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diff --git a/README.txt b/README.txt
index 3fe4b55..4b9e9bc 100644
--- a/README.txt
+++ b/README.txt
@@ -3,9 +3,6 @@ R Example Tools
 
 <R_bridge@esri.com>
 
-Dimistry Pavlushko, Mark Janikas, Konstantin Krivoruchko, Shaun Walbridge and Steve Kopp, Esri Inc
-
-
 Sample ArcMap MXD and ArcGIS Pro Project
 
 Add .tbx files
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diff --git a/scripts/brcm.R b/scripts/brcm.R
deleted file mode 100644
index dd0b9e6..0000000
--- a/scripts/brcm.R
+++ /dev/null
@@ -1,160 +0,0 @@
-#
-# Belarus thyroid cancer in children case study
-#
-
-tool_exec <- function(in_params, out_params)
-{
-### handle input gp tool paramaters
-  dataset_path <- in_params[[1]]
-  case_field <- in_params[[2]]
-  pop_field <- in_params[[3]]
-  covar1_field <- in_params[[4]]
-  covar2_field <- in_params[[5]]
-
-  df = regional_bayesian(dataset_path, case_field, pop_field, covar1_field, covar2_field)
-
-### handle gp tool outputs
-  output_dataset_name = out_params[[1]]
-  arc.write(output_dataset_name, df)
-  return(out_params)
-}
-
-#stat analysis with WINBUGS
-regional_bayesian <- function(dataset_path, case_field, pop_field, covar1_field, covar2_field)
-{
-  if (!requireNamespace("sp", quietly = TRUE))
-    install.packages("sp")
-  if (!requireNamespace("spdep", quietly = TRUE))
-    install.packages("spdep")
-  if (!requireNamespace("R2WinBUGS", quietly = TRUE))
-    install.packages("R2WinBUGS")
-  if (!requireNamespace("rgeos", quietly = TRUE))
-    install.packages("rgeos")
-  require(sp)
-  require(spdep)
-
-  message("Loading dataset...")
-  ds <- arc.open(dataset_path)
-  df <- arc.select(ds, fields = list(case_field, pop_field, covar1_field, covar2_field))
-  sp.df <- arc.data2sp(df)
-
-  # convert shapefile data to a list of polygons and a data frame
-  centroids <- rgeos::gCentroid(sp.df, byid = TRUE)
-  centroids <- centroids@coords
-
-  kk <- nrow(sp.df)
-
-  # calculate the expected counts
-  pmap <- probmap(df[[case_field]], df[[pop_field]])
-
-  # make neighbours list
-  message("Making Neighbours...")
-  five.nn <- knn2nb(knearneigh(centroids, k=5), sym=TRUE)
-
-  #plotpolys(th_polys, bbs, border="grey")
-  #plot(five.nn, centroids, add=TRUE)
-
-#####4 "GWR"
-
-  ## data preparation
-  dlist <- nbdists(five.nn, centroids)
-  num <- sapply(five.nn, length)
-
-  ll <- sum(num)
-  # equal Weights:
-  weights <- rep(1, ll)
-
-  adj <- rep(0, ll)
-  mm <- 1
-  for (i in 1:kk)
-  {
-   for (j in 1:num[i])
-   {
-     adj[mm] <- five.nn[[i]][j]
-     mm <- mm + 1
-   }
-  }
-
-  input.data <- list(n=kk,
-    cases = df[[case_field]],
-    expected = pmap$expCount,
-    weights=weights,
-    a1 = df[[covar1_field]],
-    a2 = df[[covar2_field]]/min(df[[covar2_field]]),
-    num=num,
-    adj=adj)
-
-  ### inits
-  b_init <- rep(0.0, kk)
-  model.inits <- function() {list(alpha = 0, tau.v = 1, tau.u = 1, b1 = b_init, b2 = b_init)}
