Automated Vector Autoregression Models and Networks


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Documentation for package ‘autovarCore’ version 1.0-0

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autovarCore-package Automated Vector Autoregression 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 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
compete Returns the winning model
daypart_dummies Calculate day-part dummy variables
day_dummies Calculate day 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
rcpp_hello_world Simple function using Rcpp
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
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