diff --git a/.gitignore b/.gitignore
index b71e700..b191ef8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -34,9 +34,4 @@ vignettes/*.RData
 inst/shiny/rsconnect
 
 # inst/shiny/www/..
-inst/shiny/www/report_full.Rmd
-inst/shiny/www/report_full.html
-inst/shiny/www/report_full_purl.R
-inst/shiny/www/report_meta.Rmd
-inst/shiny/www/report_meta.html
-inst/shiny/www/report_meta_purl.R
+inst/shiny/www/report_expanded*
diff --git a/inst/shiny/global.R b/inst/shiny/global.R
index 775832e..a5fdda3 100644
--- a/inst/shiny/global.R
+++ b/inst/shiny/global.R
@@ -1,46 +1,10 @@
-# Function for overwriting the param key of the report YAML
-overwrite_params <- function(file, params) {
-  # Load file into memory
-  .r <- readLines(file)
-  params_lines_start <- which(.r == "#' params:") # Assumes a line on its own
-  params_lines_end <- max(which(.r == "#' ---")) # Assumes 'params:' block is the last element
-
-  #Create YAML
-  tmpfile <- tempfile() # Temp file to hold dput output
-  params_yaml <- character()
-
-  for (i in seq_along(params)) {
-    dput(params[[i]], tmpfile)
-    params_yaml <-
-      c(params_yaml,
-        sprintf("#'   %s: !r %s", names(params)[i],
-                paste0(readLines(tmpfile), collapse = "")))
-  }
-
-  # Insert into character vector
-  new_file <- c(.r[seq_len(params_lines_start - 1)],
-                params_yaml,
-                .r[params_lines_end:length(.r)])
-
-  writeLines(text = new_file, con = file)
+# Function for dput'ting objects into a character string
+cput <- function(x) {
+  f <- tempfile()
+  dput(x, file = f)
+  return(readLines(f))
 }
 
-# Testing function
-# params <- list(file = "../../../data/freshVsFrozen.csv",
-#                header = TRUE, sep = ";",
-#                quote = "\"",
-#                model_cols = c("PreVsPost.Fresh.pval", "PreVsPost.Frozen.pval"),
-#                meta_large_vals = FALSE,
-#                init_par = c(pie1 = 0.7, mu = 1, sigma = 1, rho = 0.5),
-#                meta_method = "NM",
-#                meta_max_ite = 50L,
-#                meta_positive_rho = TRUE,
-#                meta_IDR_thres_type = "IDR",
-#                meta_IDR_thres = 0.05,
-#                theta = GMCM::rtheta())
-# overwrite_params(file = "inst/shiny/www/CopyOfreport_meta.R", params)
-
-
 # Function for plotting meta results
 meta_plot <- function(fit, # A fitted object data
                       idr, # Output from get.IDR
diff --git a/inst/shiny/run-reports.R b/inst/shiny/run-reports.R
index 2f641dd..c17fa72 100644
--- a/inst/shiny/run-reports.R
+++ b/inst/shiny/run-reports.R
@@ -2,14 +2,19 @@ library("knitr")
 library("rmarkdown")
 library("GMCM")
 
-# GENERAL GMCM ----
-report_path <- "inst/shiny/www/report_full.Rmd"
+# dput object to character string
+cput <- function(x) {
+  f <- tempfile()
+  dput(x, file = f)
+  return(readLines(f))
+}
+
 
 # SPECIAL GMCM ----
 report_path <- "inst/shiny/www/report_meta.Rmd"
 
 # Parameters to expand
-params <- list(input_file = "../../../data/u133VsExon.csv",
+params <- list(data_file = "../../../data/u133VsExon.csv",
                header = TRUE,
                sep = ";",
                quote = '\"',
@@ -22,13 +27,6 @@ params <- list(input_file = "../../../data/u133VsExon.csv",
                meta_IDR_thres_type = "IDR",
                meta_IDR_thres = 1e-04)
 
-# dput object to character string
-cput <- function(x) {
-  f <- tempfile()
-  dput(x, file = f)
-  return(readLines(f))
-}
-
 
 expand_args <- c(list(file = report_path), params)
 report_expanded <- do.call(knitr::knit_expand, expand_args)
@@ -36,6 +34,10 @@ report_expanded_path <- gsub("/report_", "/report_expanded_", report_path)
 
 cat(report_expanded, file = report_expanded_path)
 
