diff --git a/ccn2019.rev2.Rmd b/ccn2019.rev2.Rmd
index 47e0435..7582591 100644
--- a/ccn2019.rev2.Rmd
+++ b/ccn2019.rev2.Rmd
@@ -73,21 +73,18 @@
 library(ggplot2)
 library(tidyverse)
 library(stringi)
-library(plsRglm)
-library(plsdof)
+library(pls)
+#library(plsRglm)
+#library(plsdof)
+library(pls)
 library(caret)
 library(here)
+library(tsibble)
 ```
 
-```{r params}
+```{r preprocessing}
 load(here('data/CL2015.RData'))
-
 window_size <- 8
-```
-
-
-
-```{r history}
 
 with_lures <- function(stimulus, stimulus_type, n) {
   sapply(1:length(stimulus), function(i) {
@@ -99,68 +96,29 @@
   })
 }
 
-with_history <- function(stimuli, length=16, fixed=F) {
-  seq <- paste(stimuli, collapse = '')
-  
-  sapply(1:length(stimuli), function(i) {
-    stri_reverse(str_sub(seq, max(1,i-length+1), i))
-  })
-  #ifelse(fixed, h[str_length(h)==size], h)
-}
-
-# $x_{s,local}$
-with_skewness <- function(history) {
-  sapply(history, function(h) {
-    freqs <- table(unlist(str_split(h,"")))
-    sum(sort(freqs, decreasing = T)[1:2]) - 1
-  })
-}
-
-# $x_{u,local}$
-with_lumpiness <- function(history) {
-  sapply(history, function(h) {
-    freqs <- table(unlist(str_split(h,"")))
-    max(freqs) - 1
-  })
-}
-
-
-with_targets_ratio <- function(stimulus_type, length=16) {
-  sapply(1:length(stimulus_type), function(i) {
-    trials <- stimulus_type[max(1,i-length):i]
-    length(trials[trials=="target"]) / length(trials)
-  })
-}
-
-with_lures_ratio <- function(stimulus_type, length=16) {
-  sapply(1:length(stimulus_type), function(i) {
-    trials <- stimulus_type[max(1,i-length):i]
-    length(trials[trials=="lure"]) / length(trials)
-  })
-}
-
-#TODO change to list column workflow with broom for model fitting and evaluating the fits
-# duh! we are using list nested insided a tibble, so put all new columns in a new list column
-# instead of adding a new column for each.
 NB2 <- NB %>%
   group_by(participant, condition, block) %>%
   mutate(n = ifelse(condition=='2-back',2,3)) %>%
   mutate(stimulus_type = with_lures(stimulus, stimulus_type, n)) %>%
-  mutate(history = with_history(stimulus, window_size)) %>%
-  group_nest(.key='design_matrix')
+  mutate(tl = slide_dbl(stimulus_type, ~length(which(.=='target')), .partial=T,.size=window_size)) %>%
+  mutate(ll = slide_dbl(stimulus_type, ~length(which(.=='lure')), .partial=T, .size=window_size)) %>%
+  mutate(sl = slide_dbl(stimulus, ~sum(sort(table(.), decreasing = T)[1:2]) - 1, .partial=T, .size=window_size)) %>%
+  mutate(ul = slide_dbl(stimulus, ~max(table(.))-1, .partial=T, .size=window_size)) %>%
+  mutate(vl = slide_dbl(stimulus, ~length(unique(.)), .partial=T, .size=window_size)) %>%
+  mutate(t  = length(which(stimulus_type=='target'))) %>%
+  mutate(l  = length(which(stimulus_type=='lure'))) %>%
+  mutate(s  = sum(sort(table(stimulus), decreasing = T)[1:2]) - 1) %>%
+  mutate(v  = length(unique(stimulus))) %>%
+  # replace NAs in sl column
+  mutate(sl = ifelse(is.na(sl), 0, sl)) %>%
+  nest(.key='design_matrix')
 
-#FIXME update the following functions to accept and also generate tidy list-columns
-#FIXME use map() and map_*() from purrr
+# Models
 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'])
+  mutate(model.lm = map(design_matrix, ~lm(rt~.,.x))) %>%
+  mutate(model.pls = map(design_matrix, ~plsr(rt ~ ., data = .x, validation = "CV")))
+  #mutate(model.pca = map(design_matrix, ~prcomp(~rt,.x, center=T,scale.=T, na.action=na.exclude))) %>%
+  #mutate(pc1=pca$x[,'PC1'], pc2=pca$x[,'PC2'])
 
 # caret
 library(caret)