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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:


For more information on Autovar, see Help documentation for AutovarCore can be found on

Should I use Autovar or AutovarCore?

You should use Autovar if you

You should use AutovarCore if you

Example use


# 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.)

# This example data set can be downloaded from
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

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autovarCore documentation built on May 2, 2019, 4:01 a.m.