_outliers.R
+++ b/R/check_outliers.R
@@ -167,7 +167,9 @@
 #'  value for outliers classification. Refer to the help-file of
 #'  [ICSOutlier::ics.outlier()] to get more details about this procedure.
 #'  Note that `method = "ics"` requires both **ICS** and **ICSOutlier**
-#'  to be installed, and that it takes some time to compute the results.
+#'  to be installed, and that it takes some time to compute the results. You
+#'  can speed up computation time using parallel computing. Set the number of
+#'  cores to use with `options(mc.cores = 4)` (for example).
 #'
 #'  - **OPTICS**:
 #'  The Ordering Points To Identify the Clustering Structure (OPTICS) algorithm
@@ -299,6 +301,7 @@
 #' group_iris <- datawizard::data_group(iris, "Species")
 #' check_outliers(group_iris)
 #'
+#' @examplesIf require("see") && require("bigutilsr") && require("loo") && require("MASS") && require("ICSOutlier") && require("ICS") && require("dbscan")
 #' \donttest{
 #' # You can also run all the methods
 #' check_outliers(data, method = "all")
@@ -315,10 +318,7 @@
 #' model <- lm(disp ~ mpg + hp, data = mt2)
 #'
 #' outliers_list <- check_outliers(model)
-#'
-#' if (require("see")) {
-#'   plot(outliers_list)
-#' }
+#' plot(outliers_list)
 #'
 #' insight::get_data(model)[outliers_list, ] # Show outliers data
 #' }
@@ -506,7 +506,7 @@ check_outliers.default <- function(x,
 
   num.df <- outlier_count$all[!names(outlier_count$all) %in% c("Row", ID)]
   if (isTRUE(nrow(num.df) > 0)) {
-    num.df <- datawizard::change_code(
+    num.df <- datawizard::recode_values(
       num.df,
       recode = list(`2` = "(Multivariate)")
     )
@@ -1764,7 +1764,20 @@ check_outliers.metabin <- check_outliers.metagen
   n_cores <- if (!requireNamespace("parallel", quietly = TRUE)) {
     NULL
   } else {
-    max(1L, parallel::detectCores() - 2L, na.rm = TRUE)
+    getOption("mc.cores", 1L)
+  }
+
+  # tell user about n-cores option
+  if (is.null(n_cores)) {
+    insight::format_alert(
+      "Package `parallel` is not installed. `check_outliers()` will run on a single core.",
+      "Install package `parallel` and set, for example, `options(mc.cores = 4)` to run on multiple cores."
+    )
+  } else if (n_cores == 1) {
+    insight::format_alert(
+      "Package `parallel` is installed, but `check_outliers()` will run on a single core.",
+      "To use multiple cores, set `options(mc.cores = 4)` (for example)."
+    )
   }
 
   # Run algorithm
diff --git a/R/logLik.R b/R/logLik.R
index 90c12930b..5c42f41a5 100644
--- a/R/logLik.R
+++ b/R/logLik.R
@@ -55,7 +55,7 @@ logLik.plm <- function(object, ...) {
 
   attr(val, "nall") <- N0
   attr(val, "nobs") <- N
-  attr(val, "df") <- insight::get_df(object, type = "model")
+  attr(val, "df") <- insight::n_parameters(object) + 1L
   class(val) <- "logLik"
 
   val
diff --git a/man/check_outliers.Rd b/man/check_outliers.Rd
index e3c218b7a..c19d4ecb0 100644
--- a/man/check_outliers.Rd
+++ b/man/check_outliers.Rd
@@ -195,7 +195,9 @@ of 0.025 (corresponding to the 2.5\\% most extreme observations) as a cut-off
 value for outliers classification. Refer to the help-file of
 \code{\link[ICSOutlier:ics.outlier]{ICSOutlier::ics.outlier()}} to get more details about this procedure.
 Note that \code{method = "ics"} requires both \strong{ICS} and \strong{ICSOutlier}
-to be installed, and that it takes some time to compute the results.
+to be installed, and that it takes some time to compute the results. You
+can speed up computation time using parallel computing. Set the number of
+cores to use with \code{options(mc.cores = 4)} (for example).
 \item \strong{OPTICS}:
 The Ordering Points To Identify the Clustering Structure (OPTICS) algorithm
 (Ankerst et al., 1999) is using similar concepts to DBSCAN (an unsupervised
@@ -290,6 +292,7 @@ filtered_data <- data[outliers_info$Outlier < 0.1, ]
 group_iris <- datawizard::data_group(iris, "Species")
 check_outliers(group_iris)
 
+\dontshow{if (require("see") && require("bigutilsr") && require("loo") && require("MASS") && require("ICSOutlier") && require("ICS") && require("dbscan")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 \donttest{
 # You can also run all the methods
 check_outliers(data, method = "all")
@@ -306,13 +309,11 @@ mt2 <- rbind(mt1, data.frame(
 model <- lm(disp ~ mpg + hp, data = mt2)
 
 outliers_list <- check_outliers(model)
-
-if (require("see")) {
-  plot(outliers_list)
-}
+plot(outliers_list)
 
 insight::get_data(model)[outliers_list, ] # Show outliers data
 }
+\dontshow{\}) # examplesIf}
 }
 \references{
 \itemize{
diff --git a/tests/testthat/test-logLik.R b/tests/testthat/test-logLik.R
new file mode 100644
index 000000000..2691f1d3c
--- /dev/null
+++ b/tests/testthat/test-logLik.R
@@ -0,0 +1,27 @@
+test_that("logLik", {
+  skip_if_not_installed("plm")
+  skip_if_not_installed("withr")
+
+  withr::local_options(list(expressions = 25))
+  set.seed(1)
+  nnn <- 100
+  ddta <- data.frame(
+    x1 = rnorm(nnn),
+    x2 = rnorm(nnn),
+    id = rep_len(1:(nnn / 10), nnn),
+    year = rep_len(1:11, nnn)
+  )
+  ddta$y <- ddta$x1 * 0.5 - ddta$x2 * 0.5 + rnorm(nnn)
+
+  m1 <- lm(y ~ x1 + x2, data = ddta)
+  l1 <- logLik(m1)
+
+  m2 <- plm(
+    y ~ x1 + x2,
+    data = ddta,
+    model = "pooling",
+    index = c("id", "year")
+  )
+  l2 <- logLik(m2)
+  expect_equal(l1, l2, tolerance = 1e-3, ignore_attr = TRUE)
+})