Visualizing Class Specific Heterogeneous Tendencies in
Categorical Data
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.
dummies, magic, parallel, stringr, ggplot2, wordcloud,
RColorBrewer, methods, stats, utils, grDevices

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>.
magic, stringr, ggplot2, wordcloud, RColorBrewer, stats,
utils, grDevices

magic, stringr, ggplot2, wordcloud, RColorBrewer, stats,
utils, grDevices, Rcpp (>= 1.0.9)

Creates the MCC-F1 Curve and Calculates the MCC-F1 Metric and
the Best Threshold
c(person("Chang", "Cao", role = c("aut", "cre"),
email = "kirin.cao@mail.utoronto.ca"),
person("Michael", "Hoffman", role = "aut", email = "michael.hoffman@utoronto.ca"),
person("Davide", "Chicco", role = "aut",
email = "davide.chicco@gmail.com"))
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.
Chang Cao [aut, cre],
Michael Hoffman [aut],
Davide Chicco [aut]

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.

Estimating Parameters for a Matched Case Control Design with a
Mismeasured Exposure using Instrumental Variables
Applying the methodology from Manuel, Wang, and Sinha (2018), estimates for the
parameters for matched case control data with a mismeasured exposure are calculated
through the use of user supplied instrumental variables.