diff --git a/chapters/evaluation_data_input.qmd b/chapters/evaluation_data_input.qmd
index c361abf..9439b40 100644
--- a/chapters/evaluation_data_input.qmd
+++ b/chapters/evaluation_data_input.qmd
@@ -15,7 +15,7 @@ library(formattable)
 
 # others ----
 library(here)
-library(aggreCAT)
+# library(aggreCAT)
 library(DescTools)
 select <- dplyr::select 
 
diff --git a/code/DistAggModified.R b/code/DistAggModified.R
index 6ef7458..1c2e0c9 100644
--- a/code/DistAggModified.R
+++ b/code/DistAggModified.R
@@ -1,124 +1,138 @@
-# aggreCAT::DistributionWAgg Modified to also include 90% upper and lower bounds of the aggregated distribution
+
+# aggreCAT::DistributionWAgg modified to also include 90% upper and lower bounds 
+# of the aggregated distribution.
+# preprocess_judgements() is taken from aggreCAT with some modifications
+
+preprocess_judgements <- function(expert_judgements,
+  round_2_filter = TRUE,
+  three_point_filter = TRUE,
+  percent_toggle = FALSE){
+
+  if(any(is.na(expert_judgements$value))) stop("NAs Found in Values")
+
+  # Variables of focus
+  expert_judgements <- expert_judgements %>%
+    dplyr::select(round,
+      paper_id,
+      user_name,
+      element,
+      value)
+
+  filter_round <- function(expert_judgements, round_2_filter){
+    output_df <-  if(isTRUE(round_2_filter)){
+      expert_judgements %>%
+        dplyr::filter(round %in% "round_2")
+    } else {
+      expert_judgements
+    }
+  }
+
+  filter_element <- function(expert_judgements, three_point_filter){
+    output_df <- if(isTRUE(three_point_filter)){
+      expert_judgements %>%
+        dplyr::group_by(round, paper_id, user_name) %>%
+        dplyr::filter(element %in% c("three_point_best",
+          "three_point_lower",
+          "three_point_upper"))
+    } else {
+      expert_judgements %>%
+        dplyr::filter(element != "binary_question")
+    }
+  }
+
+  change_value <- function(expert_judgements, percent_toggle){
+    # Converts values to 0,1
+    output_df <- if(isTRUE(percent_toggle)){
+      expert_judgements %>%
+        dplyr::mutate(value =
+            dplyr::case_when(
+              element %in% c("three_point_best",
+                "three_point_lower",
+                "three_point_upper") ~ value / 100,
+              TRUE ~ value
+            ))
+    } else {
+      expert_judgements
+    }
+
+  }
+
+  check_values <- function(expert_judgements, round_2_filter, three_point_filter, percent_toggle){
+    if(isTRUE(percent_toggle)){
+      if(isTRUE(max(expert_judgements$value) > 1)) {
+        warning("Non probability value outside 0,1 ")
+      }
+    }
+
+    return(expert_judgements)
+  }
+
+  method_out <-  expert_judgements %>%
+    filter_round(round_2_filter) %>%
+    filter_element(three_point_filter) %>%
+    change_value(percent_toggle) %>%
+    dplyr::bind_rows() %>%
+    check_values(round_2_filter, three_point_filter, percent_toggle) %>%
+    dplyr::ungroup()
+
+  return(method_out)
+}
 
 DistributionWAggMOD <- function(expert_judgements,
-         type = "DistribArMean",
          name = NULL,
          placeholder = FALSE,
          percent_toggle = FALSE,
          round_2_filter = TRUE) {
-  
-  if(!(type %in% c("DistribArMean",
-                   "TriDistribArMean"))){
-    
-    stop('`type` must be one of "DistribArMean" or "TriDistribArMean"')
-    
-  }
-  
+
   ## Set name argument
   
   name <- ifelse(is.null(name),
-                 type,
+                 "DistributionWAggMOD",
                  name)
-  
-  cli::cli_h1(sprintf("DistributionWAgg: %s",
-                      name))
-  
-  if(isTRUE(placeholder)){
-    
-    method_placeholder(expert_judgements,
-                       name)
-    
-  } else {
-    
+
     df <- expert_judgements %>%
-      preprocess_judgements(percent_toggle = {{percent_toggle}},
-                            round_2_filter = {{round_2_filter}}) %>%
+      preprocess_judgements(percent_toggle = percent_toggle,
+                            round_2_filter = round_2_filter) %>%
       dplyr::group_by(paper_id)
     
