## All functions

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 Models and 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

outliers_column()

Determine the outliers column for the given column data

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

significance_from_pearson_coef()

Calculate the significance of a Pearson correlation coefficient

sktest_joint_p()

SK test p-level

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