A package to automate and simplify the process from raw data to VAR models.
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DESCRIPTION
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NAMESPACE
README.md

README.md

Autovar

Autovar is an R package for automating and simplifying the process from raw data to VAR models. For the actual VAR calculations, Bernhard Pfaff's vars package is used.

To install, type the following:

install.packages('devtools')
require('devtools')
install_github('roqua/autovar')

If you're using Windows and the above steps give you errors, try the following alternate way to install Autovar:

unloadNamespace('autovar')
download.file('https://autovar.nl/binaries/autovar_0.2-2.zip',destfile='autovar_0.2-2.zip'); install.packages('autovar_0.2-2.zip',repos = NULL)
install.packages(c('Amelia','e1071','foreign','ggplot2','gridExtra','igraph','jsonlite','knitr','markdown','norm','parallel','psych','RcppArmadillo','reshape2','stringi','stringr','TimeProjection','urca','vars'))
library('autovar')

Documentation for this package can be found here.

Example Use
library('autovar')

# Example data sets can be found on https://autovar.nl
av_state <- load_file("/path/to/file.dta")

# Include models with (and without) trends in the search
av_state <- add_trend(av_state)

# Include models with (and without) day dummies in the search
av_state <- set_timestamps(av_state,          
                           date_of_first_measurement = "2015-12-31",
                           measurements_per_day = 1)
                           
# Search for VAR models for the variables Depression and Activity up to lag 3.
av_state <- var_main(av_state, vars = c("Depression", "Activity"),
                     lag_max = 3,
                     log_level = 3)

# Show the best models found
print_best_models(av_state)