https://lcorag.r-universe.devRecent package updates in lcoragR-universehttps://github.com/lcorag.pnghttps://lcorag.r-universe.devFri, 05 Jun 2026 11:40:02 GMTluca.coraggio@unina.it (Luca Coraggio)Performs tuning of clustering models, methods and algorithms including the problem of determining an appropriate number of clusters. Validation of cluster analysis results is performed via quadratic scoring using resampling methods, as in Coraggio, L. and Coretto, P. (2023) <doi:10.1016/j.jmva.2023.105181>.https://github.com/r-universe/lcorag/actions/runs/27022653598Fri, 05 Jun 2026 11:40:02 GMTqcluster2.0.1successhttps://lcorag.r-universe.devhttps://github.com/cran/qclusterluca.coraggio@unina.it (Luca Coraggio)Tools to perform Pearson-Quetelet analysis on two-way contingency tables. The package computes absolute and relative frequencies, Quetelet indices, Pearson-Quetelet decomposition, apex tables, and chi-square summaries for interpreting associations between categorical variables.https://github.com/r-universe/lcorag/actions/runs/26156405865Mon, 30 Mar 2026 19:31:30 GMTPQA1.0.0successhttps://lcorag.r-universe.devhttps://github.com/cran/PQAluca.coraggio@unina.it (Luca Coraggio)Performs robust and sparse correlation matrix estimation. Robustness is achieved based on a simple robust pairwise correlation estimator, while sparsity is obtained based on thresholding. The optimal thresholding is tuned via cross-validation. See Serra, Coretto, Fratello and Tagliaferri (2018) <doi:10.1093/bioinformatics/btx642>.https://github.com/r-universe/lcorag/actions/runs/27056436126Tue, 09 Sep 2025 12:40:13 GMTRSC2.0.5successhttps://lcorag.r-universe.devhttps://github.com/cran/RSC