+# We can also purl
+knitr::purl(report_expanded_path, output = gsub(".Rmd$", ".R", report_expanded_path),
+            documentation = 0)
+
 # Render the expanded document
 rmarkdown::render(
   input = report_expanded_path,
@@ -44,8 +46,40 @@ rmarkdown::render(
   envir = new.env(parent = globalenv())
 )
 
-# We can also purl
+
+
+
+# GENERAL GMCM ----
+report_path <- "inst/shiny/www/report_full.Rmd"
+
+# Parameters to expand
+params <- list(data_file = "../../../data/u133VsExon.csv",
+               header = TRUE,
+               sep = ";",
+               quote = '\"',
+               model_cols = c("u133", "exon"),
+               theta = GMCM::rtheta(m = 2, d = 2),
+               fit_method = "NM",
+               max_ite = 50,
+               full_class_type = "thres_prob",
+               full_thres_prob = 0.9)
+
+expand_args <- c(list(file = report_path), params)
+report_expanded <- do.call(knitr::knit_expand, expand_args)
+report_expanded_path <- gsub("/report_", "/report_expanded_", report_path)
+
+cat(report_expanded, file = report_expanded_path)
+
+# We can purl
 knitr::purl(report_expanded_path, output = gsub(".Rmd$", ".R", report_expanded_path),
             documentation = 0)
 
+# Render the expanded document
+rmarkdown::render(
+  input = report_expanded_path,
+  output_options = list(self_contained = TRUE),
+  params = params,
+  envir = new.env(parent = globalenv())
+)
+
 
diff --git a/inst/shiny/server.R b/inst/shiny/server.R
index 5c629cd..df015b9 100644
--- a/inst/shiny/server.R
+++ b/inst/shiny/server.R
@@ -966,57 +966,73 @@ shinyServer(function(input, output, session) {
   # Output reports --------
   # https://shiny.rstudio.com/articles/generating-reports.html
 
+  full_expand_rmd <- reactive({
+    # Copy the report file to a temporary directory before processing it, in
+    # case we don't have write permissions to the current working dir (which
+    # can happen when deployed).
+    temp_file <- file.path(tempdir(),  "report_full.Rmd")
+    file.copy("www/report_full.Rmd", temp_file, overwrite = TRUE)
+    message("Copied 'www/report_full.Rmd' into ", temp_file)
+
+
+    # Set up parameters to pass to Rmd document
+    params <- list(data_file = input$in_file$datapath,
+                   header = input$header,
+                   sep = input$sep,
+                   quote = input$quote,
+                   model_cols = input$model_cols,
+                   theta = as.theta(full_start_theta()),
+                   fit_method = input$full_method,
+                   max_ite = input$full_max_ite,
+                   full_class_type = input$full_class_type,
+                   full_thres_prob = input$full_thres_prob)
+
+    # Expand the args in params
+    knit_expand_args <- c(list(file = temp_file), params)
+    report_expanded <- do.call(knitr::knit_expand, knit_expand_args)
+    message(temp_file, " expanded with parameters.")
+
+    # Write the R file and path to it
+    temp_file_out <- gsub("report_", "report_expanded_", temp_file)
+    cat(report_expanded, file = temp_file_out)
+    message("Expanded .Rmd written to ", temp_file_out)
+
+    return(temp_file_out)
+
+  })
+
   output$full_dl_r <- downloadHandler(
     filename = "report_full.R",
     content = function(file) {
-      file.copy("www/report_full.R", file, overwrite = TRUE)
+      expanded_rmd <- full_expand_rmd()
+      outfile <- gsub(".Rmd$", ".R", expanded_rmd)
+      message("purl ", expanded_rmd, " to ", outfile)
+      knitr::purl(expanded_rmd,
+                  output = outfile,
+                  documentation = 0)
+
+      file.copy(outfile, file, overwrite = TRUE)
     }
   )
 
   output$full_dl_rmd <- downloadHandler(
     filename = "report_full.Rmd",
     content = function(file) {
-      temp_file <- file.path(tempdir(), "report_full.R")
-      file.copy("www/report_full.R", temp_file, overwrite = TRUE)
-      out <- spin(hair = temp_file,
-                  knit = FALSE,
-                  format = "Rmd")
-      writeLines(text = readLines(out), con = file)
+      file.copy(full_expand_rmd(), file, overwrite = TRUE)
     }
   )
 
