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_squared
|
Tests the homeskedasticity assumption for a VAR model using a portmanteau test on the squared residuals |
assess_portmanteau
|
Tests the white noise assumption for a VAR model using a portmanteau test on the 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 |