apply_ln_transformation
                        Applies the natural logarithm to the data set
assess_joint_sktest     Tests the skewness and kurtosis of a VAR model
assess_kurtosis         Tests the kurtosis of a VAR model
assess_portmanteau      Tests the white noise assumption for a VAR
                        model using a portmanteau test on the residuals
assess_portmanteau_squared
                        Tests the homeskedasticity assumption for a VAR
                        model using a portmanteau test on the squared
                        residuals
assess_skewness         Tests the skewness of a VAR model
autovar                 Return the best VAR models found for a time
                        series data set
autovarCore-package     Automated Vector Autoregression Networks
coefficients_of_kurtosis
                        Kurtosis coefficients.
coefficients_of_skewness
                        Skewness coefficients.
compete                 Returns the winning model
day_dummies             Calculate weekday dummy variables
daypart_dummies         Calculate day-part dummy variables
explode_dummies         Explode dummies columns into separate dummy
                        variables
impute_datamatrix       Imputes the missing values in the input data
invalid_mask            Calculate a bit mask to identify invalid
                        outlier dummies
model_is_stable         Eigenvalue stability condition checking
model_score             Return the model fit for the given varest model
needs_trend             Determines if a trend is required for the
                        specified VAR model
portmanteau_test_statistics
                        An implementation of the portmanteau test.
print_correlation_matrix
                        Print the correlation matrix of the residuals
                        of a model annotated with p-values
residual_outliers       Calculate dummy variables to mask residual
                        outliers
run_tests               Execute a series of model validity assumptions
run_var                 Calculate the VAR model and apply restrictions
select_valid_masks      Select and return valid dummy outlier masks
selected_columns        Convert an outlier_mask to a vector of column
                        indices
trend_columns           Construct linear and quadratic trend columns
validate_params         Validates the params given to the autovar
                        function
validate_raw_dataframe
                        Validates the dataframe given to the autovar
                        function
