Man pages for roqua/autovar
Automated Vector Autoregression Model Creation

add_derived_columnAdd a new column using data from other columns
add_trendAdds a trend variable to a data set
contemporaneous_correlations_plotPlot the contemporaneous correlations summary
convert_to_graphConvert best model to graph
generate_networkReturn a JSON array of network data of a fitting model...
generate_networksReturn a JSON array of network data of a fitting model
group_bySplit up a data set into different subsets
impute_dataframeImpute missing values in a data.frame using EM imputation
impute_missing_valuesImpute missing values
load_dataframeReturns an av_state for data loaded from a data.frame
load_fileLoad a data set from a .sav, .dta, or .csv file
order_byOrder the rows in a data set
plot_barchartPlots a barchart of manual_score and the av_scores
print_accepted_modelsPrint a list of accepted models after a call to var_main
print_best_modelsPrints the best model from the list of accepted models
print_rejected_modelsPrint a list of rejected models after a call to var_main
select_rangeSelect a subset of rows of a data set to be retained
select_relevant_columnsSelect and return the relevant columns
select_relevant_rowsSelect and return the relevant rows
set_timestampsAdd dummy variables for weekdays and day parts
store_fileExport a modified data set as an SPSS readable .sas file
vargranger_plotPlot the Granger causality summary
var_infoPrint summary information and tests for a VAR model...
var_mainDetermine possibly optimal models for Vector Autoregression
var_summaryPrint the output of var_main
visualizeVisualize columns of the data set
visualize_residualsVisualize the residuals of a VAR model
roqua/autovar documentation built on May 27, 2019, 10:41 p.m.