diff --git a/tests/testthat/test_compression.R b/tests/testthat/test_compression.R
index cf0b68d..e4041ab 100644
--- a/tests/testthat/test_compression.R
+++ b/tests/testthat/test_compression.R
@@ -1,3 +1,7 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_compression")
 
@@ -10,13 +14,11 @@ all_neighbors <- expand.grid(a = all_neighbors, b = all_neighbors)
 res_all <- compress(subset, "race", "school", weight = "n", neighbors = all_neighbors)
 
 test_that("result is the same with no neighbors given", {
-    testthat::skip_on_cran()
     res2 <- compress(subset, "race", "school", neighbors = "all", weight = "n")
     expect_equal(res_all$iterations, res2$iterations)
 })
 
 test_that("compress works", {
-    testthat::skip_on_cran()
     # 9 merges
     expect_equal(nrow(res_all$iterations), 16)
     # M values is declining continously
@@ -26,13 +28,11 @@ test_that("compress works", {
 })
 
 test_that("print", {
-    testthat::skip_on_cran()
     expect_output(print(res_all), "17 units")
     expect_output(print(res_all), "Threshold 99%")
 })
 
 test_that("get_crosswalk works", {
-    testthat::skip_on_cran()
     expect_error(
         get_crosswalk(schools00),
         "either n_units or percent has to be given"
@@ -60,7 +60,6 @@ test_that("get_crosswalk works", {
 })
 
 test_that("parts", {
-    testthat::skip_on_cran()
     # get_crosswalk
     res_no_parts <- get_crosswalk(res_all, percent = 0.6)
     res_parts <- get_crosswalk(res_all, percent = 0.6, parts = TRUE)
@@ -83,13 +82,11 @@ test_that("parts", {
 })
 
 test_that("compress edge case", {
-    testthat::skip_on_cran()
     res_edge <- compress(subset, "race", "school", neighbors = "all", weight = "n", max_iter = 1)
     expect_equal(nrow(get_crosswalk(res_edge, n_units = 16)), n_schools)
 })
 
 test_that("merge_units", {
-    testthat::skip_on_cran()
     merged <- merge_units(res_all, percent = 0.8)
     new_units_cw <- sort(unique(get_crosswalk(res_all, percent = 0.8)$new))
     new_units_merged <- sort(unique(merged$school))
@@ -97,7 +94,6 @@ test_that("merge_units", {
 })
 
 test_that("percent works", {
-    testthat::skip_on_cran()
     M_full <- mutual_total(subset, "race", "school", weight = "n")[stat == "M"][["est"]]
 
     for (pct in seq(0.1, 0.9, by = 0.05)) {
@@ -110,7 +106,6 @@ test_that("percent works", {
 })
 
 test_that("merge_units edge case", {
-    testthat::skip_on_cran()
     res_edge <- compress(subset, "race", "school", neighbors = "all", weight = "n", max_iter = 1)
     merged <- merge_units(res_edge, n_units = 16)
     # replicate manual merge
@@ -121,7 +116,6 @@ test_that("merge_units edge case", {
 })
 
 test_that("scree plot", {
-    testthat::skip_on_cran()
     if (requireNamespace("ggplot2", quietly = TRUE)) {
         plot <- scree_plot(res_all)
         expect_equal(nrow(plot$data), n_schools)
diff --git a/tests/testthat/test_dissimilarity.R b/tests/testthat/test_dissimilarity.R
index 095e1fd..87e68a0 100644
--- a/tests/testthat/test_dissimilarity.R
+++ b/tests/testthat/test_dissimilarity.R
@@ -1,9 +1,11 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_dissimilarity")
 
 test_that("correct calculations", {
-    testthat::skip_on_cran()
-
     m0 <- matrix_to_long(matrix(c(100, 100, 100, 100, 100, 100), ncol = 2))
     expect_equal(dissimilarity(m0, "group", "unit", weight = "n")$est[[1]], 0)
 
