https://penny4nonsense.r-universe.devRecent package updates in penny4nonsenseR-universehttps://github.com/penny4nonsense.pnghttps://penny4nonsense.r-universe.devMon, 22 Jun 2026 15:20:17 GMTjparker588@gmail.com (Jason Parker)Efficient computation of multivariate statistical depth functions in arbitrary dimension d. Implements Mahalanobis depth, Tukey (halfspace) depth, Liu simplicial depth (via adaptive Monte Carlo), projection depth, and spatial depth. Provides depth-based medians, central regions, outlier detection, and depth-depth plots. 'C++' backends via 'Rcpp' and 'RcppEigen' ensure performance at large n and d. References: Liu (1990) <doi:10.1214/aos/1176347507>, Zuo and Serfling (2000) <doi:10.1214/aos/1016218226>, Vardi and Zhang (2000) <doi:10.1073/pnas.97.4.1423>.https://github.com/r-universe/penny4nonsense/actions/runs/28285897746Mon, 22 Jun 2026 15:20:17 GMTdepthR0.1.8failurehttps://penny4nonsense.r-universe.devhttps://github.com/penny4nonsense/depthrjparker588@gmail.com (Jason Parker)Eigenvalue-based estimation of the number of factors in approximate factor models. Designed to work when either N or T is large, without requiring both dimensions to grow simultaneously. Implements the eigenvalue ratio estimator of Ahn and Horenstein (2013) <doi:10.3982/ECTA8968>, the information criteria of Bai and Ng (2002) <doi:10.1111/1468-0262.00273>, the tuned penalty of Alessi, Barigozzi and Capasso (2010) <doi:10.1016/j.spl.2010.08.005>, the auto-covariance ratio estimator of Lam and Yao (2012) <doi:10.1214/12-AOS970>, and the edge distribution estimators of Onatski (2009) <doi:10.3982/ECTA6964> and Onatski (2010) <doi:10.1162/REST_a_00043>.https://github.com/r-universe/penny4nonsense/actions/runs/27947425771Wed, 22 Apr 2026 16:45:48 GMTfactorselect0.1.2successhttps://penny4nonsense.r-universe.devhttps://github.com/penny4nonsense/factorselectfactorselect.Rmdfactorselect.htmlIntroduction to factorselect2026-04-17 16:45:492026-04-17 16:45:49