Evaluation of Algorithm Collections Using Item Response Theory
airt: Evaluation of Algorithm Collections Using Item Response Theory
Computes the actual and predicted effectiveness of a given algorithm.
Computes the actual and predicted effectiveness of a given algorithm.
Fits a continuous IRT model.
Computes the actual and predicted effectiveness of the collection of a...
Computes the actual and predicted effectiveness of the collection of a...
Function to produce heatmaps from a continuous IRTmodel
Performs the latent trait analysis
Converts continuous performance data to polytomous data with 5 categor...
Computes the goodness of IRT model for all algorithms.
Computes the goodness of IRT model for a given algorithm.
Computes the goodness of the IRT model fit for a given algorithm.
Computes the goodness of IRT model for all algorithms.
Fits a polytomous IRT model.
Objects exported from other packages
Function to plot tracelines from a polytomous IRTmodel
An evaluation framework for algorithm portfolios using Item Response Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.