diff --git a/DESCRIPTION b/DESCRIPTION
index 5708356..9c02f07 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: oolong
 Title: Create Validation Tests for Automated Content Analysis
-Version: 0.6.1
+Version: 0.6.2
 Authors@R: 
     c(person(given = "Chung-hong", family = "Chan", role = c("aut", "cre"), email = "chainsawtiney@gmail.com", comment = c(ORCID = "0000-0002-6232-7530")),
     person(given = "Marius", family = "Sältzer", role = c("aut"), email = "msaeltze@mail.uni-mannheim.de", comment = c(ORCID = "0000-0002-8604-4666")))
@@ -19,7 +19,7 @@ Imports:
     digest,
     R6,
     quanteda (>= 3.0.0),
-    irr,
+    icr,
     ggplot2,
     cowplot,
     cli,
diff --git a/NEWS.md b/NEWS.md
index b213ab0..7312301 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,6 +1,7 @@
 # oolong 0.6.2 (development)
 
 * Add content for MH 
+* Use `icr` for the calculation of Krippendorff's Alpha
 
 # oolong 0.6.1
 
diff --git a/R/oolong_summary.R b/R/oolong_summary.R
index ce660cb..fa84eed 100644
--- a/R/oolong_summary.R
+++ b/R/oolong_summary.R
@@ -3,7 +3,7 @@
 #' These functions print or plot a useful summary of the results from \code{\link{summarize_oolong}}. For details, please see the overview vignette: \code{vignette("overview", package = "oolong")}
 #'
 #' @section Summary:
-#' 
+#'
 #' Print function displays the following information:
 #' \describe{
 #'   \item{Mean model precision}{(wi, wsi) Higher value indicates better topic interpretability}
@@ -18,9 +18,9 @@
 #'   \item{Correlation (average answer)}{(gs) Pearson's correlation between average answer and target value}
 #'   \item{Corrlation (content length)}{(gs) Pearson's correlation between content length and target value}
 #' }
-#' 
+#'
 #' @section Diagnostic plot:
-#' 
+#'
 #' Plot function displays a diagnostic plot with the following subplots (gs only).
 #' \describe{
 #'   \item{Top left}{Correlation between answer from coders and target value to check for correlation between two values. Both axes are minmax transformed.}
@@ -28,7 +28,7 @@
 #'   \item{Bottom left}{Correlation between target value and content length to check for the influence of content length.}
 #'   \item{Bottom right}{Cook's distance to check for influential observations.}
 #' }
-#' 
+#'
 #' @param x an oolong_summary
 #' @param ... other parameters
 #' @method print oolong_summary
@@ -53,7 +53,7 @@ plot.oolong_summary <- function(x, ...) {
     } else {
         .cstop(TRUE, "Don't know how to plot this oolong_summary.")
     }
-    
+
 }
 
 .print_oolong_summary_tm <- function(oolong_summary) {
@@ -85,7 +85,7 @@ plot.oolong_summary <- function(x, ...) {
 
