diff --git a/ccn2019.rev2.Rmd b/ccn2019.rev2.Rmd
index eb989f4..e6051d9 100644
--- a/ccn2019.rev2.Rmd
+++ b/ccn2019.rev2.Rmd
@@ -143,16 +143,21 @@
 # instead of adding a new column for each.
 NB2 <- NB %>%
   group_by(participant, condition, block) %>%
-  nest() %>% unnest(data) %>%
   mutate(n = ifelse(condition=='2-back',2,3)) %>%
   mutate(stimulus_type = with_lures(stimulus, stimulus_type, n)) %>%
   mutate(history = with_history(stimulus, window_size)) %>%
-  mutate(x_sl = with_skewness(history)) %>%
-  mutate(x_ul = with_lumpiness(history)) %>%
-  mutate(x_t = with_targets_ratio(stimulus_type, window_size)) %>%
-  mutate(x_l = with_lures_ratio(stimulus_type, window_size)) %>%
-  ungroup()
+  group_nest(.key='design_matrix')
+
+#FIXME update the following functions to accept and also generate tidy list-columns
+#FIXME use map() and map_*() from purrr
+NB2 <- NB2 %>%
+  mutate(x_sl = map(design_matrix, ~with_skewness(.x$history))) %>%
+  mutate(x_ul = map(design_matrix, ~with_lumpiness(.x$history))) %>%
+  mutate(x_t  = map(design_matrix, ~with_targets_ratio(.x$stimulus_type, window_size))) %>%
+  mutate(x_l  = map(design_matrix, ~with_lures_ratio(.x$stimulus_type, window_size)))
+  #%>% ungroup() %>% unnest()
   
+#FIXME perform modelings within tidy list-columns and put result in "model.pca" column
 pca <- prcomp(~x_sl+x_ul+x_t+x_l, NB2, center = TRUE,scale. = TRUE, na.action=na.exclude)
 NB2 <- NB2 %>% mutate(pc1=pca$x[,'PC1'], pc2=pca$x[,'PC2'])
 

diff --git a/ccn2019.rev2.Rmd b/ccn2019.rev2.Rmd
index eb989f4..e6051d9 100644
--- a/ccn2019.rev2.Rmd
+++ b/ccn2019.rev2.Rmd
@@ -143,16 +143,21 @@
 # instead of adding a new column for each.
 NB2 <- NB %>%
   group_by(participant, condition, block) %>%
-  nest() %>% unnest(data) %>%
   mutate(n = ifelse(condition=='2-back',2,3)) %>%
   mutate(stimulus_type = with_lures(stimulus, stimulus_type, n)) %>%
   mutate(history = with_history(stimulus, window_size)) %>%
-  mutate(x_sl = with_skewness(history)) %>%
-  mutate(x_ul = with_lumpiness(history)) %>%
-  mutate(x_t = with_targets_ratio(stimulus_type, window_size)) %>%
-  mutate(x_l = with_lures_ratio(stimulus_type, window_size)) %>%
-  ungroup()
+  group_nest(.key='design_matrix')
+
+#FIXME update the following functions to accept and also generate tidy list-columns
+#FIXME use map() and map_*() from purrr
+NB2 <- NB2 %>%
+  mutate(x_sl = map(design_matrix, ~with_skewness(.x$history))) %>%
+  mutate(x_ul = map(design_matrix, ~with_lumpiness(.x$history))) %>%
+  mutate(x_t  = map(design_matrix, ~with_targets_ratio(.x$stimulus_type, window_size))) %>%
+  mutate(x_l  = map(design_matrix, ~with_lures_ratio(.x$stimulus_type, window_size)))
+  #%>% ungroup() %>% unnest()
   
+#FIXME perform modelings within tidy list-columns and put result in "model.pca" column
 pca <- prcomp(~x_sl+x_ul+x_t+x_l, NB2, center = TRUE,scale. = TRUE, na.action=na.exclude)
 NB2 <- NB2 %>% mutate(pc1=pca$x[,'PC1'], pc2=pca$x[,'PC2'])
 
diff --git a/pls_playground.Rmd b/pls_playground.Rmd
index c35feac..d4da99c 100644
--- a/pls_playground.Rmd
+++ b/pls_playground.Rmd
@@ -1,6 +1,8 @@
 ---
 title: "PLS Training"
 output: html_notebook
+editor_options: 
+  chunk_output_type: console
 ---
 
 PLS:
@@ -83,7 +85,7 @@
 ```
 
 
-```{r plsrglm}
+```
 rm(list = ls())
 library(plsRglm)