@@ -17,8 +19,6 @@ test_that("correct calculations", {
 })
 
 test_that("alternative calculation", {
-    testthat::skip_on_cran()
-
     tab <- t(matrix(c(100, 60, 40, 0, 0, 40, 60, 100), ncol = 2))
     div <- sweep(tab, 1, rowSums(tab), "/")
     d <- 1 / 2 * sum(apply(div, 2, segregation:::abs_diff))
@@ -27,7 +27,6 @@ test_that("alternative calculation", {
 })
 
 test_that("SE works", {
-    testthat::skip_on_cran()
     m0 <- matrix_to_long(matrix(c(100, 60, 40, 0, 0, 40, 60, 100), ncol = 2))
     d <- dissimilarity(m0, "group", "unit", weight = "n", se = TRUE)
     expect_equal(dim(d), c(1, 5))
@@ -36,8 +35,6 @@ test_that("SE works", {
 })
 
 test_that("names of columns", {
-    testthat::skip_on_cran()
-
     m0 <- matrix_to_long(matrix(c(100, 60, 40, 0, 0, 40, 60, 100), ncol = 2),
         group = "race", unit = "tract"
     )
@@ -52,15 +49,11 @@ test_that("names of columns", {
 
 
 test_that("bootstrapping fails when sample size is non-integer", {
-    testthat::skip_on_cran()
-
     m0 <- matrix_to_long(matrix(c(100.3, 60, 40, 0, 0, 40, 60, 100), ncol = 2))
     expect_error(dissimilarity(m0, "group", "unit", weight = "n", se = TRUE))
 })
 
 test_that("gives error when group > 2", {
-    testthat::skip_on_cran()
-
     m0 <- matrix_to_long(matrix(c(100, 60, 40, 10, 20, 40, 60, 100, 50), ncol = 3))
     expect_error(dissimilarity(m0, "group", "unit", weight = "n"))
 })
diff --git a/tests/testthat/test_entropy.R b/tests/testthat/test_entropy.R
index e7e9b6d..e5afeeb 100644
--- a/tests/testthat/test_entropy.R
+++ b/tests/testthat/test_entropy.R
@@ -1,15 +1,15 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_entropy")
 
 test_that("custom log function", {
-    testthat::skip_on_cran()
-
     expect_equal(logf(2, exp(1)), log(2))
 })
 
 test_that("correct entropy calculation", {
-    testthat::skip_on_cran()
-
     expect_equal(entropy(data.frame(x = c(1)), "x"), 0)
     expect_equal(entropy(data.frame(x = c(1, 2)), "x"), log(2))
     expect_equal(entropy(data.frame(x = c(1, 2, 3)), "x"), log(3))
diff --git a/tests/testthat/test_exposure_isolation.R b/tests/testthat/test_exposure_isolation.R
index 9f419a3..088d4ad 100644
--- a/tests/testthat/test_exposure_isolation.R
+++ b/tests/testthat/test_exposure_isolation.R
@@ -1,9 +1,11 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_exposure_isolation")
 
 test_that("two group case", {
-    testthat::skip_on_cran()
-
     two <- data.table::as.data.table(schools00)
     two <- two[race %in% c("white", "black")]
     exp <- exposure(two, "race", "school", "n")
@@ -19,8 +21,6 @@ test_that("two group case", {
 })
 
 test_that("exposure", {
-    testthat::skip_on_cran()
-
     exp <- exposure(schools00, "race", "school", "n")
     expect_equal(
         exp[, .(sum = sum(exposure)), by = .(of)][["sum"]],
@@ -29,8 +29,6 @@ test_that("exposure", {
 })
 
 test_that("exposure and isolation", {
-    testthat::skip_on_cran()
-
     exp <- exposure(schools00, "race", "school", "n")[of == to]
     iso <- isolation(schools00, "race", "school", "n")
     comp <- merge(exp, iso, by.x = "of", by.y = "race")
diff --git a/tests/testthat/test_ipf.R b/tests/testthat/test_ipf.R
index 03482de..2811b04 100644
--- a/tests/testthat/test_ipf.R
+++ b/tests/testthat/test_ipf.R
@@ -1,9 +1,11 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_ipf")
 
