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 |