diff --git a/.Rbuildignore b/.Rbuildignore
index ffee759..a6f9627 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -6,7 +6,7 @@
 ^Figures*
 man/hello.Rd
 ^LICENSE\.md$
-dependencies/
+^dev_resources/$
 ^\.github$
 ^PoolPoweR\.Rproj$
 ^CHANGELOG.md$
diff --git a/CHANGELOG.md b/CHANGELOG.md
index d8503ef..9c86db7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -23,8 +23,8 @@ and this project adheres to
 **Focus:** Package
 - [ ] Fix integrand issue  
 - [ ] Implement codecov.io  
+- [ ] Refactor functions (i.e. break down into smaller ones for readability)
 - [ ] Configure and optimise GHA workflows
-- [ ] Refer to package and tidyverse guides for coherency and note rooms for improvement
 
 ### [v0.0.3]
 **Focus:** Documentation
@@ -32,6 +32,7 @@ and this project adheres to
 - [ ] Rename interval [#13](https://github.com/AngusMcLure/PoolPoweR/issues/13)
 - [ ] Update docs with cluster and non-cluster cases
 [#14](https://github.com/AngusMcLure/PoolPoweR/issues/14)
+- [ ] Add PoolPoweR-package.R (#18)  
 - [x] Rename real.scale to real_scale
 
 ### [v0.0.2] - 2023-12-01
diff --git a/R/fisher_information.R b/R/fisher_information.R
index b643ef2..795ba57 100644
--- a/R/fisher_information.R
+++ b/R/fisher_information.R
@@ -305,7 +305,7 @@ fi_pool_cluster <- function(pool_size,
             prod(choose(N, y))
         })
       }
-    }
+    } ## End for form == "beta"
     tol <- 1e-5
     if (abs(sum(lik) - 1) > tol ||
       abs(sum(lik_theta)) > tol ||
diff --git a/R/optimise_prevalence.R b/R/optimise_prevalence.R
index 003c4ea..9aa933e 100644
--- a/R/optimise_prevalence.R
+++ b/R/optimise_prevalence.R
@@ -78,6 +78,8 @@ optimise_s_prevalence <- function(pool_number = 1,
          respect to s often has mulitple minima and therefore the discrete
          distribution is not currently supported for optimisation')
   }
+  if (interval < 0) stop("interval must be 0 or higher")
+
   invalid_cost <- FALSE # trigger for when costs are infinite to ensure that there's no cost output in these cases
   # print(c(theta = prevalence, sens = sensitivity, spec = specificity, unit = cost_unit, test = cost_pool, location =cost_cluster , rho = correlation, N = N, form = form, max_s = max_s))
 
@@ -173,8 +175,6 @@ optimise_s_prevalence <- function(pool_number = 1,
       cost_interval = cost_interval,
       catch_interval = N * c(lower, upper)
     )
-  } else {
-    stop("interval must be between 0 and 1.")
