diff --git a/examples/plotBounds.R b/examples/plotBounds.R
index c28479ea..42c34b05 100644
--- a/examples/plotBounds.R
+++ b/examples/plotBounds.R
@@ -1,9 +1 @@
 # examples
-plotBounds(boundsPredprob(
-  nvec = c(10, 20, 30, 40), p = 0.20, tT = 0.80,
-  phiL = 0.10, phiU = 0.90, a = 1, b = 1
-), yt = "x")
-plotBounds(boundsPredprob(
-  nvec = c(10, 20, 30, 40), p = 0.20, tT = 0.80,
-  phiL = 0.10, phiU = 0.90, a = 1, b = 1
-), yt = "p")
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 25c8d9e2..63e17a09 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,3 +117,124 @@ The criteria for Decision 2 for Futility looks are :
 }
 }
 }
+\examples{
+# Here we illustrate an example for Decision 1 with the following assumptions :
+# True response rate or truep of the treatment group = 40\%
+# The following are the Final Stop rules respectively :
+# - Final look for Efficacy: Pr( response rate > 25\% ) > 60\% or P(response rate > p0) > tT
+# - Final look for Futility: Pr( response rate < 25\% ) < 60\% or P(response rate > p0) < tT
+# - Interim look for Efficacy: Pr( success at final ) > 80\% or P(success at final) > phiU
+# - Interim look for Futility: Pr( failure at final ) < 20\% or P(success at final) < phiL
+# We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
+
+# Decision 1 with no wiggle.
+set.seed(20)
+result <- ocPredprob(
+  nnE = c(10, 20),
+  truep = 0.4,
+  p0 = 0.25,
+  tT = 0.6,
+  phiL = 0.2,
+  phiU = 0.8,
+  parE = c(1, 1),
+  sim = 50,
+  wiggle = FALSE,
+  decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with wiggle.
+result <- ocPredprob(
+  nnE = c(10, 20),
+  truep = 0.4,
+  p0 = 0.25,
+  tT = 0.6,
+  phiL = 0.2,
+  phiU = 0.8,
+  parE = c(1, 1),
+  sim = 50,
+  wiggle = TRUE,
+  nnF = c(10, 20),
+  decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
+result <- ocPredprob(
+  nnE = c(10, 25, 30),
+  truep = 0.4,
+  p0 = 0.25,
+  p1 = 0.2,
+  tT = 0.6,
+  phiL = 0.2,
+  phiU = 0.8,
+  parE = c(1, 1),
+  sim = 50,
+  wiggle = FALSE,
+  nnF = c(10, 15, 20),
+  decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
+result <- ocPredprob(
+  nnE = c(10, 25, 30),
+  truep = 0.4,
+  p0 = 0.25,
+  p1 = 0.2,
+  tT = 0.6,
+  phiL = 0.2,
+  phiU = 0.8,
+  parE = c(1, 1),
+  sim = 50,
+  wiggle = TRUE,
+  nnF = c(10, 15, 20),
+  decision1 = TRUE
+)
+result$oc
+
+# Here we illustrate an example for Decision 2 with the following assumptions :
+# True response rate or truep of the treatment group = 60\%
+# The following are the Final Stop rules respectively :
+# - Final look for Efficacy: Pr( response rate > 25\% ) > 60\% or P(response rate > p0) > tT
+# - Final look for Futility: Pr( response rate < 25\% ) < 60\% or P(response rate < p1) > tF
+# - Interim look for Efficacy: Pr( success at final ) > 80\% or P(success at final) > phiU
+# - Interim look for Futility: Pr( failure at final ) > 80\% or P(failure at final) > phiFu
+# We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
+
+# Decision 2 without wiggle.
+result <- ocPredprob(
+  nnE = c(10, 20),
+  truep = 0.6,
+  p0 = 0.25,
+  p1 = 0.25,
+  tT = 0.6,
+  tF = 0.6,
+  phiU = 0.8,
+  phiFu = 0.8,
+  parE = c(1, 1),
+  sim = 50,
+  wiggle = FALSE,
+  nnF = c(10, 20),
+  decision1 = FALSE
+)
+result$oc
+
+# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
+result <- ocPredprob(
+  nnE = c(10, 25, 30),
+  truep = 0.6,
+  p0 = 0.25,
+  p1 = 0.25,
+  tT = 0.6,
+  tF = 0.6,
+  phiL = 0.8,
+  phiU = 0.8,
+  parE = c(11, 19),
+  sim = 50,
+  wiggle = TRUE,
+  nnF = 30,
+  decision1 = FALSE
+)
+result$oc
+}
diff --git a/tests/testthat/test-boundsPredprob.R b/tests/testthat/test-boundsPredprob.R
index 9ff4d20d..cc51ea33 100644
--- a/tests/testthat/test-boundsPredprob.R
+++ b/tests/testthat/test-boundsPredprob.R
@@ -1,4 +1,4 @@
-# boundsPredProb ----
+# boundsPredprob ----
 test_that("boundsPredprob gives correct result and when default weight is not assigned", {
   result_weights <- boundsPredprob(
     looks = c(10, 20, 30, 40),
@@ -40,7 +40,7 @@ test_that("boundsPredprob gives correct result and when default weight is not as
   expect_equal(result$LciU, c(0.15, 0.1773, 0.1663, 0.1424))
 })
 
-test_that("boundsPredprob with Beta Mixture Priors give correct results with predprob", {
+test_that("boundsPredprob with Beta Mixture Priors give correct results", {
   result <- boundsPredprob(
     looks = c(10, 20),
     p0 = 0.20,