nlme

R package nlme: Linear and Nonlinear Mixed Effects Models , Fit and compare Gaussian linear and nonlinear mixed-effects models. (Source: http://cran.r-project.org/web/packages)

Keywords for this software

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References in zbMATH (referenced in 111 articles )

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  1. Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM (2020) not zbMATH
  2. Emma C. Martin, Alessandro Gasparini, Michael J. Crowther: merlin: An R package for Mixed Effects Regression for Linear, Nonlinear and User-defined models (2020) arXiv
  3. Rainer Hirk, Kurt Hornik, Laura Vana: mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020) not zbMATH
  4. Ritz, Christian; Jensen, Signe Marie; Gerhard, Daniel; Streibig, Jens Carl: Dose-response analysis using R (2020)
  5. Yang, Yu-Chen; Lin, Tsung-I; Castro, Luis M.; Wang, Wan-Lun: Extending finite mixtures of (t) linear mixed-effects models with concomitant covariates (2020)
  6. Addo, E. jun.; Chanda, E. K.; Metcalfe, A. V.: Spatial pair-copula model of grade for an anisotropic gold deposit (2019)
  7. Ann-Kristin Kreutzmann; Sören Pannier; Natalia Rojas-Perilla; Timo Schmid; Matthias Templ; Nikos Tzavidis: The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators (2019) not zbMATH
  8. Geraci, Marco: Modelling and estimation of nonlinear quantile regression with clustered data (2019)
  9. Graf, Monique; Marín, J. Miguel; Molina, Isabel: A generalized mixed model for skewed distributions applied to small area estimation (2019)
  10. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  11. Lee, Wonyul; Miranda, Michelle F.; Rausch, Philip; Baladandayuthapani, Veerabhadran; Fazio, Massimo; Downs, J. Crawford; Morris, Jeffrey S.: Bayesian semiparametric functional mixed models for serially correlated functional data, with application to glaucoma data (2019)
  12. Mies, Fabian; Steland, Ansgar: Nonparametric Gaussian inference for stable processes (2019)
  13. Singer, Julio M.; Rocha, Francisco M. M.; André, Carmen D. S.; Zerbini, Talita: Fitting mixed models to messy longitudinal data: a case study involving estimation of post mortem intervals (2019)
  14. Tekbudak, Merve Yasemin; Alfaro-Córdoba, Marcela; Maity, Arnab; Staicu, Ana-Maria: A comparison of testing methods in scalar-on-function regression (2019)
  15. Wang, Wan-Lun: Mixture of multivariate (t) nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values (2019)
  16. Brown, Jonathon D.: Advanced statistics for the behavioral sciences. A computational approach with R (2018)
  17. Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
  18. Lars Rönnegård, Xia Shen, Moudud Alam: hglm: A Package for Fitting Hierarchical Generalized Linear Models (2018) not zbMATH
  19. Odile Sauzet; Jannik Rehse; Janne Helene Breiding: DistdichoR a R Package for the distributional dichotomisation of continuous outcomes (2018) arXiv
  20. Wagner Bonat: Multiple Response Variables Regression Models in R: The mcglm Package (2018) not zbMATH

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