An implementation of the clugen algorithm for generating
    multidimensional clusters with arbitrary distributions. Each cluster is
    supported by a line segment, the position, orientation and length of which
    guide where the respective points are placed. This package is described in
    Fachada & de Andrade (2023) <doi:10.1016/j.knosys.2023.110836>.

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.

Clustered covariate regression enables estimation and inference in 
 both linear and non-linear models with linear predictor functions even when the
 design matrix is column rank deficient. Routines in this package implement algorithms
 in Soale and Tsyawo (2019) <doi:10.13140/RG.2.2.32355.81441>.

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) <https://mpra.ub.uni-muenchen.de/120220>.

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>.