All functions
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add_derived_column()
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Add a new column using data from other columns |
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add_trend()
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Adds a trend variable to a data set |
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contemporaneous_correlations_plot()
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Plot the contemporaneous correlations summary |
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convert_to_graph()
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Convert best model to graph |
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generate_network()
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Return a JSON array of network data of a fitting model specific to hoegekis.nl |
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generate_networks()
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Return a JSON array of network data of a fitting model |
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group_by()
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Split up a data set into different subsets |
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impute_dataframe()
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Impute missing values in a data.frame using EM imputation |
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impute_missing_values()
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Impute missing values |
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load_dataframe()
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Returns an av_state for data loaded from a data.frame |
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load_file()
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Load a data set from a .sav, .dta, or .csv file |
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order_by()
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Order the rows in a data set |
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plot_barchart()
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Plots a barchart of manual_score and the av_scores |
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print_accepted_models()
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Print a list of accepted models after a call to var_main |
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print_best_models()
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Prints the best model from the list of accepted models |
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print_rejected_models()
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Print a list of rejected models after a call to var_main |
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select_range()
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Select a subset of rows of a data set to be retained |
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select_relevant_columns()
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Select and return the relevant columns |
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select_relevant_rows()
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Select and return the relevant rows |
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set_timestamps()
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Add dummy variables for weekdays and day parts |
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store_file()
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Export a modified data set as an SPSS readable .sas file |
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var_info()
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Print summary information and tests for a VAR model estimation |
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var_main()
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Determine possibly optimal models for Vector Autoregression |
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var_summary()
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Print the output of var_main |
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vargranger_plot()
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Plot the Granger causality summary |
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visualize()
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Visualize columns of the data set |
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visualize_residuals()
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Visualize the residuals of a VAR model |