Tests, utilities, and case studies for analyzing significance in
  clustered binary matched-pair data. The central function clust.bin.pair uses
  one of several tests to calculate a Chi-square statistic. Implemented are the
  tests Eliasziw (1991) <doi:10.1002/sim.4780101211>, Obuchowski (1998)
  <doi:10.1002/(SICI)1097-0258(19980715)17:13%3C1495::AID-SIM863%3E3.0.CO;2-I>,
  Durkalski (2003) <doi:10.1002/sim.1438>, and Yang (2010)
  <doi:10.1002/bimj.201000035> with McNemar (1947) <doi:10.1007/BF02295996>
  included for comparison. The utility functions nested.to.contingency and
  paired.to.contingency convert data between various useful formats. Thyroids
  and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989)
  <doi:10.1016/0165-1781(89)90196-0> respectively.

Hierarchical and partitioning algorithms to cluster blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. Different thresholds per cluster can be sets. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>)  and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data. Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available. Clustering of rows in multi-block context (notably with ClusMB strategy) is also included.

Enhancing T cell receptor (TCR) sequence analysis, 'ClusTCR2', based on 'ClusTCR' python program, leverages Hamming distance to compare the complement-determining region three (CDR3) sequences for sequence similarity, variable gene (V gene) and length. The second step employs the Markov Cluster Algorithm to identify clusters within an undirected graph, providing a summary of amino acid motifs and matrix for generating network plots. Tailored for single-cell RNA-seq data with integrated TCR-seq information, 'ClusTCR2' is integrated into the Single Cell TCR and Expression Grouped Ontologies (STEGO) R application or 'STEGO.R'. See the two publications for more details. Sebastiaan Valkiers, Max Van Houcke, Kris Laukens, Pieter Meysman (2021) <doi:10.1093/bioinformatics/btab446>, Kerry A. Mullan, My Ha, Sebastiaan Valkiers, Nicky de Vrij, Benson Ogunjimi, Kris Laukens, Pieter Meysman (2023) <doi:10.1101/2023.09.27.559702>.