 test_that("different precisions", {
-    testthat::skip_on_cran()
-
     # reduce to overlap sample
     schools00_r <- schools00[schools00$school %in% schools05$school, ]
     schools05_r <- schools05[schools05$school %in% schools00$school, ]
@@ -30,8 +32,6 @@ test_that("different precisions", {
 })
 
 test_that("warn if iterations are too low", {
-    testthat::skip_on_cran()
-
     expect_error(
         suppressWarnings(
             ipf(schools00, schools05, "race", "school",
@@ -43,8 +43,6 @@ test_that("warn if iterations are too low", {
 })
 
 test_that("gives sames results as mutual_difference", {
-    testthat::skip_on_cran()
-
     diff <- mutual_difference(schools00, schools05,
         group = "race", unit = "school",
         weight = "n", method = "km", precision = 0.000001
@@ -72,8 +70,6 @@ test_that("gives sames results as mutual_difference", {
 })
 
 test_that("example from Karmel & Maclachlan 1988", {
-    testthat::skip_on_cran()
-
     source <- data.frame(
         occ = rep(c(1, 2, 3), 2),
         gender = c(rep("male", 3), rep("female", 3)),
@@ -108,15 +104,11 @@ test_that("example from Karmel & Maclachlan 1988", {
 })
 
 test_that("warning about units and groups being dropped", {
-    testthat::skip_on_cran()
-
     expect_warning(ipfd <- ipf(schools00, schools05, "race", "school", weight = "n"))
     expect_equal(sum(ipfd$n), sum(ipfd$n_source))
 })
 
 test_that("returns same number of observations as before", {
-    testthat::skip_on_cran()
-
     # schools are dropped here
     suppressWarnings(ipfd <- ipf(schools00, schools05, "race", "school", weight = "n"))
     expect_equal(sum(ipfd$n), sum(ipfd$n_source))
diff --git a/tests/testthat/test_matrix_to_long.R b/tests/testthat/test_matrix_to_long.R
index b5e006d..6060d9b 100644
--- a/tests/testthat/test_matrix_to_long.R
+++ b/tests/testthat/test_matrix_to_long.R
@@ -1,16 +1,16 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_matrix_to_long")
 
 test_that("accept only matrix", {
-    testthat::skip_on_cran()
-
     a <- data.frame()
     expect_error(matrix_to_long(a))
 })
 
 test_that("no names", {
-    testthat::skip_on_cran()
-
     m <- matrix(c(10, 20, 30, 30, 20, 10), nrow = 3)
     long <- matrix_to_long(m)
     expect_equal(names(long), c("unit", "group", "n"))
@@ -19,8 +19,6 @@ test_that("no names", {
 })
 
 test_that("rownames only", {
-    testthat::skip_on_cran()
-
     m <- matrix(c(10, 20, 30, 30, 20, 10), nrow = 3)
     colnames(m) <- c("A", "B")
     long <- matrix_to_long(m)
@@ -30,8 +28,6 @@ test_that("rownames only", {
 })
 
 test_that("colnames only", {
-    testthat::skip_on_cran()
-
     m <- matrix(c(10, 20, 30, 30, 20, 10), nrow = 3)
     rownames(m) <- c("S1", "S2", "S3")
     long <- matrix_to_long(m)
@@ -41,8 +37,6 @@ test_that("colnames only", {
 })
 
 test_that("rownames + colnames", {
-    testthat::skip_on_cran()
-
     m <- matrix(c(10, 20, 30, 30, 20, 10), nrow = 3)
     colnames(m) <- c("A", "B")
     rownames(m) <- c("S1", "S2", "S3")
@@ -53,8 +47,6 @@ test_that("rownames + colnames", {
 })
 
 test_that("arguments", {
-    testthat::skip_on_cran()
-
     # drop zero
     m <- matrix(c(10, 20, 30, 0, 20, 0), nrow = 3)
     long1 <- matrix_to_long(m)
diff --git a/tests/testthat/test_mutual_difference.R b/tests/testthat/test_mutual_difference.R
index 8a9092b..1397a92 100644
--- a/tests/testthat/test_mutual_difference.R
+++ b/tests/testthat/test_mutual_difference.R
@@ -1,3 +1,7 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_mutual_diff")
 