   }
   out
 }
@@ -219,10 +219,10 @@ optimise_sN_prevalence <- function(prevalence,
     }
     opt$N <- Nopt
     if (opt$N == max_N) {
-      warning("Maximum cost effectivness is achieved at or above the maximum number of pools allowed. Consider increasing max.N")
+      warning("Maximum cost effectivness is achieved at or above the maximum number of pools allowed. Consider increasing max_N")
     }
     if (opt$s == max_s) {
-      warning("Maximum cost effectivness is achieved at or above the maximum size of pools allowed. Consider increasing max.s")
+      warning("Maximum cost effectivness is achieved at or above the maximum size of pools allowed. Consider increasing max_s")
     }
   }
 
diff --git a/dependencies/README.md b/dev_resources/README.md
similarity index 100%
rename from dependencies/README.md
rename to dev_resources/README.md
diff --git a/dev_resources/cov.csv b/dev_resources/cov.csv
new file mode 100644
index 0000000..485cf45
--- /dev/null
+++ b/dev_resources/cov.csv
@@ -0,0 +1,290 @@
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diff --git a/dev_resources/coverage.csv b/dev_resources/coverage.csv
new file mode 100644
index 0000000..485cf45
--- /dev/null
+++ b/dev_resources/coverage.csv
@@ -0,0 +1,290 @@
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+"108","R/fisher_information.R","fi_pool_cluster",286,13,286,27,13,27,350,350,1152
+"109","R/fisher_information.R","fi_pool_cluster",288,11,289,30,11,30,352,353,1152
+"110","R/fisher_information.R","fi_pool_cluster",292,9,298,10,9,10,356,362,15
+"111","R/fisher_information.R","fi_pool_cluster",293,11,295,69,11,69,357,359,1100
+"112","R/fisher_information.R","fi_pool_cluster",294,13,294,33,13,33,358,358,1100
+"113","R/fisher_information.R","fi_pool_cluster",296,11,297,30,11,30,360,361,1100
+"114","R/fisher_information.R","fi_pool_cluster",300,9,306,10,9,10,364,370,15
+"115","R/fisher_information.R","fi_pool_cluster",301,11,303,80,11,80,365,367,1099
+"116","R/fisher_information.R","fi_pool_cluster",302,13,302,31,13,31,366,366,1099
+"117","R/fisher_information.R","fi_pool_cluster",304,11,305,30,11,30,368,369,1099
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+"125","R/fisher_information.R","fi_pool_cluster",318,7,318,25,7,25,382,382,1
+"126","R/fisher_information.R","fi_pool_cluster",319,7,319,14,7,14,383,383,1
+"127","R/fisher_information.R","fi_pool_cluster",320,7,320,14,7,14,384,384,1
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+"129","R/fisher_information.R","fi_pool_cluster",322,7,322,19,7,19,386,386,1
+"130","R/fisher_information.R","fi_pool_cluster",323,7,323,16,7,16,387,387,1
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+"160","R/fisher_information.R","fi_pool_cluster",394,5,394,15,5,15,458,458,2
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+"188","R/fisher_information.R","fi_pool_cluster",443,7,443,106,7,106,507,507,2
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+"195","R/fisher_information.R","fi_pool_cluster",459,7,459,31,7,31,523,523,2
+"196","R/fisher_information.R","fi_pool_cluster",462,5,462,126,5,126,526,526,0
+"197","R/optimise_prevalence.R","optimise_s_prevalence",74,3,74,18,3,18,603,603,16
+"198","R/optimise_prevalence.R","optimise_s_prevalence",76,7,76,24,7,24,76,76,16
+"199","R/optimise_prevalence.R","optimise_s_prevalence",77,5,79,67,5,67,606,608,1
+"200","R/optimise_prevalence.R","optimise_s_prevalence",81,7,81,18,7,18,81,81,15
+"201","R/optimise_prevalence.R","optimise_s_prevalence",81,21,81,56,21,56,81,81,1
