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