Packaged: 2018-09-29 08:20:47 UTC; pengyiyang

Description: Clustering algorithms are designed to cluster objects into a number of clusters. Any clustering algorithm provides the 'best'
grouping of objects according to some criterion.
This does not guarantee a 'good' clustering solution in the sense that some allocations were not simply the result of chance.
This package allows the user to apply any clustering algorithm to a data set. The cluster allocations are subjected to a
to determine the extent to which the clustering structure is stable and fundamental to the data set. For more information

Title: Simultaneous Semi-Parametric Estimation of Clustering and
Authors@R: c(person("Matthieu", "Marbac", role = c("aut", "cre", "cph"), email = "matthieu.marbac-lourdelle@ensai.fr"),
person("Mohammed", "Sedki", role = "aut"),
person("Christophe", "Biernacki", role = "aut"),
person("Vincent", "Vandewalle", role = "aut"))
Description: Parameter estimation of regression models with fixed group effects, when the group variable is missing while group-related variables are available. Parametric and semi-parametric approaches described in Marbac et al. (2020) <arXiv:2012.14159> are implemented.
Imports: Rcpp, parallel, ALDqr, ald, quantreg, VGAM
Author: Matthieu Marbac [aut, cre, cph],

Title: ROC Analysis in Three-Class Classification Problems for
Author: Duc-Khanh To [aut, cre] (<https://orcid.org/0000-0002-4641-0764>), with contributions from Gianfranco Adimari and Monica Chiogna
Description: Statistical methods for ROC surface analysis in three-class classification problems for clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for:
(i) true class fractions (TCFs) at fixed pairs of thresholds;
(iii) the volume under ROC surface (VUS);
Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022) <doi:10.1177/09622802221089029>. Referring to point (iv), three  different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018) <doi:10.1177/0962280217742539>. Visualization tools are also provided. We refer readers to the articles cited above for all details.
Depends: R (>= 3.5.0), stats, utils, graphics, nlme
Imports: rgl, car, numDeriv, ggplot2, ggpubr, snow, doSNOW, foreach