Description: Implementing the Progeny Clustering algorithm, the progenyClust package assesses the clustering stability and identifies the optimal clustering number for a given data matrix. It uses kmeans clustering as default, but can be customized to work with other clustering algorithms and different parameter settings. The package includes one main function progenyClust(), plot and summary methods for progenyClust object, and two example datasets (test and cell) for testing.

Description: Implementing the Progeny Clustering algorithm, the 'progenyClust' package assesses the clustering stability and identifies the optimal clustering number for a given data matrix. It uses k-means clustering as a default, provides a tailored hierarchical clustering function, and can be customized to work with other clustering algorithms and different parameter settings. The package includes a main function progenyClust(), plot and summary methods for 'progenyClust' object, a function hclust.progenyClust() for hierarchical clustering, and two example datasets (test and cell) for testing.

Title: Prognostic ROC curves for evaluating the predictive capacity of
Author: Y. Foucher <Yohann.Foucher@univ-nantes.fr> and C. Combescure <Christophe.Combescure@hcuge.ch>
Description: Prognostic ROC curve is an alternative graphical approach to represent the discriminative capacity of the marker: a receiver operating characteristic (ROC) curve by plotting 1 minus the survival in the high-risk group against 1 minus the survival in the low-risk group. This package contains functions to assess prognostic ROC curve. The user can enter the survival according to a model previously estimated or the user can also enter individual survival data for estimating the prognostic ROC curve by using Kaplan-Meier estimator. The area under the curve (AUC) corresponds to the probability that a patient in the low-risk group has a longer lifetime than a patient in the high-risk group. The prognostic ROC curve provides complementary information compared to survival curves. The AUC is assessed by using the trapezoidal rules. When survival curves do not reach 0, the prognostic ROC curve is incomplete and the extrapolations of the AUC are performed by assuming pessimist, optimist and non-informative situations.