@@ -13,12 +17,10 @@ test_data2 <- data.frame(
 )
 
 test_that("mutual_difference method not defined error", {
-    testthat::skip_on_cran()
     expect_error(mutual_difference(test_data1, test_data2, "g", "u", weight = "n", method = "X"))
 })
 
 test_that("mutual_difference SHAPLEY", {
-    testthat::skip_on_cran()
     sh1 <- mutual_difference(test_data1, test_data2, "g", "u", weight = "n", method = "shapley")
     sh2 <- mutual_difference(test_data1, test_data2, "u", "g", weight = "n", method = "shapley")
     sh3 <- mutual_difference(test_data2, test_data1, "g", "u", weight = "n", method = "shapley")
@@ -100,7 +102,6 @@ test_that("mutual_difference SHAPLEY", {
 })
 
 test_that("mutual_difference KM", {
-    testthat::skip_on_cran()
     ret1 <- mutual_difference(test_data1, test_data2, "g", "u",
         weight = "n", method = "km",
         precision = .0001
@@ -156,7 +157,6 @@ test_that("mutual_difference KM", {
 
 
 test_that("mutual_difference MRC", {
-    testthat::skip_on_cran()
     ret <- mutual_difference(test_data1, test_data2,
         "g", "u",
         weight = "n", method = "mrc"
@@ -198,7 +198,6 @@ test_that("mutual_difference MRC", {
 
 
 test_that("mutual_difference SE", {
-    testthat::skip_on_cran()
     ret <- mutual_difference(test_data1, test_data2, "g", "u",
         weight = "n",
         method = "shapley", se = TRUE, n_bootstrap = 5
@@ -228,7 +227,6 @@ test_that("mutual_difference SE", {
 })
 
 test_that("mutual_difference shapley_detailed structural", {
-    testthat::skip_on_cran()
     diff_simple <- mutual_difference(schools05, schools00,
         group = "school", unit = "race",
         weight = "n", method = "shapley", precision = .000001
@@ -273,7 +271,6 @@ test_that("mutual_difference shapley_detailed structural", {
 })
 
 test_that("mutual_difference shapley_detailed marginal", {
-    testthat::skip_on_cran()
     diff_simple <- mutual_difference(schools05, schools00,
         group = "school", unit = "race",
         weight = "n", method = "shapley", precision = .000001
@@ -314,7 +311,6 @@ test_that("mutual_difference shapley_detailed marginal", {
 
 
 test_that("mutual_difference shapley_detailed with SE", {
-    testthat::skip_on_cran()
     diff <- mutual_difference(schools05, schools00,
         group = "school", unit = "race",
         weight = "n", method = "shapley_detailed", precision = .1, se = TRUE, n_bootstrap = 2
@@ -326,8 +322,6 @@ test_that("mutual_difference shapley_detailed with SE", {
 
 
 test_that("mutual_difference log base", {
-    testthat::skip_on_cran()
-
     ret <- mutual_difference(test_data1, test_data2, "g", "u",
         weight = "n", method = "shapley", base = 2
     )
@@ -352,7 +346,6 @@ test_that("mutual_difference log base", {
 
 
 test_that("difference same as mutual_total (zero weights)", {
-    testthat::skip_on_cran()
     # test with zero weights
     test_data1 <- data.frame(
         u = c(rep("a", 4), rep("b", 4), rep("c", 4)),
@@ -402,7 +395,6 @@ test_that("difference same as mutual_total (zero weights)", {
 
 
 test_that("correctly identifies marginal/structural changes", {
-    testthat::skip_on_cran()
     test_data <- data.frame(
         u = c(rep("a", 2), rep("b", 2), rep("c", 2)),
         g = rep(c(1, 2), 3),
@@ -442,7 +434,6 @@ test_that("correctly identifies marginal/structural changes", {
 
 
 test_that("errors", {
-    testthat::skip_on_cran()
     schools00 <- as.data.table(schools00)
     schools05 <- as.data.table(schools05)
     expect_error(mutual_difference(
diff --git a/tests/testthat/test_mutual_expected.R b/tests/testthat/test_mutual_expected.R
index 32b1ec2..c74ade8 100644
--- a/tests/testthat/test_mutual_expected.R
+++ b/tests/testthat/test_mutual_expected.R
@@ -1,3 +1,7 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_mutual_expected")
 