-    # create different Fx_fun and avdist_fun based on "type"
-    switch(type,
-           "DistribArMean" = {
-             
-             Fx_fun <- function(x, lower, best, upper) {
-               dplyr::case_when(
-                 x < 0 ~ 0,
-                 x >= 0 & x < lower ~ 0.05 / lower * x,
-                 x >= lower &
-                   x < best ~ 0.45 / (best - lower) * (x - lower) + 0.05,
-                 x >= best &
-                   x < upper ~ 0.45 / (upper - best) * (x - best) + 0.5,
-                 x >= upper &
-                   x < 1 ~ 0.05 / (1 - upper) * (x - upper) + 0.95,
-                 x > 1 ~ 1
-               )
-             }
-             
-             avdist_fun <- function(dq, claim_input) {
-               claim_input %>%
-                 dplyr::mutate(Fx = purrr::pmap(
-                   list(three_point_lower,
-                        three_point_best,
-                        three_point_upper),
-                   .f = function(l, b, u) {
-                     function(x) {
-                       Fx_fun(x,
-                              
-                              lower = l,
-                              best = b,
-                              upper = u)
-                     }
-                   }
-                 ))  %>%
-                 purrr::pluck("Fx") %>%
-                 purrr::map_dbl(
-                   .f = function(Fx_fun) {
-                     Fx_fun(dq)
-                   }
-                 ) %>%
-                 mean()
-             }
-             
-           },
-           "TriDistribArMean" = {
-             
-             Fx_fun <- function(x, lower, best, upper) {
-               dplyr::case_when(
-                 x < lower ~ 0,
-                 x >= lower &
-                   x < best ~ ((x-lower) ^ 2) / ((upper - lower) * (best - lower)),
-                 x >= best &
-                   x < upper ~ 1 - (((upper - x) ^ 2) / ((upper - lower) * (upper - best))),
-                 x >= upper ~ 1
-               )
-             }
-             
-             avdist_fun <- function(dq, claim_input) {
-               claim_input %>%
-                 dplyr::mutate(Fx = purrr::pmap(
-                   list(three_point_lower,
-                        three_point_best,
-                        three_point_upper),
-                   .f = function(l, b, u) {
-                     function(x) {
-                       Fx_fun(x,
-                              
-                              lower = l,
-                              best = b,
-                              upper = u)
-                     }
-                   }
-                 ))  %>%
-                 purrr::pluck("Fx") %>%
-                 purrr::map_dbl(
-                   .f = function(Fx_fun) {
-                     Fx_fun(dq)
-                   }
-                 ) %>%
-                 mean()
-             }
-             
-           })
-
+    Fx_fun <- function(x, lower, best, upper) {
+      dplyr::case_when(
+        x < 0 ~ 0,
+        x >= 0 & x < lower ~ 0.05 / lower * x,
+        x >= lower &
+          x < best ~ 0.45 / (best - lower) * (x - lower) + 0.05,
+        x >= best &
+          x < upper ~ 0.45 / (upper - best) * (x - best) + 0.5,
+        x >= upper &
+          x < 1 ~ 0.05 / (1 - upper) * (x - upper) + 0.95,
+        x > 1 ~ 1
+      )
+    }
+    
+    avdist_fun <- function(dq, claim_input) {
+      claim_input %>%
+        dplyr::mutate(Fx = purrr::pmap(
+          list(three_point_lower,
+               three_point_best,
+               three_point_upper),
+          .f = function(l, b, u) {
+            function(x) {
+              Fx_fun(x,
+                     
+                     lower = l,
+                     best = b,
+                     upper = u)
+            }
+          }
+        ))  %>%
+        purrr::pluck("Fx") %>%
+        purrr::map_dbl(
+          .f = function(Fx_fun) {
+            Fx_fun(dq)
+          }
+        ) %>%
+        mean()
+    }
     agg_judge_df <- df  %>%
       tidyr::pivot_wider(names_from = element, values_from = value) %>%
       dplyr::mutate(
@@ -208,7 +222,9 @@ DistributionWAggMOD <- function(expert_judgements,
     
