S-PLUS

S-PLUS is a powerful environment for statistical and graphical analysis of data. It provides the tools to implement many standard and modern statistical methods made possible by the widespread availability of workstations having good graphics and computational capabilities.


References in zbMATH (referenced in 574 articles , 1 standard article )

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  1. Argiento, Raffaele; Cremaschi, Andrea; Vannucci, Marina: Hierarchical normalized completely random measures to cluster grouped data (2020)
  2. Cunen, Céline; Walløe, Lars; Hjort, Nils Lid: Focused model selection for linear mixed models with an application to whale ecology (2020)
  3. Everitt, Brian S.: A handbook of statistical analyses using S-PLUS (2020)
  4. Fuino, Michel; Wagner, Joël: Duration of long-term care: socio-economic factors, type of care interactions and evolution (2020)
  5. Galarza, Christian E.; Castro, Luis M.; Louzada, Francisco; Lachos, Victor H.: Quantile regression for nonlinear mixed effects models: a likelihood based perspective (2020)
  6. Javeed, Aurya; Hooker, Giles: Timing observations of diffusions (2020)
  7. Pullenayegum, Eleanor M.: Meeting the assumptions of inverse-intensity weighting for longitudinal data subject to irregular follow-up: suggestions for the design and analysis of clinic-based cohort studies (2020)
  8. Reynolds, Angus; Kvam, Peter D.; Osth, Adam F.; Heathcote, Andrew: Correlated racing evidence accumulator models (2020)
  9. Roustant, Olivier; Padonou, Espéran; Deville, Yves; Clément, Aloïs; Perrin, Guillaume; Giorla, Jean; Wynn, Henry: Group kernels for Gaussian process metamodels with categorical inputs (2020)
  10. Xu, Ancha; Wang, You-Gan; Zheng, Shurong; Cai, Fengjing: Bias reduction in the two-stage method for degradation data analysis (2020)
  11. Baey, Charlotte; Cournède, Paul-Henry; Kuhn, Estelle: Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models (2019)
  12. Conde-Amboage, Mercedes; Sánchez-Sellero, César: A plug-in bandwidth selector for nonparametric quantile regression (2019)
  13. Das, Sumonkanti; Rahman, Azizur; Ahamed, Ashraf; Rahman, Sabbir Tahmidur: Multi-level models can benefit from minimizing higher-order variations: an illustration using child malnutrition data (2019)
  14. Flores-Agreda, Daniel; Cantoni, Eva: Bootstrap estimation of uncertainty in prediction for generalized linear mixed models (2019)
  15. Fu, Liyong; Wang, Mingliang; Wang, Zuoheng; Song, Xinyu; Tang, Shouzheng: Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first-order conditional linearization and sequential quadratic programming (2019)
  16. García, Oscar: Estimating reducible stochastic differential equations by conversion to a least-squares problem (2019)
  17. Geraci, Marco: Modelling and estimation of nonlinear quantile regression with clustered data (2019)
  18. Gerhard Kurz; Igor Gilitschenski; Florian Pfaff; Lukas Drude; Uwe Hanebeck; Reinhold Haeb-Umbach; Roland Siegwart: Directional Statistics and Filtering Using libDirectional (2019) not zbMATH
  19. Heck, Daniel W.: Accounting for estimation uncertainty and shrinkage in Bayesian within-subject intervals: a comment on Nathoo, Kilshaw, and Masson (2018) (2019)
  20. Jensen, Melanie A.; Wang, Ying-Ying; Lai, Samuel K.; Forest, M. Gregory; McKinley, Scott A.: Antibody-mediated immobilization of virions in mucus (2019)

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