Description: It contains six common multi-category classification accuracy evaluation measures.
All of these measures could be found in Li and Ming (2019) <doi:10.1002/sim.8103>. Specifically,

Title: Visualizing Class Specific Heterogeneous Tendencies in
Description: Functions for performing multiple-class cluster correspondence analysis(MCCCA). The main functions are create.MCCCAdata() to create a list to be applied to MCCCA, MCCCA() to apply MCCCA, and plot.mccca() for visualizing MCCCA result.
Imports: dummies, magic, parallel, stringr, ggplot2, wordcloud,
RColorBrewer, methods, stats, utils, grDevices
Packaged: 2022-01-05 13:11:02 UTC; takagishimariko

Description: Performing multiple-class cluster correspondence analysis(MCCCA). The main functions are create.MCCCAdata() to create a list to be applied to MCCCA, MCCCA() to apply MCCCA, and plot.mccca() for visualizing MCCCA result. Methods used in the
package refers to Mariko Takagishi and Michel van de Velden (2022)<doi:10.1080/10618600.2022.2035737>.
Imports: magic, stringr, ggplot2, wordcloud, RColorBrewer, stats,
Packaged: 2022-09-06 07:35:12 UTC; takagishimariko

Packaged: 2022-11-28 12:40:37 UTC; takagishimariko

Title: Creates the MCC-F1 Curve and Calculates the MCC-F1 Metric and
Authors@R: c(person("Chang", "Cao", role = c("aut", "cre"),
person("Michael", "Hoffman", role = "aut", email = "michael.hoffman@utoronto.ca"),
person("Davide", "Chicco", role = "aut",
Description: The MCC-F1 analysis is a method to evaluate the performance of binary classifications.
The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth.
The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification.

Title: Process an MCMC Sample of Clusterings
Description: Implements methods for processing a sample of (hard)
clusterings, e.g. the MCMC output of a Bayesian clustering
model. Among them are methods that find a single best
clustering to represent the sample, which are based on the
posterior similarity matrix or a relabelling algorithm.