-  # output parameters
-  model.parameters <- c("theta", "mu", "v", "u", "alpha", "PP", "b1", "b2", "RR_exp", "RR_het", "RR_clust")
-
-  model_file <- tempfile("BRCM-model", fileext='.bug')
-  R2WinBUGS::write.model(BRCM.model, model_file)
-
-  ### run BUGS
-  message("Running WinBUGS model...")
-  result.4 <- R2WinBUGS::bugs(input.data, model.inits, model.parameters, model_file, n.chains=1, n.iter=100000, debug=F, working.directory=tempdir())
-
-  unlink(model_file)
-
-#append results columns
-  df$expected <- pmap$expCount
-  df$rel_risk       <- result.4$mean$theta
-  df$rel_risk_se    <- result.4$sd$theta
-  df$extra_risk       <- result.4$mean$PP
-  df$extra_risk_se <- result.4$sd$PP
-  df$mean       <- result.4$mean$mu
-  df$mean_se    <- result.4$sd$mu
-  df$u_cor    <- result.4$mean$u
-  df$u_cor_se <- result.4$sd$u
-  df$u_heter  <- result.4$mean$v
-  df$u_heter_se <- result.4$sd$v
-  df$covariates   <- result.4$mean$RR_exp
-  df$heterogeneous   <- result.4$mean$RR_het
-  df$residuals <- result.4$mean$RR_clust
-  df$r1       <- result.4$mean$b1
-  df$r1_se    <- result.4$sd$b1
-  df$r2       <- result.4$mean$b2
-  df$r2_se    <- result.4$sd$b2
-
-  return(df)
-}
-
-BRCM.model <- function()
-{
-  for (i in 1:n)
-  {
-    # Poisson likelihood for observed counts
-    cases[i] ~ dpois(mu[i])
-    log(mu[i]) <- log(expected[i]) + alpha + u[i] + v[i] + b1[i]*a1[i] + b2[i]*a2[i]
-    # Relative Risk
-    theta[i] <- exp(alpha + u[i] + v[i] + b1[i]*a1[i] + b2[i]*a2[i])
-    # Posterior probability of RR[i]>1
-    PP[i] <- step(theta[i] - 1 + eps)
-    # Prior distribution for the uncorrelated heterogeneity
-    v[i] ~ dnorm(0,tau.v)
-    # Relative Risk decomposition
-    RR_exp[i]<-exp(b1[i]*a1[i] + b2[i]*a2[i])
-    RR_het[i]<-exp(v[i])
-    RR_clust[i]<-exp(u[i])
-  }
-
-  eps <- 1.0E-6
-
-  # CAR prior distribution for spatial correlated heterogeneity
-  u[1:n] ~ car.normal(adj[], weights[], num[], tau.u)
-  b1[1:n] ~ car.normal(adj[], weights[], num[], tau.b1)
-  b2[1:n] ~ car.normal(adj[], weights[], num[], tau.b2)
-
-  # Improper prior distribution for the mean relative risk in the study region
-  alpha ~ dflat()
-  mean <- exp(alpha)
-
-  # Hyperprior distributions on inverse variance parameter of random effects
-  tau.u ~ dgamma(0.5,0.0005)
-  tau.v ~ dgamma(0.5,0.0005)
-  tau.b1 ~ dgamma(0.5,0.0005)
-  tau.b2 ~ dgamma(0.5,0.0005)
-}
diff --git a/scripts/clustering.R b/scripts/clustering.R
index 0209064..b5b5472 100644
--- a/scripts/clustering.R
+++ b/scripts/clustering.R
@@ -1,7 +1,6 @@
 # case study using mclust package
 tool_exec <- function(in_params, out_prams)
 {
-  #message(getOption("repos"))
   if (!requireNamespace("sp", quietly = TRUE))
     install.packages("sp")
   if (!requireNamespace("mclust", quietly = TRUE))
@@ -10,22 +9,24 @@ tool_exec <- function(in_params, out_prams)
   require(sp)
 