   output$full_dl_html <- downloadHandler(
     filename = "report_full.html",
     content = function(file) {
-      # Copy the report file to a temporary directory before processing it, in
-      # case we don't have write permissions to the current working dir (which
-      # can happen when deployed).
-      temp_file <- file.path(tempdir(), "report_full.R")
-      file.copy("www/report_full.R", temp_file, overwrite = TRUE)
-
-      # Set up parameters to pass to Rmd document
-      params <- list(file = input$in_file$datapath,
-                     header = input$header,
-                     sep = input$sep,
-                     quote = input$quote,
-                     model_cols = input$model_cols,
-                     theta = as.theta(full_start_theta()),
-                     fit_method = input$full_method,
-                     max_ite = input$full_max_ite,
-                     full_class_type = input$full_class_type,
-                     full_thres_prob = input$full_thres_prob)
-      print(str(params))
-
-      # Spin and knit the document, passing in the `params` list, and eval it in a
-      # child of the global environment (this isolates the code in the document
-      # from the code in this app).
+      # Render the expanded Rmd document
       rmarkdown::render(
-        input = temp_file,
+        input = full_expand_rmd(),
         output_file = file,
         output_options = list(self_contained = TRUE),
-        params = params,
         envir = new.env(parent = globalenv())
       )
+      message("Expanded .Rmd rendered.")
     }
   )
 
@@ -1024,10 +1040,6 @@ shinyServer(function(input, output, session) {
 
 
 
-
-
-
-
   # __SPECIAL GMCM ________________________________________________________ ----
 
   # Initalise reactive values ----
@@ -1292,61 +1304,78 @@ shinyServer(function(input, output, session) {
   # Output reports --------
   # https://shiny.rstudio.com/articles/generating-reports.html
 
+
+  meta_expand_rmd <- reactive({
+    # Copy the report file to a temporary directory before processing it, in
+    # case we don't have write permissions to the current working dir (which
+    # can happen when deployed).
+
+    temp_file <- file.path(tempdir(),  "report_meta.Rmd")
+    file.copy("www/report_meta.Rmd", temp_file, overwrite = TRUE)
+    message("Copied 'www/report_meta.Rmd' into ", temp_file)
+
+    # Set up parameters to pass to Rmd document
+    params <- list(data_file = input$in_file$datapath,
+                   header = input$header,
+                   sep = input$sep,
+                   quote = input$quote,
+                   model_cols = input$model_cols,
+                   meta_large_vals = input$meta_large_vals,
+                   init_par = c(pie1  = input$par1,
+                                mu    = input$par2,
+                                sigma = input$par3,
+                                rho   = input$par4),
+                   meta_method = input$meta_method,
+                   meta_max_ite = input$meta_max_ite,
+                   meta_positive_rho = input$meta_positive_rho,
+                   meta_IDR_thres_type = input$meta_IDR_thres_type,
+                   meta_IDR_thres = input$meta_IDR_thres)
+
+    # Expand the args in params
+    knit_expand_args <- c(list(file = temp_file), params)
+    report_expanded <- do.call(knitr::knit_expand, knit_expand_args)
+    message(temp_file, " expanded with parameters.")
+
+    # Write the R file and path to it
+    temp_file_out <- gsub("report_", "report_expanded_", temp_file)
+    cat(report_expanded, file = temp_file_out)
+    message("Expanded .Rmd written to ", temp_file_out)
+
+    return(temp_file_out)
+  })
+
   output$meta_dl_r <- downloadHandler(
     filename = "report_meta.R",
     content = function(file) {
-      file.copy("www/report_meta.R", file, overwrite = TRUE)
+      expanded_rmd <- meta_expand_rmd()
+      outfile <- gsub(".Rmd$", ".R", expanded_rmd)
+      message("purl ", expanded_rmd, " to ", outfile)
+      knitr::purl(expanded_rmd,
+                  output = outfile,
+                  documentation = 0)
+
+      file.copy(outfile, file, overwrite = TRUE)
     }
   )
 
   output$meta_dl_rmd <- downloadHandler(
     filename = "report_meta.Rmd",
     content = function(file) {
-      temp_file <- file.path(tempdir(), "report_meta.R")
-      file.copy("www/report_meta.R", temp_file, overwrite = TRUE)
-      out <- spin(hair = temp_file,
-                  knit = FALSE,
-                  format = "Rmd")
-      writeLines(text = readLines(out), con = file)
+      file.copy(meta_expand_rmd(), file, overwrite = TRUE)
     }
   )
 