@@ -16,8 +20,6 @@ data2 <- data.frame(
 )
 
 test_that("works both ways around", {
-    testthat::skip_on_cran()
-
     expect_equal(
         mutual_expected(data1, "u", "g", weight = "n")[stat == "M under 0", est],
         mutual_expected(data1, "g", "u", weight = "n")[stat == "M under 0", est],
@@ -32,8 +34,6 @@ test_that("works both ways around", {
 })
 
 test_that("fixed margins = FALSE", {
-    testthat::skip_on_cran()
-
     expect_equal(
         mutual_expected(data1, "u", "g", weight = "n", fixed_margins = FALSE)[stat == "M under 0", est],
         mutual_expected(data1, "g", "u", weight = "n", fixed_margins = FALSE)[stat == "M under 0", est],
@@ -42,8 +42,6 @@ test_that("fixed margins = FALSE", {
 })
 
 test_that("within argument", {
-    testthat::skip_on_cran()
-
     within <- mutual_expected(school_ses, "ethnic_group", "school_id", within = "ses_quintile")
     # manually
     d <- data.table::as.data.table(school_ses)
@@ -57,8 +55,6 @@ test_that("within argument", {
 
 
 test_that("dissimilarity", {
-    testthat::skip_on_cran()
-
     expect_error(dissimilarity_expected(data1, "u", "g", weight = "n"))
     expect_equal(dissimilarity_expected(data1, "g", "u", n_bootstrap = 500, weight = "n")$est,
         0.098,
@@ -75,8 +71,6 @@ test_that("dissimilarity", {
 })
 
 test_that("dissimilarity - Winship 1977", {
-    testthat::skip_on_cran()
-
     # see table 2
     mat <- matrix(c(rep(1, 1000), rep(9, 1000)), ncol = 2)
     d <- matrix_to_long(mat)
@@ -104,8 +98,6 @@ test_that("dissimilarity - Winship 1977", {
 })
 
 test_that("errors", {
-    testthat::skip_on_cran()
-
     dat <- data.frame(
         u = rep(c(1, 2, 3, 4), 2),
         g = c(rep("a", 4), rep("b", 4)),
diff --git a/tests/testthat/test_mutual_local.R b/tests/testthat/test_mutual_local.R
index 0f58682..82eae95 100644
--- a/tests/testthat/test_mutual_local.R
+++ b/tests/testthat/test_mutual_local.R
@@ -1,3 +1,7 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_mutual_local")
 
@@ -13,8 +17,6 @@ localse <- mutual_local(test_data, "u", "g", weight = "n", se = TRUE, n_bootstra
 localbase2 <- mutual_local(test_data, "g", "u", weight = "n", base = 2)
 
 test_that("local calculation works", {
-    testthat::skip_on_cran()
-
     expect_equal(sum(local[stat == "p", est]), 1)
     expect_equal(sum(local2[local2$stat == "p", est]), 1)
     expect_equal(sum(localse[localse$stat == "p", est]), 1)
@@ -34,28 +36,20 @@ test_that("local calculation works", {
 })
 
 test_that("return works", {
-    testthat::skip_on_cran()
-
     expect_equal(nrow(local), 8)
     expect_equal(ncol(local), 3)
 })
 
 test_that("bootstrapping works", {
-    testthat::skip_on_cran()
-
     expect_equal(nrow(localse), 8)
     expect_equal(ncol(localse), 6)
 })
 
 test_that("bootstrap attributes exists", {
-    testthat::skip_on_cran()
-
     expect_equal(dim(attr(localse, "bootstrap")), c(10 * length(unique(test_data$g)) * 2, 3))
 })
 
 test_that("bootstrapping fails when sample size is non-integer", {
-    testthat::skip_on_cran()
-
     test_data <- data.frame(
         u = c(rep("a", 4), rep("b", 4)),
         g = rep(c(1, 2, 3, 4), 2),
@@ -70,8 +64,6 @@ test_that("bootstrapping fails when sample size is non-integer", {
 })
 
