AutovarCore finds the best fitting VAR models for a given time series data set that pass the selected set of residual assumptions. AutovarCore will also generate Granger causality networks given a data frame (this functionality is not yet implemented). AutovarCore is a simplified/efficient version of Autovar.
To install, type the following:
You should use Autovar if you
You should use AutovarCore if you
library('autovarCore') # AutovarCore requires input data in data.frame format. # If you have data in a .csv, .dta, or .sav file, use # the 'foreign' library to load this data into R first. # (You may need to type: # install.packages('foreign') # if you do not have the foreign library installed on # your system.) library('foreign') # This example data set can be downloaded from # https://autovar.nl/datasets/aug_pp5_da.sav suppressWarnings(dfile <- read.spss('~/Downloads/aug_pp5_da.sav')) dframe <- data.frame(Activity = dfile$Activity, Depression = dfile$Depression) # Call autovar with the given data frame. Type: # ?autovar # (after having typed "library('autovarCore')") to see # which other options are available. models_found <- autovar(dframe, selected_column_names = c('Activity', 'Depression')) # Show details for the best model found print(models_found[])