refactor analysis into a single ccn2019 notebook.

refact ccn2019/

enet and feature selection

recursive feature elimination with caret

model-free variable importance

Fix caret tuning, and plot var importance

fix VL bug, and use lag to shift local variables so for every trial we only consider previous local params.

fix param

remove current trial from local stat calculations.

reactor, and add plots

a new glmnet (lasso and ridge) and pls analysis with dummy vars for stimulus, stimulus_type, and N.

init a new dummy var analysis for ccn2019

Update correctness prediction

imbalanced sampling

basic diagram for predicted-actual of accuracy

pls and RMSE for accuracy

refactor modeling "correct" to another script

a new chunk to mark and remove highly correlated predictors for glm, etc

rewrite splitting into train/test so both will be a representative samples of the original "correct" ratio

comments and PLS VarImp diagrams

add variable importance for RT and accuracy

add PLS model for RT

plot PLS model output

two models, one for commonly used model of the n-back, and a new model to contain all local and global vars.

caret, AUC, etc for CCN2019

rewrite the whole thing from scratch

use here::here() to load the dataset

put preprocssed data into nested list-columns instead of unnested mutated cols

comments for list-column tibble ETL

Add CCA with cancor, pls on simulated data, and improve readability