 test_that("option wide works", {
-    testthat::skip_on_cran()
-
     nowide <- mutual_local(test_data, "u", "g", weight = "n")
     nowide_se <- mutual_local(test_data, "u", "g",
         weight = "n",
diff --git a/tests/testthat/test_mutual_total.R b/tests/testthat/test_mutual_total.R
index d1d8450..3c461aa 100644
--- a/tests/testthat/test_mutual_total.R
+++ b/tests/testthat/test_mutual_total.R
@@ -1,3 +1,7 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_mutual_total")
 
@@ -10,8 +14,6 @@ test_data <- data.frame(
 )
 
 test_that("mutual M works both ways around", {
-    testthat::skip_on_cran()
-
     expect_equal(
         mutual_total(test_data, "u", "g", weight = "n")[stat == "M", est],
         mutual_total(test_data, "g", "u", weight = "n")[stat == "M", est]
@@ -45,8 +47,6 @@ test_that("mutual M works both ways around", {
 })
 
 test_that("between + within = total", {
-    testthat::skip_on_cran()
-
     expect_equal(
         mutual_total(test_data, "u", "g", weight = "n")[stat == "M", est],
         mutual_total(test_data, "u", "supergroup", weight = "n")[stat == "M", est] +
@@ -62,8 +62,6 @@ test_that("between + within = total", {
 p_12 <- sum(test_data[test_data$supergroup == 12, "n"]) / sum(test_data$n)
 p_34 <- sum(test_data[test_data$supergroup == 34, "n"]) / sum(test_data$n)
 test_that("within estimations are correct", {
-    testthat::skip_on_cran()
-
     d_12 <- test_data[test_data$supergroup == 12, ]
     d_34 <- test_data[test_data$supergroup == 34, ]
     expect_equal(
@@ -75,15 +73,11 @@ test_that("within estimations are correct", {
 })
 
 test_that("H is correct", {
-    testthat::skip_on_cran()
-
     ret <- mutual_total(test_data, "u", "g", weight = "n")
     expect_equal(ret[stat == "H", est] >= 0 & ret[stat == "H", est] <= 1, TRUE)
 })
 
 test_that("bootstrapping works", {
-    testthat::skip_on_cran()
-
     ret <- mutual_total(test_data, "u", "g", weight = "n", se = TRUE, n_bootstrap = 10)
     expect_equal(dim(ret), c(2, 5))
     expect_equal(all(ret$se > 0), TRUE)
@@ -97,15 +91,11 @@ test_that("bootstrapping works", {
 })
 
 test_that("bootstrap attributes exists", {
-    testthat::skip_on_cran()
-
     ret <- mutual_total(test_data, "u", "g", weight = "n", se = TRUE, n_bootstrap = 10)
     expect_equal(dim(attr(ret, "bootstrap")), c(2 * 10, 2))
 })
 
 test_that("bootstrapping fails when sample size is non-integer", {
-    testthat::skip_on_cran()
-
     test_data <- data.frame(
         u = c(rep("a", 4), rep("b", 4)),
         g = rep(c(1, 2, 3, 4), 2),
@@ -129,8 +119,6 @@ test_data <- data.frame(
 )
 
 test_that("zero weights no problem", {
-    testthat::skip_on_cran()
-
     expect_equal(dim(mutual_total(test_data, "u", "g",
         weight = "n",
         se = TRUE, n_bootstrap = 10
@@ -148,8 +136,6 @@ test_that("zero weights no problem", {
 })
 
 test_that("gives errors", {
-    testthat::skip_on_cran()
-
     expect_error(mutual_total("test_data", "u", "g", weight = "n"), "not a data.frame")
     expect_error(
         mutual_total(test_data[test_data$u == "c", ], "u", "g", weight = "n"),
@@ -167,8 +153,6 @@ test_that("gives errors", {
 })
 