   source_dataset = in_params[[1]]
-  nclust = NULL #in_params[[2]]
+  #nclust = in_params[[2]]
   out_table = out_prams[[1]]
   out_ellipses = out_prams[[2]]
   out_dens = out_prams[[3]]
   out_sim = out_prams[[4]]
-
   ### read data
   arc.progress_label("Loading Dataset")
-  data <- arc.select(arc.open(source_dataset))
+  d <- arc.open(source_dataset)
+  # read only OID field
+  data <- arc.select(d, names(d@fields[d@fields == "OID"]))
   data_shp <- arc.shape(data)
 
   data.xy <- cbind(data_shp$x, data_shp$y) # or data.frame(x=data_shp$x, y=data_shp$y)
 
   #########
   arc.progress_label("Bayesian Information Criterion for Model-Based Clustering...")
-  patternBIC <- mclustBIC(data.xy, G = nclust)
+  patternBIC <- mclustBIC(data.xy) # G = nclust)
+
   patternModel <- summary(patternBIC, data.xy)
   print(patternModel)
 
diff --git a/scripts/semi_par_regression.R b/scripts/semi_par_regression.R
deleted file mode 100644
index 476f243..0000000
--- a/scripts/semi_par_regression.R
+++ /dev/null
@@ -1,90 +0,0 @@
-
-make_knots <-function (x1, x2, num.knots)
-{
-  if (missing(num.knots)) 
-  num.knots <- max(10, min(50, round(length(x1)/4)))
-  X <- cbind(x1, x2)
-  dup.inds <- (1:nrow(X))[dup.matrix(X) == T]
-  if (length(dup.inds) > 0) 
-      X <- X[-dup.inds, ]
-  knots <- cluster::clara(X, num.knots)$medoids
-  return(knots)
-}
-
-tool_exec <- function(in_params, out_params)
-{
-  #### Load Library for Analysis ####
-  if (!requireNamespace("SemiPar", quietly = TRUE))
-    install.packages("SemiPar")
-  require(SemiPar)
-
-  #### Get Input Parameters ####
-  input_features = in_params[[1]]
-  input_predictions = in_params[[2]]
-  dep_variable = in_params[[3]]
-  nonlin_variables = in_params[[4]]
-  lin_variables = in_params[[5]]
-  input_knots = in_params[[6]]
-
-  #### Import Dataset to Dataframe ####
-  fc = arc.open(input_features)
-  df = arc.select(fc, c(dep_variable, nonlin_variables, lin_variables))
-  df['x'] = arc.shape(df)$x
-  df['y'] = arc.shape(df)$y
-
-  #### Import Knots to DataFrame ####
-  if (is.null(input_knots))
-  {
-    knots.est = make_knots(df$x, df$y)
-  }
-  else
-  {
-    knots_df = arc.select(arc.open(input_knots))
-    knots.est = make_knots(arc.shape(knots_df)$x, arc.shape(knots_df)$y)
-  }
-
-  #### Create Spatial Effect ####
-  fxy = "f(x,y,knots=knots.est)"
-
-  #### Create Non-Linear Params ####
-  nonlin_params = paste(paste0("f(", nonlin_variables, ")"), collapse = "+")
-
-  #### Create Formula and Fit SemiPar ####
-  e = as.list(df)
-  e$knots.est = knots.est
-  all_params = paste0(dep_variable, " ~ ",  fxy, "+", nonlin_params)
-    
-  #### Create Linear Params ####
-  if (!is.null(lin_variables))
-    all_params = paste0(all_params, "+", paste(lin_variables, collapse = "+"))
-
-#print(all_params)
-  tryCatch(
-  {
-    attach(e)
-    form = as.formula(all_params)
-    fit = spm(form, family="binomial")
-  },finally = detach(e))
-
-  #### Prediction ####
-  pred_fc = arc.open(input_predictions)
-  pred_df = arc.select(pred_fc, c(nonlin_variables, lin_variables))
-  summary(fit)
-  pred_df_xy <- data.frame(
-                    x = arc.shape(pred_df)$x,
-                    y = arc.shape(pred_df)$y,
-                    pred_df)
-
-  pred = with(pred_df_xy, predict.spm(fit, newdata = pred_df_xy, se = TRUE))
-
-  #### Write Output ####
-  pred_df['prediction'] = 1 / (1+exp(-pred$fit))
-  pred_df['prediction_se'] = pred$se
-  pred_df['lower_sign'] = 1 / (1+exp(-pred$fit+1.96*pred$se))
-  pred_df['upper_sign'] = 1/(1+exp(-pred$fit-1.96*pred$se))
-
-  output_features = out_params[[1]]
-  arc.write(output_features, pred_df)
-
-  return(out_params)
-}
diff --git a/symbology/DensityPoints_Symbology.lyr b/symbology/DensityPoints_Symbology.lyr
new file mode 100644
index 0000000..0fece6d
Binary files /dev/null and b/symbology/DensityPoints_Symbology.lyr differ