https://maria-alonsopena.r-universe.devRecent package updates in maria-alonsopenaR-universehttps://github.com/maria-alonsopena.pnghttps://maria-alonsopena.r-universe.devFri, 20 Feb 2026 06:40:22 GMTmariaalonso.pena@usc.es (Maria Alonso-Pena)Nonparametric smoothing methods for density and regression estimation and inference with circular data. The package provides kernel density estimation along with inferential tools such as circular SiZer for feature significance, mode estimation, and modal clustering. It includes multiple methods for selecting the smoothing parameter, allowing users to optimize the trade-off between bias and variance. Various plotting functions help visualize estimated densities, modes, clusters, and significance features. For regression, the package implements nonparametric estimation of the mean regression function as well as other conditional characteristics, including modal regression and generalized regression. Bandwidth selection is also supported in the regression context, and testing procedures are available to assess structural features or effects in circular regression models.https://github.com/r-universe/maria-alonsopena/actions/runs/26282536041Fri, 20 Feb 2026 06:40:22 GMTNPCirc3.2.1successhttps://maria-alonsopena.r-universe.devhttps://github.com/cran/NPCircmariaalonso.pena@usc.es (María Alonso-Pena)Nonparametric density estimation for (hyper)spherical data by means of a parametrically guided kernel estimator (Alonso-Pena et al. (2024) <doi:10.1111/sjos.12737>. The package also allows the data-driven selection of the smoothing parameter and the representation of the estimated density for circular and spherical data. Estimators of the density without guide can also be obtained.https://github.com/r-universe/maria-alonsopena/actions/runs/26495624289Tue, 28 Oct 2025 21:20:02 GMTpgKDEsphere1.0.2successhttps://maria-alonsopena.r-universe.devhttps://github.com/cran/pgKDEsphere