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 374 articles , 1 standard article )

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  1. Elster, Clemens; Wübbeler, Gerd: Bayesian inference using a noninformative prior for linear Gaussian random coefficient regression with inhomogeneous within-class variances (2017)
  2. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  3. Kühnel, Line; Sommer, Stefan; Pai, Akshay; Raket, Lars Lau: Most likely separation of intensity and warping effects in image registration (2017)
  4. Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant: Boosted multivariate trees for longitudinal data (2017)
  5. Wood, Simon N.: Generalized additive models. An introduction with R. (2017)
  6. Costa Mota Paraíba, Carolina; Ribeiro Diniz, Carlos Alberto: Randomly truncated nonlinear mixed-effects models (2016)
  7. Faraway, Julian J.: Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. (2016)
  8. Furlan, Sarah; Agnoli, Franca; Reyna, Valerie F.: Intuition and analytic processes in probabilistic reasoning: the role of time pressure (2016) MathEduc
  9. Heinzl, Felix; Tutz, Gerhard: Additive mixed models with approximate Dirichlet process mixtures: the EM approach (2016)
  10. Katahira, Kentaro: How hierarchical models improve point estimates of model parameters at the individual level (2016)
  11. Kogan, Clark; Kalachev, Leonid; Van Dongen, Hans P.A.: Prediction accuracy in multivariate repeated-measures Bayesian forecasting models with examples drawn from research on sleep and circadian rhythms (2016)
  12. Kohli, Nidhi; Harring, Jeffrey R.; Zopluoglu, Cengiz: A finite mixture of nonlinear random coefficient models for continuous repeated measures data (2016)
  13. Wang, Wei: Identifiability of covariance parameters in linear mixed effects models (2016)
  14. Chan, Ngai Hang; Wong, Hoi Ying: Simulation techniques in financial risk management (2015)
  15. Faraway, Julian J.: Linear models with R (2015)
  16. Francis, Brian; Liu, Jiayi: Modelling escalation in crime seriousness: a latent variable approach (2015)
  17. Harrell, Frank E. jun.: Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis (2015)
  18. Heiberger, Richard M.; Holland, Burt: Statistical analysis and data display. An intermediate course with examples in R (2015)
  19. Kreutz, Clemens; Raue, Andreas; Timmer, Jens: Statistics for model calibration (2015)
  20. Pek, Jolynn; Wu, Hao: Profile likelihood-based confidence intervals and regions for structural equation models (2015)

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