diff --git a/R/factory.R b/R/factory.R
index a5ba943..e2b14bc 100644
--- a/R/factory.R
+++ b/R/factory.R
@@ -856,9 +856,9 @@ bayesianSynth <- R6::R6Class(
         quantity = TRUE,
         tense = "past",
         display_mode_name = TRUE,
-        title = "Contrafactual Lift",
+        title = "Counterfactual Lift",
         xlab = "Effect of the Intervention",
-        units = "the contrafactual lift",
+        units = "the counterfactual lift",
         ...
       ) %>% return()
     },
@@ -942,6 +942,78 @@ bayesianSynth <- R6::R6Class(
         colors = c("#4daf4a", "#377eb8", "#e41a1c"),
         ...
       ) %>% return()
+    },
+    # TODO(jvives): finish implicit weight plots function to work generally
+    #' @description 
+    #' Plot implicit weight distribution across draws.
+    #' @return ggplot object with weight distribution per unit.
+    weightDraws = function(){
+      betas <- private$fitted %>% 
+        as.data.frame() %>%
+        dplyr::select(contains('beta'))
+      
+      beta_names <- private$data %>%
+        dplyr::filter(!!private$treated == 0) %>% 
+        dplyr::select(!!private$id) %>% 
+        unique() %>% pull()
+      
+      treated_name <- private$data %>%
+        dplyr::filter(!!private$treated == 1) %>% 
+        dplyr::select(!!private$id) %>% 
+        unique() %>% pull()
+      
+      donor_names <- beta_names[beta_names %in% treated_name == FALSE] 
+      
+      names(betas) <- donor_names
+      melt_betas <- tidyr::gather(betas, ID, weight)
+      
+      melt_betas %>% ggplot2::ggplot(ggplot2::aes(x=weight, y=ID, fill=ID)) +
+        ggridges::geom_density_ridges() + 
+        ggplot2::theme_minimal() +
+        ggplot2::theme(legend.position = "none") %>% return()
+    },
+    ## TODO(jvives): finish correlation plot function to work generally
+    #' @description
+    #' Plots correlations between weights across draws.
+    #' @return ggplot heatmap object with correlations.
+    weightCorr = function(){
+      betas <- private$fitted %>% 
+        as.data.frame() %>%
+        dplyr::select(contains('beta'))
+      
+      beta_names <- private$data %>%
+        dplyr::filter(!!private$treated == 0) %>% 
+        dplyr::select(!!private$id) %>% 
+        unique() %>% pull()
+      
+      treated_name <- private$data %>%
+        dplyr::filter(!!private$treated == 1) %>% 
+        dplyr::select(!!private$id) %>% 
+        unique() %>% pull()
+      
+      donor_names <- beta_names[beta_names %in% treated_name == FALSE] 
+      
+      names(betas) <- donor_names
+      
+      cormat <- round(cor(betas),3)
+      diag(cormat) <- NA
+      
+      melted_cormat <- melt(cormat)
+      ggplot2::ggplot(data = melted_cormat, 
+                      ggplot2::aes(x=X1,
+                                   y=X2,
+                                   fill=value)) + 
+        ggplot2::xlab('Units') + 
+        ggplot2::ylab('Units') +
+        ggplot2::labs(fill = 'Corr') +
+        ggplot2::geom_tile() + 
+        ggplot2::scale_fill_gradient2(low='red',
+                                      mid = 'white',
+                                      high='green') +
+        ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90,
+                                                           vjust = 0.5,
+                                                           hjust=1)) %>% 
+        return()
     }
   )
 )