     x <-  quantiles %>%
       dplyr::left_join(n_experts, by = "paper_id") %>%
-      dplyr::select(paper_id, aggregated_judgement, aggregated_judgement_90ci_lb, aggregated_judgement_90ci_ub, n_experts) %>%
+      dplyr::select(paper_id, aggregated_judgement, 
+                    aggregated_judgement_90ci_lb, aggregated_judgement_90ci_ub, 
+                    n_experts) %>%
       dplyr::ungroup()
     
     x %>%
@@ -226,5 +242,4 @@ DistributionWAggMOD <- function(expert_judgements,
         agg_90ci_lb = aggregated_judgement_90ci_lb,
         agg_90ci_ub = aggregated_judgement_90ci_ub
       )
-  }
 }
diff --git a/renv.lock b/renv.lock
index a9064fa..4ba11ca 100644
--- a/renv.lock
+++ b/renv.lock
@@ -145,42 +145,6 @@
       ],
       "Hash": "999d7960fe5a5fd98267d0b68fd10065"
     },
-    "R2WinBUGS": {
-      "Package": "R2WinBUGS",
-      "Version": "2.1-22.1",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "R",
-        "boot",
-        "coda",
-        "graphics",
-        "stats",
-        "utils"
-      ],
-      "Hash": "5f50ae507977ff05af153a3b76a775a7"
-    },
-    "R2jags": {
-      "Package": "R2jags",
-      "Version": "0.8-5",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "R",
-        "R2WinBUGS",
-        "abind",
-        "coda",
-        "grDevices",
-        "graphics",
-        "methods",
-        "parallel",
-        "rjags",
-        "stats",
-        "stringr",
-        "utils"
-      ],
-      "Hash": "1d783d1b22f80a173631a45b505f3eb8"
-    },
     "R6": {
       "Package": "R6",
       "Version": "2.5.1",
@@ -212,65 +176,6 @@
       ],
       "Hash": "5ea2700d21e038ace58269ecdbeb9ec0"
     },
-    "VGAM": {
-      "Package": "VGAM",
-      "Version": "1.1-11",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "R",
-        "methods",
-        "splines",
-        "stats",
-        "stats4"
-      ],
-      "Hash": "0a347b0c06e87ad505699b7a6290abd2"
-    },
-    "abind": {
-      "Package": "abind",
-      "Version": "1.4-5",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "R",
-        "methods",
-        "utils"
-      ],
-      "Hash": "4f57884290cc75ab22f4af9e9d4ca862"
-    },
-    "aggreCAT": {
-      "Package": "aggreCAT",
-      "Version": "0.0.0.9004",
-      "Source": "GitHub",
-      "RemoteType": "github",
-      "RemoteHost": "api.github.com",
-      "RemoteUsername": "unjournal",
-      "RemoteRepo": "aggreCAT",
-      "RemoteRef": "master",
-      "RemoteSha": "cd221069bfbc624c28dcc9ad040d61db2be76a0c",
-      "Remotes": "softloud/neet, unjournal/rfUtilities@f64894e",
-      "Requirements": [
-        "GoFKernel",
-        "R",
-        "R2jags",
-        "VGAM",
-        "cli",
-        "coda",
-        "crayon",
-        "dplyr",
-        "ggplot2",
-        "insight",
-        "magrittr",
-        "mathjaxr",
-        "precrec",
-        "purrr",
-        "rfUtilities",
-        "stringr",
-        "tibble",
-        "tidyr"
-      ],
-      "Hash": "8375c42308d4931b23a97b17cdcd00c2"
-    },
     "airtabler": {
       "Package": "airtabler",
       "Version": "0.1.6",
@@ -298,16 +203,6 @@
       ],
       "Hash": "cad6cf7f1d5f6e906700b9d3e718c796"
     },
-    "assertthat": {
-      "Package": "assertthat",
-      "Version": "0.2.1",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "tools"
-      ],
-      "Hash": "50c838a310445e954bc13f26f26a6ecf"
-    },
     "backports": {
       "Package": "backports",
       "Version": "1.4.1",
@@ -506,31 +401,6 @@
       ],
       "Hash": "3f038e5ac7f41d4ac41ce658c85e3042"
     },
-    "cluster": {
-      "Package": "cluster",