   output$meta_dl_html <- downloadHandler(
     filename = "report_meta.html",
     content = function(file) {
-      # Copy the report file to a temporary directory before processing it, in
-      # case we don't have write permissions to the current working dir (which
-      # can happen when deployed).
-      temp_file <- file.path(tempdir(), "report_meta.R")
-      file.copy("www/report_meta.R", temp_file, overwrite = TRUE)
-
-      # Set up parameters to pass to Rmd document
-      params <- list(file = input$in_file$datapath,
-                     header = input$header,
-                     sep = input$sep,
-                     quote = input$quote,
-                     model_cols = input$model_cols,
-                     meta_large_vals = input$meta_large_vals,
-                     init_par = c(pie1  = input$par1,
-                                  mu    = input$par2,
-                                  sigma = input$par3,
-                                  rho   = input$par4),
-                     meta_method = input$meta_method,
-                     meta_max_ite = input$meta_max_ite,
-                     meta_positive_rho = input$meta_positive_rho,
-                     meta_IDR_thres_type = input$meta_IDR_thres_type,
-                     meta_IDR_thres = input$meta_IDR_thres)
-
-      # Spin and knit the document, passing in the `params` list, and eval it in a
-      # child of the global environment (this isolates the code in the document
-      # from the code in this app).
+      # Render the expanded Rmd document
       rmarkdown::render(
-        input = temp_file,
+        input = meta_expand_rmd(),
         output_file = file,
         output_options = list(self_contained = TRUE),
-        params = params,
         envir = new.env(parent = globalenv())
       )
+      message("Expanded .Rmd rendered.")
     }
   )
 
