diff --git a/R/machinelearning-functions-tagm-mcmc-helper.R b/R/machinelearning-functions-tagm-mcmc-helper.R
index 391ca527..f29831f2 100644
--- a/R/machinelearning-functions-tagm-mcmc-helper.R
+++ b/R/machinelearning-functions-tagm-mcmc-helper.R
@@ -1,8 +1,38 @@
+
+# Helper function to get number of outlier at each iteration of MCMC
 mcmc_get_outliers <- function(x) {
     stopifnot(inherits(x, "MCMCParams"))
     lapply(x@chains@chains, function(mc) coda::mcmc(colSums(mc@Outlier)))
 }
 
+# Helper function to get mean component allocation at each iteration of MCMC
+
+mcmc_get_meanComponent <- function(x) {
+  stopifnot(inherits(x, "MCMCParams"))
+  lapply(x@chains@chains, function(mc) coda::mcmc(colMeans(mc@Component)))
+}
+
+# Helper function to get mean probability of belonging to outlier at each iteration
+
+mcmc_get_meanoutliersProb <- function(x) {
+  stopifnot(inherits(x, "MCMCParams"))
+  lapply(x@chains@chains, function(mc) coda::mcmc(colMeans(mc@OutlierProb[, ,2])))
+}
+
+# Wrapper for the geweke diagnostics from coda package also return p-values
+
+geweke_test <- function(x) {
+  res <- matrix(NA, nrow = 2, ncol = length(x))
+  gwk <- sapply(x, coda::geweke.diag, simplify = TRUE)
+  res[1, ] <- unlist(gwk[1, ])
+  res[2, ] <- pnorm(abs(unlist(gwk[1, ])), lower.tail=FALSE) * 2
+  colnames(res) <- paste0("chain ", seq.int(x))
+  rownames(res) <- c("z.value", "p.value")
+  return(res)
+}
+
+# Helper function to pool chains together after processing
+
 mcmc_pool_chains <- function(param) {
   stopifnot(inherits(param, "MCMCParams"))
   
@@ -76,9 +106,11 @@ mcmc_pool_chains <- function(param) {
   
 }
 
+# Helper function to burn n iterations from the front of the chains
+
 mcmc_burn_chains <- function(x, n) {
     stopifnot(inherits(x, "MCMCParams"))
-    .chain <- pRoloc:::chains(newTanMcmc)[[1]]
+    .chain <- pRoloc:::chains(x)[[1]]
     K <- .chain@K # Number of components
     N <- .chain@N # Number of Proteins
     chainlist <-
@@ -109,85 +141,49 @@ mcmc_burn_chains <- function(x, n) {
                          summary = pRoloc:::.MCMCSummary())
 }
 
