Two-step feature-based clustering method designed for micro panel (longitudinal) data with the artificial panel data generator. See Sobisek, Stachova, Fojtik (2018) <arXiv:1807.05926>.

Providing a cluster allocation for n samples, either with an $n \times p$ data matrix or an $n \times n$ distance
                matrix, a bootstrap procedure is performed. The proportion of bootstrap replicates where a pair of samples
                cluster in the same cluster indicates who tightly the samples in a particular cluster clusters together.

Cluster Evolution Analytics allows us to use exploratory what if questions in the sense that the present information of an object is plugged-in a dataset in a previous time frame so that we can explore its evolution (and of its neighbors) to the present. See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2024) <doi:10.1016/j.softx.2024.101921>.

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.

Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test)
       for clustered data documented in
       Jiang et. al (2020) <doi:10.18637/jss.v096.i06>.

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;
  (ii) the ROC surface;
  (iii) the volume under ROC surface (VUS);
  (iv) the optimal pairs of thresholds.
  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.