 test_that("debiasing works correctly", {
-    testthat::skip_on_cran()
-
     nose <- mutual_total(test_data, "u", "g", weight = "n")
     withse <- mutual_total(test_data, "u", "g", weight = "n", se = TRUE)
     expect_equal(nose$est, withse$est + withse$bias)
diff --git a/tests/testthat/test_mutual_total_nested.R b/tests/testthat/test_mutual_total_nested.R
index ffe2188..099c419 100644
--- a/tests/testthat/test_mutual_total_nested.R
+++ b/tests/testthat/test_mutual_total_nested.R
@@ -1,9 +1,11 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_mutual_total_nested")
 
 test_that("works both ways around", {
-    testthat::skip_on_cran()
-
     decomp <- mutual_total_nested(schools00, "race",
         c("state", "district", "school"),
         weight = "n"
diff --git a/tests/testthat/test_mutual_within.R b/tests/testthat/test_mutual_within.R
index 05dd4ca..fefc432 100644
--- a/tests/testthat/test_mutual_within.R
+++ b/tests/testthat/test_mutual_within.R
@@ -1,3 +1,7 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("test_mutual_within")
 
@@ -10,8 +14,6 @@ test_data <- data.frame(
 )
 
 test_that("dimensions and bootstrapping", {
-    testthat::skip_on_cran()
-
     within <- mutual_within(test_data, "u", "g",
         within = "supergroup", weight = "n"
     )
@@ -24,8 +26,6 @@ test_that("dimensions and bootstrapping", {
 })
 
 test_that("bootstrap attributes exists", {
-    testthat::skip_on_cran()
-
     within_se <- mutual_within(test_data, "u", "g",
         within = "supergroup", weight = "n", se = TRUE, n_bootstrap = 10
     )
@@ -34,8 +34,6 @@ test_that("bootstrap attributes exists", {
 })
 
 test_that("bootstrapping fails when sample size is non-integer", {
-    testthat::skip_on_cran()
-
     test_data <- data.frame(
         u = c(rep("a", 4), rep("b", 4)),
         g = rep(c(1, 2, 3, 4), 2),
@@ -56,8 +54,6 @@ test_that("bootstrapping fails when sample size is non-integer", {
 })
 
 test_that("between + within = total", {
-    testthat::skip_on_cran()
-
     total <- mutual_total(test_data, "u", "g", within = "supergroup", weight = "n")
     m <- total[stat == "M", est]
     h <- total[stat == "H", est]
@@ -84,8 +80,6 @@ test_that("between + within = total", {
 })
 
 test_that("option wide works", {
-    testthat::skip_on_cran()
-
     nowide <- mutual_within(test_data, "u", "g",
         within = "supergroup", weight = "n"
     )
diff --git a/tests/testthat/test_plots.R b/tests/testthat/test_plots.R
index 63a360f..08bd34d 100644
--- a/tests/testthat/test_plots.R
+++ b/tests/testthat/test_plots.R
@@ -1,3 +1,7 @@
+if (!identical(Sys.getenv("NOT_CRAN"), "true")) {
+    return()
+}
+
 library("segregation")
 context("plots")
 
@@ -12,16 +16,12 @@ plot_entropy <- segplot(schools00, "race", "school",
 )
 
 test_that("dimensions", {
-    testthat::skip_on_cran()
-
     expect_equal(nrow(plot_majority$data), nrow(plot_seg$data))
     expect_equal(nrow(plot_majority$data), nrow(plot_entropy$data))
     expect_equal(nrow(plot_majority$data), nrow(plot_majority_fixed$data))
 })
 
 test_that("reference", {
-    testthat::skip_on_cran()
-
     reference <- data.table::as.data.table(schools00)
     reference <- reference[, .(N = sum(n)), by = .(race)]
     reference[, p := N / sum(N)]
@@ -51,8 +51,6 @@ test_that("reference", {
 })
 
 test_that("axis_labels", {
-    testthat::skip_on_cran()
-
     left <- segplot(schools00, "race", "school", weight = "n", axis_labels = "left")
     right <- segplot(schools00, "race", "school", weight = "n", axis_labels = "right")
     both <- segplot(schools00, "race", "school", weight = "n", axis_labels = "both")
@@ -61,8 +59,6 @@ test_that("axis_labels", {
 })
 