-mcmc_pool_chains <- function(param) {
-  stopifnot(inherits(param, "MCMCParams"))
-  
-  param1 <- pRoloc:::chains(param)[[1]]
-  
-  n <- param1@n
-  nPool <- param1@n * length(param) # total number of iteration increase
-  KPool <- param1@K  # number of components unchanged
-  NPool <- param1@N # number of proteins doesn't change
-  numChains <- length(param)
-  
-  pooled.Component <- matrix(0, nrow = NPool, ncol = nPool)
-  pooled.ComponentProb <- array(0, c(NPool, nPool, KPool ))
-  pooled.Outlier <- matrix(0, nrow = NPool, ncol = nPool)
-  pooled.OutlierProb <- array(0, c(NPool, nPool, 2 ))
-  
-  rownames(pooled.Component) <- rownames(param1@Component)
-  rownames(pooled.ComponentProb) <- rownames(param1@ComponentProb)
-  rownames(pooled.Outlier) <- rownames(param1@Outlier)
-  rownames(pooled.OutlierProb) <- rownames(param1@OutlierProb)
-  
-  
-  # Calculate basic quantities
-  for (j in seq_len(numChains)) {
-    
-    mc <- pRoloc:::chains(param)[[j]]
-    ## Pool chains
-    pooled.Component[, n * (j - 1) + seq.int(n)] <- mc@Component
-    pooled.ComponentProb[, n * (j - 1) + seq.int(n), ] <- mc@ComponentProb
-    pooled.Outlier[, n * (j - 1)+ seq.int(n)] <- mc@Outlier
-    pooled.OutlierProb[, n * (j - 1) + seq.int(n), ] <- mc@OutlierProb
-    
-  }
-  
-  mk.list <- lapply(param@chains@chains,function(x) x@ComponentParam@mk)
-  lambdak.list <- lapply(param@chains@chains,function(x) x@ComponentParam@lambdak)
-  nuk.list <- lapply(param@chains@chains,function(x) x@ComponentParam@nuk)
-  sk.list <- lapply(param@chains@chains,function(x) x@ComponentParam@sk)
-  
-  ## save Component parameters
-  .ComponentParam <- pRoloc:::.ComponentParam(K = KPool, D = param1@ComponentParam@D,
-                                              mk = Reduce("+", mk.list) / length(mk.list),
-                                              lambdak = Reduce("+", lambdak.list) / length(lambdak.list),
-                                              nuk = Reduce("+", nuk.list) / length(nuk.list),
-                                              sk = Reduce("+", sk.list) / length(sk.list))
-  ## apply thinning and burn-in
-  .Component <- pooled.Component
-  .ComponentProb <- pooled.ComponentProb
-  .Outlier <- pooled.Outlier
-  .OutlierProb <- pooled.OutlierProb
-  
-  ## make MCMCChain object
-  .MCMCChain <- pRoloc:::.MCMCChain(n = nPool,
-                                    K = KPool,
-                                    N = NPool,
-                                    Component = .Component,
-                                    ComponentProb = .ComponentProb,
-                                    Outlier = .Outlier,
-                                    OutlierProb = .OutlierProb,
-                                    ComponentParam = .ComponentParam)
-  
-  ## Make MCMCChains with single object
-  .MCMCChains <- pRoloc:::.MCMCChains(chains = list(.MCMCChain))
-  
-  ## Make MCMCParams object
+# Helper function to subsample the chains, known informally as thinning.
+
+mcmc_thin_chains <- function(x, freq) {
+  stopifnot(inherits(x, "MCMCParams"))
+  .chain <- pRoloc:::chains(x)[[1]]
+  K <- .chain@K # Number of components
+  N <- .chain@N # Number of Proteins
+  nThin <- floor(.chain@n/freq) # Number of iterations after thinning
+  chainlist <-
+    lapply(x@chains@chains, function(chain) {
+      .ComponentParam <- chain@ComponentParam
+      ## Subset MCMC iterations
+      retain <- freq * seq.int(1:nThin) ## samples to retain
+      ## Check correct number of iterations
+      stopifnot(ncol(chain@Component[, retain]) == nThin)
+      .Component <- chain@Component[, retain]
+      .ComponentProb <- chain@ComponentProb[, retain, ]
+      .Outlier <- chain@Outlier[, retain]
+      .OutlierProb <- chain@OutlierProb[, retain, ]
+      pRoloc:::.MCMCChain(n = as.integer(nThin),
+                          K = K,
+                          N = N,
+                          Component = .Component,
+                          ComponentProb = .ComponentProb,
+                          Outlier = .Outlier,
+                          OutlierProb = .OutlierProb,
+                          ComponentParam = .ComponentParam)
+    })
+  
+  mcmc_chainlist <- pRoloc:::.MCMCChains(chains = chainlist)
   pRoloc:::.MCMCParams(method = "TAGM.MCMC",
-                       chains = .MCMCChains,
+                       chains = mcmc_chainlist,
                        priors = param@priors,
                        summary = pRoloc:::.MCMCSummary())
-  
 }
 
-
-
+# Plotting method for violins
 
 setMethod("plot", c("MCMCParams", "character"),
           function(x, y, ...) {
               mcmc_plot_probs(x, y, n = 1)
           })
+# Plotting function for violins using ggplot2.
 
 mcmc_plot_probs <- function(param, fname, n = 1) {
     stopifnot(inherits(param, "MCMCParams"))