+"202","R/optimise_prevalence.R","optimise_s_prevalence",83,3,83,23,3,23,612,612,14
+"203","R/optimise_prevalence.R","optimise_s_prevalence",93,7,93,22,7,22,93,93,14
+"204","R/optimise_prevalence.R","optimise_s_prevalence",94,5,94,18,5,18,623,623,2
+"205","R/optimise_prevalence.R","optimise_s_prevalence",95,5,95,18,5,18,624,624,2
+"206","R/optimise_prevalence.R","optimise_s_prevalence",96,5,96,24,5,24,625,625,2
+"207","R/optimise_prevalence.R","optimise_s_prevalence",106,7,106,22,7,22,106,106,14
+"208","R/optimise_prevalence.R","optimise_s_prevalence",107,5,107,18,5,18,636,636,1
+"209","R/optimise_prevalence.R","optimise_s_prevalence",108,5,108,18,5,18,637,637,1
+"210","R/optimise_prevalence.R","optimise_s_prevalence",109,5,109,24,5,24,638,638,1
+"211","R/optimise_prevalence.R","optimise_s_prevalence",111,7,111,24,7,24,111,111,14
+"212","R/optimise_prevalence.R","optimise_s_prevalence",112,5,115,5,5,5,641,644,9
+"213","R/optimise_prevalence.R","optimise_s_prevalence",113,7,113,83,7,83,642,642,336
+"214","R/optimise_prevalence.R","optimise_s_prevalence",114,7,114,9,7,9,643,643,336
+"215","R/optimise_prevalence.R","optimise_s_prevalence",117,5,127,5,5,5,646,656,5
+"216","R/optimise_prevalence.R","optimise_s_prevalence",118,7,125,7,7,7,647,654,103
+"217","R/optimise_prevalence.R","optimise_s_prevalence",126,7,126,9,7,9,655,655,102
+"218","R/optimise_prevalence.R","optimise_s_prevalence",130,3,130,43,3,43,659,659,14
+"219","R/optimise_prevalence.R","optimise_s_prevalence",131,3,131,48,3,48,660,660,13
+"220","R/optimise_prevalence.R","optimise_s_prevalence",132,3,132,44,3,44,661,661,13
+"221","R/optimise_prevalence.R","optimise_s_prevalence",134,7,134,39,7,39,134,134,13
+"222","R/optimise_prevalence.R","optimise_s_prevalence",135,5,135,29,5,29,664,664,5
+"223","R/optimise_prevalence.R","optimise_s_prevalence",136,5,136,32,5,32,665,665,5
+"224","R/optimise_prevalence.R","optimise_s_prevalence",138,5,138,27,5,27,667,667,8
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+"226","R/optimise_prevalence.R","optimise_s_prevalence",142,7,142,16,7,16,142,142,13
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+"228","R/optimise_prevalence.R","optimise_s_prevalence",144,7,144,19,7,19,144,144,13
+"229","R/optimise_prevalence.R","optimise_s_prevalence",145,9,145,20,9,20,145,145,8
+"230","R/optimise_prevalence.R","optimise_s_prevalence",145,23,145,36,23,36,145,145,1
+"231","R/optimise_prevalence.R","optimise_s_prevalence",146,5,146,55,5,55,675,675,8
+"232","R/optimise_prevalence.R","optimise_s_prevalence",147,14,147,25,14,25,147,147,5
+"233","R/optimise_prevalence.R","optimise_s_prevalence",148,5,148,41,5,41,677,677,5
+"234","R/optimise_prevalence.R","optimise_s_prevalence",149,9,149,26,9,26,149,149,5
+"235","R/optimise_prevalence.R","optimise_s_prevalence",150,7,150,16,7,16,679,679,2
+"236","R/optimise_prevalence.R","optimise_s_prevalence",152,7,154,23,7,23,681,683,3
+"237","R/optimise_prevalence.R","optimise_s_prevalence",153,9,153,26,9,26,682,682,26
+"238","R/optimise_prevalence.R","optimise_s_prevalence",156,9,156,30,9,30,156,156,5
+"239","R/optimise_prevalence.R","optimise_s_prevalence",157,7,157,20,7,20,686,686,1
+"240","R/optimise_prevalence.R","optimise_s_prevalence",158,7,158,132,7,132,687,687,1
+"241","R/optimise_prevalence.R","optimise_s_prevalence",160,7,162,27,7,27,689,691,4
+"242","R/optimise_prevalence.R","optimise_s_prevalence",161,9,161,26,9,26,690,690,52
+"243","R/optimise_prevalence.R","optimise_s_prevalence",164,9,164,20,9,20,164,164,5
+"244","R/optimise_prevalence.R","optimise_s_prevalence",165,7,165,20,7,20,694,694,2
