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