Statistical analysis of axial using distributions Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, and more. Fernandez-Duran, J.J. and Gregorio-Dominguez, M.M. (2025), Multimodal distributions for circular axial data", <doi:10.48550/arXiv.2504.04681>.

CircSeqAlignTk is designed for end-to-end RNA-Seq data analysis of circular genome sequences, from alignment to visualization. It mainly targets viroids which are composed of 246-401 nt circular RNAs. In addition, CircSeqAlignTk implements a tidy interface to generate synthetic sequencing data that mimic real RNA-Seq data, allowing developers to evaluate the performance of alignment tools and workflows.

r-circrnaprofiler is a computational framework for a comprehensive in silico analysis of circular RNA (circRNAs). This computational framework allows combining and analyzing circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis.

The statistical analysis of circular data using distributions based on symmetric Nonnegative Trigonometric Sums (NNTS). It includes functions to perform empirical analysis and estimate the parameters of density functions. Fernandez-Duran, J.J. and Gregorio-Dominguez, M.M. (2025), "Multimodal Symmetric Circular Distributions Based on Nonnegative Trigonometric Sums and a Likelihood Ratio Test for Reflective Symmetry", <doi:10.48550/arXiv.2412.19501>.

Calculating silhouette information for clusters on circular or linear data using fast algorithms. These algorithms run in linear time on sorted data, in contrast to quadratic time by the definition of silhouette. When used together with the fast and optimal circular clustering method FOCC (Debnath & Song 2021) <doi:10.1109/TCBB.2021.3077573> implemented in R package OptCirClust', circular silhouette can be maximized to find the optimal number of circular clusters; it can also be used to estimate the period of noisy periodical data.

Implementation of Librino, Levorato, and Zorzi (2014) <doi:10.1002/wcm.2305> algorithm for computation of the intersection areas of an arbitrary number of circles.