Menu
  • About & Contact
  • Feedback
  • Contribute
  • Help
  • zbMATH

swMATH

swmath-logo
  • Search
  • Advanced search
  • Browse
  • browse software by name
  • browse software by keywords
  • browse software by MSC
  • browse software by types

gamm4

R package gamm4: Generalized additive mixed models using mgcv and lme4. Fit generalized additive mixed models via a version of mgcv’s gamm function, using lme4 for estimation.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

  • optimal bandwidth selections
  • penalized least squares
  • R package
  • discrete survival
  • sparse sampling design
  • hierarchical structured data sets
  • generalized linear and additive models
  • functional principal components
  • longitudinal data
  • linear models
  • smoothing
  • Cholesky decomposition
  • heterogeneity
  • analysis of variance
  • binomial data
  • mixed models
  • data visualisation in R
  • fixed effects
  • bootstrap resampling methods
  • discrete kernel
  • count regression function
  • Journal of Statistical Software
  • variable selection
  • homogenous variance heterogeneous variance
  • R
  • wood formation
  • linear mixed models
  • sparse matrix methods
  • Lasso

  • URL: cran.r-project.org/web...
  • Code
  • InternetArchive
  • Manual: cran.r-project.org/web...
  • Authors: Simon Wood, Fabian Scheipl
  • Dependencies: R

  • Add information on this software.


  • Related software:
  • lme4
  • R
  • mgcv
  • nlme
  • lattice
  • Matrix
  • MASS (R)
  • glmmLasso
  • nlmeU
  • survival
  • Show more...
  • NLopt
  • influence.ME
  • SuiteSparse
  • Eigen
  • gamair
  • cmrutils
  • pedigreem
  • longRPart
  • lmm
  • fda (R)
  • Show less...

References in zbMATH (referenced in 5 articles )

Showing results 1 to 5 of 5.
y Sorted by year (citations)

  1. Gertheiss, Jan; Goldsmith, Jeff; Staicu, Ana-Maria: A note on modeling sparse exponential-family functional response curves (2017)
  2. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  3. Cuny, Henri E.; Kiessé, Tristan Senga: On modeling wood formation using parametric and semiparametric regressions for count data (2016)
  4. Douglas Bates; Martin Mächler; Ben Bolker; Steve Walker: Fitting Linear Mixed-Effects Models Using lme4 (2015) not zbMATH
  5. Gałecki, Andrzej; Burzykowski, Tomasz: Linear mixed-effects models using R. A step-by-step approach (2013)

  • Article statistics & filter:

  • Search for articles
  • MSC classification / top
    • Top MSC classes
      • 62 Statistics

  • Publication year
    • 2010 - today
    • 2005 - 2009
    • 2000 - 2004
    • before 2000
  • Terms & Conditions
  • Imprint
  • Privacy Policy