 test_that("segcurve", {
-    testthat::skip_on_cran()
-
     expect_error(segcurve(schools00, "race", "school", weight = "n"))
 
     p1 <- segcurve(subset(schools00, race %in% c("white", "black")),
diff --git a/vignettes/segregation.Rmd b/vignettes/segregation.Rmd
index 3d42a46..72da5ed 100644
--- a/vignettes/segregation.Rmd
+++ b/vignettes/segregation.Rmd
@@ -98,10 +98,9 @@ Therefore, if the H index is used, it is important to specify the groups and uni
 For inference (discussed in more detail [below](#inference)), we can use bootstrapping to obtain standard errors and confidence intervals:
 
 ```{r}
-# setting n_bootstrap = 100 here to speed up - more iterations are recommended in practice
 mutual_total(schools00, "race", "school",
   weight = "n",
-  se = TRUE, CI = .95, n_bootstrap = 100
+  se = TRUE, CI = .95, n_bootstrap = 500
 )
 ```
 
@@ -201,10 +200,9 @@ mutual_local(schools00, "race", "school", weight = "n", wide = TRUE)
 Local segregation scores are based on much less data than the full M index, so it often makes sense to obtain confidence intervals. The following code plots the length of the 95% confidence interval in relation to the size of each school:
 
 ```{r}
-# setting n_bootstrap = 100 here to speed up
 localse <- mutual_local(schools00, "race", "school",
   weight = "n",
-  se = TRUE, wide = TRUE, n_bootstrap = 100
+  se = TRUE, wide = TRUE, n_bootstrap = 500
 )
 localse$lengthCI <- sapply(localse$ls_CI, base::diff)
 with(localse, plot(x = p, y = lengthCI, pch = 16, cex = 0.3))
@@ -233,10 +231,9 @@ The four main functions of the packages, `mutual_total()`, `mutual_within()`, `m
 To estimate standard errors and confidence intervals, use `se = TRUE`. The coverage of the confidence interval can be specified in the `CI` argument. The number of bootstrap iterations can be specified as well:
 
 ```{r}
-# setting n_bootstrap = 100 here to speed up
 (se <- mutual_total(schools00, "race", "school",
   weight = "n",
-  se = TRUE, CI = .95, n_bootstrap = 100
+  se = TRUE, CI = .95, n_bootstrap = 500
 ))
 ```
 The confidence intervals are based on the percentiles from the bootstrap distribution, and hence require a large number of bootstrap iterations for valid interpretation. The estimate `est` that is reported in the results has already been "debiased", i.e. the bias that has been estimated from the bootstrap distribution (which is reported in `bias`) has been subtracted from the usual maximum-likelihood estimate that we would obtain from `mutual_total` with `se = FALSE`. The confidence interval is centered around the debiased estimate.
@@ -254,10 +251,9 @@ provide effectively the same coverage as the confidence intervals obtained from
 Whenever the bootstrap is used, the bootstrap distributions for each parameter are reported in an attribute `bootstrap` of the returned object. This can be used, for instance, to check whether the bootstrap distribution is skewed. The following code computes local segregation scores for all schools, and then shows a histogram of the bootstrap distribution for school C137_9, which has a very low local segregation score:
 
 ```{r}
-# setting n_bootstrap = 100 here to speed up
 local <- mutual_local(schools00, "race", "school",
   weight = "n",
-  se = TRUE, CI = .95, n_bootstrap = 100
+  se = TRUE, CI = .95, n_bootstrap = 500
 )
 # pick bootstrap distribution of local segregation scores for school C137_9
 ls_school <- attr(local, "bootstrap")[school == "C137_9" & stat == "ls", boot_est]
@@ -271,8 +267,7 @@ the package also provides a function `mutual_expected()` that simulates random c
 from the marginal distributions of your table. For the `schools00` dataset:
 
 ```{r}
-# setting n_bootstrap = 100 here to speed up
-mutual_expected(schools00, "race", "school", weight = "n", n_bootstrap = 100)
+mutual_expected(schools00, "race", "school", weight = "n", n_bootstrap = 500)
 ```
 
 Here, there is no concern about bias due to a small sample size.