+"245","R/optimise_prevalence.R","optimise_s_prevalence",166,7,166,25,7,25,695,695,2
+"246","R/optimise_prevalence.R","optimise_s_prevalence",168,7,168,50,7,50,697,697,3
+"247","R/optimise_prevalence.R","optimise_s_prevalence",170,5,177,5,5,5,699,706,5
+"248","R/optimise_prevalence.R","optimise_s_prevalence",179,3,179,5,3,5,708,708,5
+"249","R/optimise_prevalence.R","optimise_sN_prevalence",196,7,196,44,7,44,196,196,3
+"250","R/optimise_prevalence.R","optimise_sN_prevalence",197,5,200,5,5,5,726,729,1
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+"255","R/optimise_prevalence.R","optimise_sN_prevalence",207,5,219,5,5,5,736,748,2
+"256","R/optimise_prevalence.R","optimise_sN_prevalence",208,7,211,7,7,7,737,740,4
+"257","R/optimise_prevalence.R","optimise_sN_prevalence",213,11,213,33,11,33,213,213,4
+"258","R/optimise_prevalence.R","optimise_sN_prevalence",214,9,214,13,9,13,743,743,2
+"259","R/optimise_prevalence.R","optimise_sN_prevalence",216,9,216,24,9,24,745,745,2
+"260","R/optimise_prevalence.R","optimise_sN_prevalence",217,9,217,22,9,22,746,746,2
+"261","R/optimise_prevalence.R","optimise_sN_prevalence",220,5,220,17,5,17,749,749,2
+"262","R/optimise_prevalence.R","optimise_sN_prevalence",221,9,221,22,9,22,221,221,2
+"263","R/optimise_prevalence.R","optimise_sN_prevalence",222,7,222,129,7,129,751,751,0
+"264","R/optimise_prevalence.R","optimise_sN_prevalence",224,9,224,22,9,22,224,224,2
+"265","R/optimise_prevalence.R","optimise_sN_prevalence",225,7,225,127,7,127,754,754,0
+"266","R/optimise_prevalence.R","optimise_sN_prevalence",229,3,229,5,3,5,758,758,3
+"267","R/optimise_prevalence.R","cost_fi",233,3,234,60,3,60,762,763,336
+"268","R/optimise_prevalence.R","cost_fi_cluster",247,3,247,16,3,16,776,776,103
+"269","R/optimise_prevalence.R","cost_fi_cluster",248,3,248,18,3,18,777,777,103
+"270","R/optimise_prevalence.R","cost_fi_cluster",250,3,250,86,3,86,779,779,103
+"271","R/optimise_prevalence.R","cost_fi_cluster",251,3,251,68,3,68,780,780,102
+"272","R/optimise_prevalence.R","cost_fi_cluster",253,3,253,24,3,24,782,782,102
+"273","R/util.R","cloglog",5,3,5,18,3,18,789,789,2
+"274","R/util.R","cloglog_inv",8,3,8,18,3,18,792,792,29878
+"275","R/util.R","mu_sigma_linknorm",14,6,14,31,6,31,798,798,2
+"276","R/util.R","mu_sigma_linknorm",15,5,15,69,5,69,799,799,0
+"277","R/util.R","mu_sigma_linknorm",17,3,17,26,3,26,801,801,2
+"278","R/util.R","mu_sigma_linknorm",18,3,18,14,3,14,802,802,2
+"279","R/util.R","mu_sigma_linknorm",19,3,27,3,3,3,803,811,2
+"280","R/util.R","mu_sigma_linknorm",20,5,20,14,5,14,804,804,338
+"281","R/util.R","mu_sigma_linknorm",21,5,21,22,5,22,805,805,338
+"282","R/util.R","mu_sigma_linknorm",22,5,22,75,5,75,806,806,338
+"283","R/util.R","mu_sigma_linknorm",23,5,23,71,5,71,807,807,338
+"284","R/util.R","mu_sigma_linknorm",24,5,24,86,5,86,808,808,338
+"285","R/util.R","mu_sigma_linknorm",25,5,25,71,5,71,809,809,338
+"286","R/util.R","mu_sigma_linknorm",26,5,26,46,5,46,810,810,338
+"287","R/util.R","mu_sigma_linknorm",28,3,28,84,3,84,812,812,2
+"288","R/util.R","mu_sigma_linknorm",29,3,29,23,3,23,813,813,2
+"289","R/util.R","mu_sigma_linknorm",30,3,30,5,3,5,814,814,2
diff --git a/dependencies/dep.png b/dev_resources/dep.png
similarity index 100%
rename from dependencies/dep.png
rename to dev_resources/dep.png
diff --git a/man/hello.Rd b/man/hello.Rd
deleted file mode 100644
index 0fa7c4b..0000000
--- a/man/hello.Rd
+++ /dev/null
@@ -1,12 +0,0 @@
-\name{hello}
-\alias{hello}
-\title{Hello, World!}
-\usage{
-hello()
-}
-\description{
-Prints 'Hello, world!'.
