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 454 articles , 1 standard article )

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  1. Bowman, Adrian W.: Big questions, informative data, excellent science (2018)
  2. Cederbaum, Jona; Scheipl, Fabian; Greven, Sonja: Fast symmetric additive covariance smoothing (2018)
  3. Johnson, Leah R.; Gramacy, Robert B.; Cohen, Jeremy; Mordecai, Erin; Murdock, Courtney; Rohr, Jason; Ryan, Sadie J.; Stewart-Ibarra, Anna M.; Weikel, Daniel: Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study (2018)
  4. Li, Xinmin; Su, Haiyan; Liang, Hua: Fiducial generalized $p$-values for testing zero-variance components in linear mixed-effects models (2018)
  5. Oliveira, Thiago de Paula; Hinde, John; Zocchi, Silvio Sandoval: Longitudinal concordance correlation function based on variance components: an application in fruit color analysis (2018)
  6. Yavuz, Fulya Gokalp; Arslan, Olcay: Linear mixed model with Laplace distribution (LLMM) (2018)
  7. Alam, M. Iftakhar; Bogacka, Barbara; Coad, D. Stephen: Pharmacokinetically guided optimum adaptive dose selection in early phase clinical trials (2017)
  8. Capes, Hannah; Maillardet, Robert J.; Baker, Thomas G.; Weston, Christopher J.; McGuire, Don; Dumbrell, Ian C.; Robinson, Andrew P.: The allometric quarter-power scaling model and its applicability to grand fir and \iteucalyptus trees (2017)
  9. Davies, Vinny; Reeve, Richard; Harvey, William T.; Maree, Francois F.; Husmeier, Dirk: A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution (2017)
  10. Elster, Clemens; Wübbeler, Gerd: Bayesian inference using a noninformative prior for linear Gaussian random coefficient regression with inhomogeneous within-class variances (2017)
  11. Ferreira, Dário; Ferreira, Sandra; Nunes, Célia; Fonseca, Miguel; Silva, Adilson; Mexia, João T.: Estimation and incommutativity in mixed models (2017)
  12. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  13. Hämäläinen, Raimo P.; Leppänen, Ilkka: Cheap talk and cooperation in Stackelberg games (2017)
  14. Kühnel, Line; Sommer, Stefan; Pai, Akshay; Raket, Lars Lau: Most likely separation of intensity and warping effects in image registration (2017)
  15. Matusiak, Marek; de Koster, René; Saarinen, Jari: Utilizing individual picker skills to improve order batching in a warehouse (2017)
  16. Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant: Boosted multivariate trees for longitudinal data (2017)
  17. Pedersen, Morten Gram; Tagliavini, Alessia; Cortese, Giuliana; Riz, Michela; Montefusco, Francesco: Recent advances in mathematical modeling and statistical analysis of exocytosis in endocrine cells (2017)
  18. Sacco, Chiara; Viroli, Cinzia; Falchi, Mario: A statistical test for detecting parent-of-origin effects when parental information is missing (2017)
  19. Sun, Huihui: Testing for autocorrelation and random-effects in nonlinear mixed effects models based on $M$-estimation (2017)
  20. Wegener, Michael; Kauermann, Göran: Forecasting in nonlinear univariate time series using penalized splines (2017)

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