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glm2

R package glm2: Fitting Generalized Linear Models. Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.

Keywords for this software

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  • EM algorithm
  • R package
  • IRLS
  • additive models
  • generalized additive model
  • monotonic P-spline
  • image processing
  • semiconductor
  • risk difference
  • additive binomial model
  • sparse Poisson regression
  • yield prediction
  • adjacency-clustering
  • multiplicative models
  • semi-parametric regression
  • penalized splines
  • combinatorial optimization
  • single index model
  • over and under dispersion
  • R
  • relative risk regression
  • variance heterogeneity
  • multinomial-Poisson transformation
  • adaptive regularization
  • model selection
  • log-binomial model
  • Markov random field
  • parameter constraints
  • Journal of Statistical Software
  • time series

  • URL: cran.r-project.org/web...
  • Code
  • InternetArchive
  • Manual: cran.r-project.org/web...
  • Authors: Ian Marschner; Mark W. Donoghoe
  • Dependencies: R

  • Add information on this software.


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References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
y Sorted by year (citations)

  1. Guastavino, Sabrina; Benvenuto, Federico: A consistent and numerically efficient variable selection method for sparse Poisson regression with applications to learning and signal recovery (2019)
  2. Rasch, Dieter; Verdooren, Rob; Pilz, Jürgen: Applied statistics. Theory and problem solutions with R (2019)
  3. Spiegel, Elmar; Kneib, Thomas; Otto-Sobotka, Fabian: Generalized additive models with flexible response functions (2019)
  4. Chatla, Suneel Babu; Shmueli, Galit: Efficient estimation of COM-Poisson regression and a generalized additive model (2018)
  5. Hochbaum, Dorit S.; Liu, Sheng: Adjacency-clustering and its application for yield prediction in integrated circuit manufacturing (2018)
  6. Mark Donoghoe; Ian Marschner: logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model (2018) not zbMATH
  7. Donoghoe, Mark W.; Marschner, Ian C.: Stable computational methods for additive binomial models with application to adjusted risk differences (2014)
  8. Marschner, Ian C.: Combinatorial EM algorithms (2014)

  • Article statistics & filter:

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  • MSC classification / top
    • Top MSC classes
      • 62 Statistics
      • 65 Numerical analysis
      • 90 Optimization

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