rgl

R package rgl: 3D visualization device system (OpenGL): Provides medium to high level functions for 3D interactive graphics, including functions modelled on base graphics (plot3d(), etc.) as well as functions for constructing representations of geometric objects (cube3d(), etc.). Output may be on screen using OpenGL, or to various standard 3D file formats including WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.


References in zbMATH (referenced in 60 articles )

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  1. Di Marzio, Marco; Fensore, Stefania; Lafratta, Giovanni; Taylor, Charles C.: The \textscnprotegpackage: nonparametric regression using local rotation matrices in R (2021)
  2. Michael C. Thrun, Quirin Stier: Fundamental clustering algorithms suite (2021) not zbMATH
  3. Pewsey, Arthur; García-Portugués, Eduardo: Recent advances in directional statistics (2021)
  4. Rozgonyi-Boissinot, Nikoletta; Buocz, Ildikó; Hatvani, István Gábor; Török, Ákos: Shear strength testing of consolidated claystones: breakpoint detection of shear stress versus shear displacement curves, a statistical approach (2021)
  5. Wang, Bruce; Sudijono, Timothy; Kirveslahti, Henry; Gao, Tingran; Boyer, Douglas M.; Mukherjee, Sayan; Crawford, Lorin: A statistical pipeline for identifying physical features that differentiate classes of 3D shapes (2021)
  6. Arsalane Chouaib Guidoum, Kamal Boukhetala: Performing Parallel Monte Carlo and Moment Equations Methods for Ito and Stratonovich Stochastic Differential Systems: R Package Sim.DiffProc (2020) not zbMATH
  7. Furlan, Claudia; Mortarino, Cinzia: Comparison among simultaneous confidence regions for nonlinear diffusion models (2020)
  8. Zhang, Ying-Ying; Xie, Yu-Han; Song, Wen-He; Zhou, Ming-Qin: The Bayes rule of the parameter in (0,1) under Zhang’s loss function with an application to the beta-binomial model (2020)
  9. Davies, Tilman M.; Lawson, Andrew B.: An evaluation of likelihood-based bandwidth selectors for spatial and spatiotemporal kernel estimates (2019)
  10. Liu, Xiaohui; Mosler, Karl; Mozharovskyi, Pavlo: Fast computation of Tukey trimmed regions and median in dimension (p > 2) (2019)
  11. Oleksii Pokotylo; Pavlo Mozharovskyi; Rainer Dyckerhoff: Depth and Depth-Based Classification with R Package ddalpha (2019) not zbMATH
  12. Polzehl, Jörg; Tabelow, Karsten: Magnetic resonance brain imaging. Modeling and data analysis using R (2019)
  13. Robert Geitner and Robby Fritzsch and Jürgen Popp and Thomas Bocklitz: corr2D: Implementation of Two-Dimensional Correlation Analysis in R (2019) not zbMATH
  14. Zhang, Ying-Ying; Wang, Ze-Yu; Duan, Zheng-Min; Mi, Wen: The empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior under Stein’s loss function (2019)
  15. Marius D. Pascariu; Maciej J. Dańko; Jonas Scholey; Silvia Rizzi: ungroup: An R package for efficient estimation of smooth distributions from coarsely binned data (2018) not zbMATH
  16. Zhang, Ying-Ying; Xie, Yu-Han; Song, Wen-He; Zhou, Ming-Qin: Three strings of inequalities among six Bayes estimators (2018)
  17. Härdle, Karl Wolfgang; Okhrin, Ostap; Okhrin, Yarema: Basic elements of computational statistics (2017)
  18. Peter E. DeWitt, Samantha MaWhinney, Nichole E. Carlson: cpr: An R Package For Finding Parsimonious B-Spline Regression Models via Control Polygon Reduction and Control Net Reduction (2017) arXiv
  19. Zhang, Ying-Ying; Zhou, Ming-Qin; Xie, Yu-Han; Song, Wen-He: The Bayes rule of the parameter in ((0,1)) under the power-log loss function with an application to the beta-binomial model (2017)
  20. Ekstrøm, Claus Thorn: The R primer (2016)

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