Man pages for autovarCore
Automated Vector Autoregression Models and Networks
| 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... |
| assess_portmanteau_squared | Tests the homeskedasticity assumption for a VAR model using a... |
| 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... |
| 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 |
| selected_columns | Convert an outlier_mask to a vector of column indices |
| select_valid_masks | Select and return valid dummy outlier masks |
| significance_from_pearson_coef | Calculate the significance of a Pearson correlation... |
| 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 |
