Evaluation of Algorithm Collections Using Item Response Theory

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Documentation for package ‘airt’ version 0.2.2

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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.
autoplot.effectivenesscrm Computes the actual and predicted effectiveness of the collection of algorithms.
autoplot.effectivenesspoly Computes the actual and predicted effectiveness of the collection of algorithms.
autoplot.heatmapcrm Function to produce heatmaps from a continuous IRTmodel
autoplot.latenttrait Performs the latent trait analysis
autoplot.modelgoodnesscrm Computes the goodness of IRT model for all algorithms.
autoplot.modelgoodnesspoly Computes the goodness of IRT model for all algorithms.
autoplot.tracelinespoly Function to plot tracelines from a polytomous IRTmodel
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.
heatmaps_crm Function to produce heatmaps from a continuous IRTmodel
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.
tracelines_poly Function to plot tracelines from a polytomous IRTmodel