-      "Version": "2.1.6",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "R",
-        "grDevices",
-        "graphics",
-        "stats",
-        "utils"
-      ],
-      "Hash": "0aaa05204035dc43ea0004b9c76611dd"
-    },
-    "coda": {
-      "Package": "coda",
-      "Version": "0.19-4.1",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "R",
-        "lattice"
-      ],
-      "Hash": "af436915c590afc6fffc3ce3a5be1569"
-    },
     "colorspace": {
       "Package": "colorspace",
       "Version": "2.1-0",
@@ -992,20 +862,6 @@
       ],
       "Hash": "d6db1667059d027da730decdc214b959"
     },
-    "gridExtra": {
-      "Package": "gridExtra",
-      "Version": "2.3",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "grDevices",
-        "graphics",
-        "grid",
-        "gtable",
-        "utils"
-      ],
-      "Hash": "7d7f283939f563670a697165b2cf5560"
-    },
     "gtable": {
       "Package": "gtable",
       "Version": "0.3.5",
@@ -1149,19 +1005,6 @@
       ],
       "Hash": "99df65cfef20e525ed38c3d2577f7190"
     },
-    "insight": {
-      "Package": "insight",
-      "Version": "0.19.10",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "R",
-        "methods",
-        "stats",
-        "utils"
-      ],
-      "Hash": "c15a38c9655cba66f5f5537a14c1bef4"
-    },
     "irr": {
       "Package": "irr",
       "Version": "0.84.1",
@@ -1322,13 +1165,6 @@
       ],
       "Hash": "7ce2733a9826b3aeb1775d56fd305472"
     },
-    "mathjaxr": {
-      "Package": "mathjaxr",
-      "Version": "1.6-0",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Hash": "87da6ccdcee6077a7d5719406bf3ae45"
-    },
     "memoise": {
       "Package": "memoise",
       "Version": "2.0.1",
@@ -1502,26 +1338,6 @@
       ],
       "Hash": "a1ac5c03ad5ad12b9d1597e00e23c3dd"
     },
-    "precrec": {
-      "Package": "precrec",
-      "Version": "0.14.4",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "R",
-        "Rcpp",
-        "assertthat",
-        "data.table",
-        "ggplot2",
-        "graphics",
-        "grid",
-        "gridExtra",
-        "methods",
-        "rlang",
-        "withr"
-      ],
-      "Hash": "e5a70cbe83260613031dfdc9a91496e0"
-    },
     "prettyunits": {
       "Package": "prettyunits",
       "Version": "1.2.0",
@@ -1644,17 +1460,6 @@
       ],
       "Hash": "082e1a198e3329d571f4448ef0ede4bc"
     },
-    "randomForest": {
-      "Package": "randomForest",
-      "Version": "4.7-1.1",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "R",
-        "stats"
-      ],
-      "Hash": "b52825075358b1ebd159e262bf40649d"
-    },
     "rappdirs": {
       "Package": "rappdirs",
       "Version": "0.3.3",
@@ -1752,35 +1557,6 @@
       ],
       "Hash": "1425f91b4d5d9a8f25352c44a3d914ed"
     },
-    "rfUtilities": {
-      "Package": "rfUtilities",
-      "Version": "2.1-4",
-      "Source": "GitHub",
-      "Repository": "CRAN",
-      "RemoteType": "github",
-      "RemoteHost": "api.github.com",
-      "RemoteUsername": "unjournal",
-      "RemoteRepo": "rfUtilities",
-      "RemoteRef": "f64894efe08d569cdb5cd74e625b72f09019caaa",
-      "RemoteSha": "f64894efe08d569cdb5cd74e625b72f09019caaa",
-      "Requirements": [
-        "R",
-        "cluster",
-        "randomForest"
-      ],
-      "Hash": "327e1ced41e7f356564d67d8379d6d48"
-    },
-    "rjags": {
-      "Package": "rjags",
-      "Version": "4-15",
-      "Source": "Repository",
-      "Repository": "CRAN",
-      "Requirements": [
-        "R",
-        "coda"
-      ],
-      "Hash": "dedea2fb969477a705de3b1a04e1aa25"
-    },
     "rlang": {
       "Package": "rlang",
       "Version": "1.1.3",