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 504 articles , 1 standard article )

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  1. Flores-Agreda, Daniel; Cantoni, Eva: Bootstrap estimation of uncertainty in prediction for generalized linear mixed models (2019)
  2. García, Oscar: Estimating reducible stochastic differential equations by conversion to a least-squares problem (2019)
  3. Riazoshams, Hossein; Midi, Habshah; Ghilagaber, Gebrenegus: Robust nonlinear regression: with applications using R (2019)
  4. Rizzo, Maria L.: Statistical computing with R (2019)
  5. Audigier, Vincent; White, Ian R.; Jolani, Shahab; Debray, Thomas P. A.; Quartagno, Matteo; Carpenter, James; van Buuren, Stef; Resche-Rigon, Matthieu: Multiple imputation for multilevel data with continuous and binary variables (2018)
  6. Bowman, Adrian W.: Big questions, informative data, excellent science (2018)
  7. Cederbaum, Jona; Scheipl, Fabian; Greven, Sonja: Fast symmetric additive covariance smoothing (2018)
  8. Grayling, Michael J.; Wason, James M. S.; Mander, Adrian P.: Group sequential crossover trial designs with strong control of the familywise error rate (2018)
  9. 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)
  10. Li, Xinmin; Su, Haiyan; Liang, Hua: Fiducial generalized (p)-values for testing zero-variance components in linear mixed-effects models (2018)
  11. Noy, Dominic; Menezes, Raquel: Parameter estimation of the linear phase correction model by hierarchical linear models (2018)
  12. Oliveira, Thiago de Paula; Hinde, John; Zocchi, Silvio Sandoval: Longitudinal concordance correlation function based on variance components: an application in fruit color analysis (2018)
  13. Valliant, Richard; Dever, Jill A.; Kreuter, Frauke: Practical tools for designing and weighting survey samples (2018)
  14. Yavuz, Fulya Gokalp; Arslan, Olcay: Linear mixed model with Laplace distribution (LLMM) (2018)
  15. Alam, M. Iftakhar; Bogacka, Barbara; Coad, D. Stephen: Pharmacokinetically guided optimum adaptive dose selection in early phase clinical trials (2017)
  16. 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 \textiteucalyptustrees (2017)
  17. 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)
  18. Elster, Clemens; Wübbeler, Gerd: Bayesian inference using a noninformative prior for linear Gaussian random coefficient regression with inhomogeneous within-class variances (2017)
  19. Ferreira, Dário; Ferreira, Sandra; Nunes, Célia; Fonseca, Miguel; Silva, Adilson; Mexia, João T.: Estimation and incommutativity in mixed models (2017)
  20. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)

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