diff --git a/inst/shiny/www/report_full.R b/inst/shiny/www/report_full.R
deleted file mode 100644
index a099f19..0000000
--- a/inst/shiny/www/report_full.R
+++ /dev/null
@@ -1,128 +0,0 @@
-#' ---
-#' title: "Unsupervised clustering with general GMCMs"
-#' output: html_document
-#' date: '`r Sys.time()`'
-#' author: "Generated by the GMCM shiny app"
-#' params:
-#'   file: !r file.path(getwd(), "../../../data/freshVsFrozen.csv")
-#'   header: TRUE
-#'   sep: ";"
-#'   quote: '"'
-#'   model_cols: !r c("PreVsPost.Fresh.pval", "PreVsPost.Frozen.pval")
-#'   theta: !r GMCM::rtheta(m = 2, d = 2)
-#'   fit_method: "NM"
-#'   max_ite: 50
-#'   full_class_type: "thres_prob"
-#'   full_thres_prob: 0.9
-#' ---
-
-
-# ---- knit-int, echo=FALSE, include=FALSE
-set.seed(7869670)
-
-#'
-#' ## Initalisation
-#' The **GMCM**^[1][1]^ package is loaded.
-# ---- load-packages, include=TRUE
-
-#install.packages("GMCM")  # Uncomment to install the GMCM package
-library("GMCM")
-
-#'
-#' If **GMCM** is *not* installed, please uncomment the above line and rerun the script.
-#'
-#' ## Load data
-#' The data is loaded and the first rows are printed
-# ---- load-data, include=TRUE, echo=TRUE
-ds <- read.table(file   = params$file,
-                 header = params$header,
-                 sep    = params$sep,
-                 quote  = params$quote)
-head(ds, n = 4)
-
-#'
-#' Next, the data is subset to the columns of interest.
-# ---- select-data, include=TRUE, echo=TRUE
-x <- ds[, params$model_cols]
-head(x, n = 2)
-
-#'
-#' ## Initial parameters
-#' The initial parameters, as chosen in the application, are given by
-# ---- show-initial-params, include=TRUE, echo=TRUE
-theta <- as.theta(params$theta)
-print(theta)
-
-#'
-#' ## Model fitting
-#' With the data loaded and defined initial parameters, the model is now fitted.
-# ---- fit_model, error=TRUE
-theta <- fit.full.GMCM(u = x,  # Ranking function is applied automatically
-                       theta = theta,
-                       method = params$fit_method,
-                       max.ite = params$max_ite,
-                       verbose = FALSE)
-print(theta)
-
-#'
-#' The fitting method is set `r params$fit_method` with a maximum number of iterations of `r params$max_ite`.
-#'
-#' ## Unsupervised clustering
-#' The estimated parameters are used to calculated posterior component probabilities on which the classification is based:
-# ---- compute_probs
-kappa <- get.prob(x, theta) # Compute component probabilities
-colnames(kappa) <- paste0("comp", seq_len(ncol(kappa))) # Add names
-
-comps <- apply(kappa, 1, which.max) # Find index of maximum entry for each row
-
-if (params$full_class_type == "thres_prob") {
-  ok_max <- apply(kappa, 1, max) > params$full_thres_prob
-  comps[!ok_max] <- NA
-}
-
-cols <- topo.colors(ncol(kappa))[comps]
-cols[is.na(comps)] <- "gray"
-res <- data.frame(kappa, comp = comps, col = cols,
-                  stringsAsFactors = FALSE)
-head(res)
-summary(res)
-
-#'
-#' ## Results
-#' The classes are counted by
-# ---- classes_table
-table(res$comp)
-
-#' The results are also displayed by plotting
-# ---- plot_results
-plot(x, col = res$col, asp = 1) # Plot of raw values
-plot(Uhat(x), col = res$col, asp = 1) # Plot of copula values
-z <- GMCM:::qgmm.marginal(Uhat(x), theta = theta) # Estimate latent process
-plot(z, col = res$col, asp = 1) # Plot of estimated latent process
-
-
-#' The fitted `theta` object can also be plotted directly:
-# ---- plot_theta
-plot(theta)
-
-#'
-#' ### Session information
-#' This report was generated using **rmarkdown**^[2][2]^ and **knitr**^[3][3]^ under the session
-#' given below. The report utilizes [parameterized reports][2] and [`knitr::spin`][3].
-#'
-# ---- session-info
-sessionInfo()
-
-#'
-#' ### References
-#' Please cite the **GMCM** paper^[1][1]^ if you use the package or shiny app.
-# ---- citation, echo=FALSE, results='asis'
-cites <- lapply(c("GMCM", "knitr", "rmarkdown"), citation)
-fmt_cites <- unlist(lapply(cites, format, style = "text"))
-cat(paste0("  ", seq_along(fmt_cites), ". ", fmt_cites, "\n"))
-
-
-
-#' [1]: http://doi.org/10.18637/jss.v070.i02
-#' [2]: https://bookdown.org/yihui/rmarkdown/parameterized-reports.html
-#' [3]: https://yihui.name/knitr/demo/stitch/
diff --git a/inst/shiny/www/report_full.Rmd b/inst/shiny/www/report_full.Rmd
new file mode 100644
index 0000000..f583e6c
--- /dev/null
+++ b/inst/shiny/www/report_full.Rmd
@@ -0,0 +1,135 @@
+---
+title: "Unsupervised clustering with general GMCMs"
+output: html_document
+date: '`r Sys.time()`'
+author: "Generated by the GMCM shiny app"
+---
+
+```{r knit-int, echo=FALSE, include=FALSE}
+set.seed(7869670)
+```
+
+
+## Initalisation
+The **GMCM**^[1][1]^ package is loaded.
+
+```{r load-packages, include=TRUE}
+
+#install.packages("GMCM")  # Uncomment to install the GMCM package
+library("GMCM")
+```
+
+