-}
-\examples{
-hello()
-}
diff --git a/tests/testthat/test-design_effect.R b/tests/testthat/test-design_effect.R
index e75f9ea..d43f96d 100644
--- a/tests/testthat/test-design_effect.R
+++ b/tests/testthat/test-design_effect.R
@@ -1,4 +1,15 @@
 test_that("design_effect() gives consistent output for basic tests", {
+  # This one has reasonable inputs
+  expect_equal(
+    design_effect(
+      pool_size = 5,
+      pool_number = 10,
+      prevalence = 0.01,
+      correlation = 0.05,
+      sensitivity = 0.99,
+      specificity = 0.95),
+    0.7240988, tolerance = 1e-7
+  )
   expect_equal(
     design_effect(
       pool_size = 10,
diff --git a/tests/testthat/test-optimise_prevalence.R b/tests/testthat/test-optimise_prevalence.R
index 07b0061..08c0c23 100644
--- a/tests/testthat/test-optimise_prevalence.R
+++ b/tests/testthat/test-optimise_prevalence.R
@@ -1,4 +1,11 @@
 test_that("optimise_sN_prevalence() gives consistent output for basic tests", {
+  # Reasonable params
+  expect_true(all.equal(
+    optimise_sN_prevalence(
+      prevalence = 0.01, cost_unit = 5, cost_pool = 10,
+      cost_cluster = 100, correlation = 0.05
+    ), list(s=5,cost =0.2513798, catch=20, N=4),tolerance=1e-7))
+  # Rest not so much
   expect_true(all.equal(
     optimise_sN_prevalence(
       prevalence = 0.9,
@@ -31,7 +38,7 @@ test_that("optimise_sN_prevalence() gives consistent output for basic tests", {
   ))
 })
 
-test_that("optimise_sN_prevalence() basic tests when correlation == 0 (flow control)", {
+test_that("optimise_sN_prevalence() when correlation == 0", {
   expect_true(all.equal(
     optimise_sN_prevalence(
       prevalence = 0.9,
@@ -49,7 +56,33 @@ test_that("optimise_sN_prevalence() basic tests when correlation == 0 (flow cont
   ))
 })
 
+test_that("optimise_sN_prevalence() when opt$N == max_N", {
+  expect_warning(
+    optimise_sN_prevalence(
+      prevalence = 0.01, cost_unit = 5, cost_pool = 10,
+      cost_cluster = 100, correlation = 0.05, max_N = 4),
+    "Maximum cost effectivness is achieved at or above the maximum number of pools allowed. Consider increasing max_N")
+})
+
+# TODO: fix this
+#test_that("optimise_sN_prevalence() when opt$s == max_s", {
+#  # Not an amazing text, catches all the optimise_s_prevalence() warnings too
+#  tryCatch(
+#  optimise_sN_prevalence(
+#    prevalence = 0.01, cost_unit = 5, cost_pool = 10,
+#    cost_cluster = 100, correlation = 0.05, max_s = 5)
+#  )
+#  expect_warning()
+#
+#})
+
 test_that("optimise_s_prevalence() gives consistent output for basic tests", {
+  # Reasonable parameters
+  expect_true(all.equal(
+    optimise_s_prevalence(prevalence = 0.01, cost_unit = 5, cost_pool = 10),
+    list(s=19, cost=0.05998076, catch=19), tolerance = 1e-7
+  ))
+  # Not very