 .print_oolong_summary_gs <- function(oolong_summary) {
     cli::cli_h1("Summary (gold standard generation):")
-    .cp(oolong_summary$n_models > 1, "Krippendorff's Alpha: ", round(oolong_summary$kripp_alpha$value, 3))
+    .cp(oolong_summary$n_models > 1, "Krippendorff's Alpha: ", round(oolong_summary$kripp_alpha$alpha, 3))
     .cp(!is.null(oolong_summary$cor), "Correlation: ", round(oolong_summary$cor$estimate, 3), " (p = ", round(oolong_summary$cor$p.value, 4), ")")
     .cp(!is.null(oolong_summary$cor_length), "Effect of content length: ", round(oolong_summary$cor_length$estimate, 3), " (p = ", round(oolong_summary$cor_length$p.value, 4), ")")
 }
@@ -93,7 +93,7 @@ plot.oolong_summary <- function(x, ...) {
 #' Summarize oolong objects
 #'
 #' This function summarizes one or more oolong objects. All oolong objects must be locked.
-#' 
+#'
 #' @param ... (tm/gs) one or more oolong objects to be summarized
 #' @param target_value (gs) a vector of numeric values, the value you want to validate against the human-coded gold standard. One example of this target value is sentiment score extracted automatically from text
 #' @param n_iter (ti) number of iterations to calculate the median test
@@ -127,7 +127,7 @@ plot.oolong_summary <- function(x, ...) {
 #' @author Chung-hong Chan
 #' @references
 #'   Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. In Advances in neural information processing systems (pp. 288-296).
-#' 
+#'
 #'   Song et al. (2020) In validations we trust? The impact of imperfect human annotations as a gold standard on the quality of validation of automated content analysis. Political Communication.
 #'
 #'   Ying, L., Montgomery, J. M., & Stewart, B. M. (2021). Topics, Concepts, and Measurement: A Crowdsourced Procedure for Validating Topics as Measures. Political Analysis.
diff --git a/R/oolong_summary_gs.R b/R/oolong_summary_gs.R
index 714060a..8501bc5 100644
--- a/R/oolong_summary_gs.R
+++ b/R/oolong_summary_gs.R
@@ -1,5 +1,5 @@
 .minmax <- function(x) {
-    (x - min(x)) / (max(x) - min(x)) 
+    (x - min(x)) / (max(x) - min(x))
 }
 
 .corr_plot <- function(answers, target_value) {
@@ -37,7 +37,7 @@
     avg_answer <- apply(answers, 1, mean)
     answers$avg_answer <- avg_answer
     if (length(obj_list) > 1) {
-        kripp <- irr::kripp.alpha(t(as.matrix(answers[,grepl("^answer", colnames(answers))])), method = "ordinal")
+        kripp <- icr::krippalpha(t(as.matrix(answers[,grepl("^answer", colnames(answers))])), metric = "ordinal")
     } else {
         kripp <- NA
     }
diff --git a/R/oolong_summary_tm.R b/R/oolong_summary_tm.R
index 8f82dcc..c49d3f6 100644
--- a/R/oolong_summary_tm.R
+++ b/R/oolong_summary_tm.R
@@ -52,7 +52,7 @@
             res$multiple_test <- NA
             res$rater_precision_p_value <- NA
         } else {
-            res$kripp_alpha <- irr::kripp.alpha(t(ifelse(correction_matrix, 2, 1)))$value
+            res$kripp_alpha <- icr::krippalpha(t(ifelse(correction_matrix, 2, 1)))$alpha
             res$multiple_test <- purrr::map(n_correct, ~binom.test(., n = nrow(correction_matrix), p = 1/n_choices, alternative = "greater"))
             res$rater_precision_p_value <- .combine_p_fisher(purrr::map_dbl(res$multiple_test, "p.value"))
         }
@@ -90,7 +90,7 @@
         if (length(obj_list) == 1) {
             res$kripp_alpha_wsi <- NA
         } else {
-            res$kripp_alpha_wsi <- irr::kripp.alpha(t(ifelse(correction_matrix, 2, 1)))$value
+            res$kripp_alpha_wsi <- icr::krippalpha(t(ifelse(correction_matrix, 2, 1)))$alpha
         }
     }
     return(res)
diff --git a/tests/testthat/_snaps/printing.md b/tests/testthat/_snaps/printing.md
index c9d7967..8177bde 100644
--- a/tests/testthat/_snaps/printing.md
+++ b/tests/testthat/_snaps/printing.md
@@ -114,7 +114,7 @@
       i Mean model precision: 0.833333333333333
       i K Precision:
       0.3, 0.7, 0.7, 0.7, 1, 1, 1, 1, 1, 1
-      i Krippendorff's alpha: 0.056
+      i Krippendorff's alpha: 0.072
 
 ---