Friedrich Leisch

TL;DR: The R package archetypes is presented, which provides an implementation of the archetypal analysis algorithm within R and different exploratory tools to analyze the algorithm during its execution and its final result.

Abstract: Clustering analysis' primary purpose is to divide a dataset into a finite number of segments based on the similarities between items. In recent years, a significant amount of study has focused on the spatio-temporal aspects of clustering. However, clusters are no longer regarded as static objects since changes influence them in the underlying population. This paper describes an R package implementing the MONIC framework for tracing the evolution of clusters extracted from temporal datasets. The name of the package is clusTransition, which stands for Cluster Transition. The algorithm is based on re-clustering cumulative datasets that evolve at successive time-points and monitoring the transitions experienced by the clusters in these clustering solutions. This paper's contribution is to demonstrate how the package clusTransition is developed in the R programming language, and its workflow is discussed using hypothetical and real-life datasets.

TL;DR: Package exmix provides functionality for tting nite mixtures of regression models and includes random intercept models where the random part is modelled by a nite mixture instead of a-priori selecting a suitable distribution.

TL;DR: This work considers autoregressive neural network (AR-NN) processes driven by additive noise and demonstrates that the characteristic roots of the shortcut determine the stochastic behavior of the overall AR-NN process.