+If **GMCM** is *not* installed, please uncomment the above line and rerun the script.
+
+## Load data
+The data is loaded and the first rows are printed
+
+```{r load-data, include=TRUE, echo=TRUE}
+ds <- read.table(file   = "{{gsub("\\", "/", data_file, fixed = TRUE)}}",
+                 header = {{header}},
+                 sep    = "{{sep}}",
+                 quote  = "\{{quote}}")
+head(ds, n = 4)
+```
+
+
+Next, the data is subset to the columns of interest.
+
+```{r select-data, include=TRUE, echo=TRUE}
+x <- ds[, {{cput(model_cols)}}]
+head(x, n = 2)
+```
+
+
+## Initial parameters
+The initial parameters, as chosen in the application, are given by
+
+```{r show-initial-params, include=TRUE, echo=TRUE}
+theta <- as.theta({{cput(theta)}})
+print(theta)
+```
+
+
+## Model fitting
+With the data loaded and defined initial parameters, the model is now fitted.
+
+```{r fit_model, error=TRUE}
+theta <- fit.full.GMCM(u = x,  # Ranking function is applied automatically
+                       theta = theta,
+                       method = "{{fit_method}}",
+                       max.ite = {{max_ite}},
+                       verbose = FALSE)
+print(theta)
+```
+
+The fitting method is set to `"{{fit_method}}"` with a maximum number of iterations of `{{max_ite}}`.
+
+
+## Unsupervised clustering
+The estimated parameters are used to calculated posterior component probabilities on which the classification is based:
+
+```{r compute_probs}
+kappa <- get.prob(x, theta) # Compute component probabilities
+colnames(kappa) <- paste0("comp", seq_len(ncol(kappa))) # Add names
+
+comps <- apply(kappa, 1, which.max) # Find index of maximum entry for each row
+
+{{ifelse(full_class_type == "thres_prob", "# Set to NA if probability is not sufficently large", "")}}
+{{ifelse(full_class_type == "thres_prob", sprintf("ok_max <- apply(kappa, 1, max) > %.3f", full_thres_prob), "")}}
+{{ifelse(full_class_type == "thres_prob", "comps[!ok_max] <- NA", "")}}
+
+cols <- topo.colors(ncol(kappa))[comps]
+cols[is.na(comps)] <- "gray"
+res <- data.frame(kappa, comp = comps, col = cols,
+                  stringsAsFactors = FALSE)
+head(res)
+summary(res)
+```
+
+
+## Results
+The classes are counted by
+
+```{r classes_table}
+table(res$comp)
+```
+
+The results are also displayed by plotting
+
+```{r plot_results}
+plot(x, col = res$col, asp = 1) # Plot of raw values
+plot(Uhat(x), col = res$col, asp = 1) # Plot of copula values
+z <- GMCM:::qgmm.marginal(Uhat(x), theta = theta) # Estimate latent process
+plot(z, col = res$col, asp = 1) # Plot of estimated latent process
+```
+
+The fitted `theta` object can also be plotted directly:
+
+```{r plot_theta}
+plot(theta)
+```
+
+
+### Session information
+This report was generated using **rmarkdown**^[2][2]^ and **knitr**^[3][3]^ under the session
+given below. The report utilizes [parameterized reports][2] and [`knitr::spin`][3].
+
+
+```{r session-info}
+sessionInfo()
+```
+
+
+### References
+Please cite the **GMCM** paper^[1][1]^ if you use the package or shiny app.
+
+```{r citation, echo=FALSE, results='asis'}
+cites <- lapply(c("GMCM", "knitr", "rmarkdown"), citation)
+fmt_cites <- unlist(lapply(cites, format, style = "text"))
+cat(paste0("  ", seq_along(fmt_cites), ". ", fmt_cites, "\n"))
+```
+
+[1]: http://doi.org/10.18637/jss.v070.i02
+[2]: https://bookdown.org/yihui/rmarkdown/parameterized-reports.html
+[3]: https://yihui.name/knitr/demo/stitch/
diff --git a/inst/shiny/www/report_meta.Rmd b/inst/shiny/www/report_meta.Rmd
index e758cab..996ff94 100644
--- a/inst/shiny/www/report_meta.Rmd
+++ b/inst/shiny/www/report_meta.Rmd
@@ -4,20 +4,6 @@ output: html_document
 date: '`r Sys.time()`'
 author: "Generated by the GMCM shiny app"
 ---
-<!-- Old YAML params -->
-<!-- params: -->
-<!--   file: !r file.path(getwd(), "../../../data/freshVsFrozen.csv") -->
-<!--   header: TRUE -->
-<!--   sep: ";" -->
-<!--   quote: '"' -->
-<!--   model_cols: !r c("PreVsPost.Fresh.pval", "PreVsPost.Frozen.pval") -->
-<!--   meta_large_vals: FALSE -->
-<!--   init_par: !r c(pie1 = 0.5, mu = 1, sigma = 1, rho = 0.5) -->
-<!--   meta_method: "NM" -->
-<!--   meta_max_ite: 50 -->
-<!--   meta_positive_rho: TRUE -->
-<!--   meta_IDR_thres_type: "IDR" -->
-<!--   meta_IDR_thres: 0.05 -->
 
 ```{r knit-int, echo=FALSE, include=FALSE}
 set.seed(5828993)
@@ -40,13 +26,14 @@ If **GMCM** is *not* installed, please uncomment the above line and rerun the sc
 The data is loaded and the first rows are printed
 
 ```{r load-data, include=TRUE, echo=TRUE}
-ds <- read.table(file   = "{{input_file}}",
+ds <- read.table(file   = "{{gsub("\\", "/", data_file, fixed = TRUE)}}",
                  header = {{header}},
                  sep    = "{{sep}}",
                  quote  = "\{{quote}}")
 head(ds, n = 4)
 ```
 
+The reproduce the output, change the file path above to the data file of interest.
 
 Next, the data is subset to the columns of interest.