Evaluation of Algorithm Collections Using Item Response Theory


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Documentation for package ‘airt’ version 0.2.0

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algo_effectiveness_crm Computes the actual and predicted effectiveness of a given algorithm.
algo_effectiveness_poly Computes the actual and predicted effectiveness of a given algorithm.
cirtmodel Fits a continuous IRT model.
classification_cts A dataset containing classification algorithm performance data in a continuous format.
classification_poly A dataset containing classification algorithm performance data in a polytomous format.
effectiveness_crm Computes the actual and predicted effectiveness of the collection of algorithms.
effectiveness_poly Computes the actual and predicted effectiveness of the collection of algorithms.
latent_trait_analysis Performs the latent trait analysis
make_polyIRT_data Converts continuous performance data to polytomous data with 5 categories.
model_goodness_crm Computes the goodness of IRT model for all algorithms.
model_goodness_for_algo_crm Computes the goodness of IRT model for a given algorithm.
model_goodness_for_algo_poly Computes the goodness of the IRT model fit for a given algorithm.
model_goodness_poly Computes the goodness of IRT model for all algorithms.
pirtmodel Fits a polytomous IRT model.
prepare_for_plots_crm Utility function to make a dataframe from the continuous IRTmodel
prepare_for_plots_poly Utility function to make a dataframe from the polytomous IRTmodel