Generalized additive (mixed) models, some of their extensions and 
             other generalized ridge regression with multiple smoothing 
             parameter estimation by (Restricted) Marginal Likelihood, 
             Generalized Cross Validation and similar, or using iterated 
             nested Laplace approximation for fully Bayesian inference. See 
             Wood (2017) <doi:10.1201/9781315370279> for an overview. 
             Includes a gam() function, a wide variety of smoothers, 'JAGS' 
             support and distributions beyond the exponential family.

methods, stats, graphics, Matrix, splines, utils

Linear and Nonlinear Mixed Effects Models

c(person("José", "Pinheiro", role = "aut", comment = "S version"),
             person("Douglas", "Bates", role = "aut", comment = "up to 2007"),
             person("Saikat", "DebRoy", role = "ctb", comment = "up to 2002"),
             person("Deepayan", "Sarkar", role = "ctb", comment = "up to 2005"),
             person("EISPACK authors", role = "ctb", comment = "src/rs.f"),
	     person("Siem", "Heisterkamp", role = "ctb", comment = "Author fixed sigma"),
             person("Bert", "Van Willigen",role = "ctb", comment = "Programmer fixed sigma"),
             person("Johannes", "Ranke", role = "ctb", comment = "varConstProp()"),
	     person("R Core Team", email = "R-core@R-project.org",
                    role = c("aut", "cre")))

Fit and compare Gaussian linear and nonlinear mixed-effects models.

José Pinheiro [aut] (S version),
  Douglas Bates [aut] (up to 2007),
  Saikat DebRoy [ctb] (up to 2002),
  Deepayan Sarkar [ctb] (up to 2005),
  EISPACK authors [ctb] (src/rs.f),
  Siem Heisterkamp [ctb] (Author fixed sigma),
  Bert Van Willigen [ctb] (Programmer fixed sigma),
  Johannes Ranke [ctb] (varConstProp()),
  R Core Team [aut, cre]

c(
    person("Alboukadel", "Kassambara", role = c("aut", "cre"), email = "alboukadel.kassambara@gmail.com"))

The 'ggplot2' package is excellent and flexible for elegant data
    visualization in R. However the default generated plots requires some formatting
    before we can send them for publication. Furthermore, to customize a 'ggplot',
    the syntax is opaque and this raises the level of difficulty for researchers
    with no advanced R programming skills. 'ggpubr' provides some easy-to-use
    functions for creating and customizing 'ggplot2'- based publication ready plots.