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 630 articles , 1 standard article )

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  1. Hennig, Christian: An empirical comparison and characterisation of nine popular clustering methods (2022)
  2. Patriota, Alexandre Galvão; Alves, Jônatas de Oliveira: A monotone frequentist measure of evidence for testing variance components in linear mixed models (2022)
  3. Tourani-Farani, Fahimeh; Kazemi, Iraj: Transformed mixed-effects modeling of correlated bounded and positive data with a novel multivariate generalized Johnson distribution (2022)
  4. West, P. W.; Ratkowsky, D. A.: Simulation studies comparing fixed effect and mixed models in data sets with multiple measurements in individual sampling units (2022)
  5. Abbaszadeh, D.; Tavassoli Kajani, M.; Momeni, M.; Zahraei, M.; Maleki, M.: Solving fractional Fredholm integro-differential equations using Legendre wavelets (2021)
  6. Burgard, J. P.; Krause, J.; Münnich, R.: An elastic net penalized small area model combining unit- and area-level data for regional hypertension prevalence estimation (2021)
  7. Casa, Alessandro; Bouveyron, Charles; Erosheva, Elena; Menardi, Giovanna: Co-clustering of time-dependent data via the shape invariant model (2021)
  8. Castellano, Rosella; Mancinelli, Marco; Ponsi, Giorgia; Tieri, Gaetano: What if versus probabilistic scenarios: a neuroscientific analysis (2021)
  9. Delattre, Maud: A review on asymptotic inference in stochastic differential equations with mixed effects (2021)
  10. Ghalani, Mohammad Reza; Zadkarami, Mohammad Reza: Investigation of covariance structures in modelling longitudinal ordinal responses with skew normal random effect (2021)
  11. Ghosh, Abhik; Thoresen, Magne: Consistent fixed-effects selection in ultrahigh-dimensional linear mixed models with error-covariate endogeneity (2021)
  12. Hančová, Martina; Gajdoš, Andrej; Hanč, Jozef; Vozáriková, Gabriela: Estimating variances in time series kriging using convex optimization and empirical BLUPs (2021)
  13. Hoff, Peter: Additive and multiplicative effects network models (2021)
  14. Larbi, Yassine Ou; El Halimi, Rachid; Akharif, Abdelhadi; Mellouk, Amal: Optimal tests for random effects in linear mixed models (2021)
  15. Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria: Evaluating class and school effects on the joint student achievements in different subjects: a bivariate semiparametric model with random coefficients (2021)
  16. Mauff, Katya; Erler, Nicole S.; Kardys, Isabella; Rizopoulos, Dimitris: Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model (2021)
  17. Menictas, M.; Nolan, T. H.; Simpson, D. G.; Wand, M. P.: Streamlined variational inference for higher level group-specific curve models (2021)
  18. M. Helena Gonçalves, M. Salomé Cabral: cold: An R Package for the Analysis of Count Longitudinal Data (2021) not zbMATH
  19. Mota, Alex; Milani, Eder A.; Calsavara, Vinicius F.; Tomazella, Vera L. D.; Leão, Jeremias; Ramos, Pedro L.; Ferreira, Paulo H.; Louzada, Francisco: Weighted Lindley frailty model: estimation and application to lung cancer data (2021)
  20. Schumacher, Fernanda L.; Dey, Dipak K.; Lachos, Victor H.: Approximate inferences for nonlinear mixed effects models with scale mixtures of skew-normal distributions (2021)

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