   expect_true(all.equal(
     optimise_s_prevalence(
       prevalence = 0.7, cost_unit = 10, cost_pool = 100,
@@ -72,7 +105,42 @@ test_that("optimise_s_prevalence() gives consistent output for basic tests", {
   ))
 })
 
+test_that("optimise_s_prevalence() throws error when form == 'discrete'", {
+  expect_error(optimise_s_prevalence(
+    prevalence = 0.01, cost_unit = 5, cost_pool = 10, form = "discrete"),
+    'When form = "discrete" the cost of unit information function with
+         respect to s often has mulitple minima and therefore the discrete
+         distribution is not currently supported for optimisation')
+})
+
+test_that("optimise_s_prevalence() when cost_unit == Inf", {
+  expect_true(all.equal(
+    optimise_s_prevalence(prevalence = 0.01, cost_unit = Inf, cost_pool = 10, interval = 0.1),
+    list(s=1, cost=NA, catch=1, s_interval=c(1,19), cost_interval=NA, catch_interval=c(1,19))
+    ))
+})
+
+test_that("optimise_s_prevalence() when cost_pool == Inf", {
+  expect_true(all.equal(
+    optimise_s_prevalence(prevalence = 0.1, cost_unit = 5, cost_pool = Inf, interval = 0.1),
+    list(s=15, cost=NA, catch=15, s_interval=c(10,21), cost_interval=NA, catch_interval=c(10,21))
+  ))
+})
+
+test_that("optimise_s_prevalence() hits max_s when determining cost floor/ceiling", {
+  expect_warning(
+    optimise_s_prevalence(prevalence = 0.01, cost_unit = 5, cost_pool = Inf, max_s = 50),
+    'Maximum cost effectivness is achieved at or above the maximum size of pools allowed. Consider increasing max_s')
+})
+
+test_that("optimise_s_prevalence() when cost(max_s) < max_cost", {
+  expect_warning(
+    optimise_s_prevalence(prevalence = 0.1, cost_pool = 1, cost_unit = 0.5, max_s = 6, interval = 0.1),
+    'A pool size greater than max_s may fall within the specified range of cost effectiveness. Consider increasing max_s')
+})
+
 test_that("optimise_s_prevalence() extremely bad integrand behaviour", {
+  # This takes a few seconds to run
   expect_error(
     optimise_s_prevalence(
       prevalence = 0.2, cost_unit = 1, cost_pool = 200,
diff --git a/tests/testthat/test-util.R b/tests/testthat/test-util.R
new file mode 100644
index 0000000..d359ffe
--- /dev/null
+++ b/tests/testthat/test-util.R
@@ -0,0 +1,11 @@
+# As util helper functions will be used to catch warnings soon
+test_that("interval is >= 0", {
+  expect_error(
+    optimise_s_prevalence(prevalence = 0.01, cost_unit = 5, cost_pool = 10, interval = -1),
+    'interval must be 0 or higher')
+  expect_equal(
+    optimise_s_prevalence(prevalence = 0.1, cost_unit = 5, cost_pool = 10, interval = 2),
+    list(s=5, cost=0.786439, catch =5, s_interval=c(1,25),
+         cost_interval=c(1.35, 2.262155), catch_interval=c(1,25)),
+    tolerance = 1e-7)
+})