mgcv:Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation

By Simon N. Wood. 2012

735 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/359622c65e0d981b28aac8d569699bc0e2754b29

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Citations in the corpus

Structural Equation Models for Dealing With Spatial Confounding (2018, methodology): “In our simulation, the smoothing parameter λ is chosen using the generalized cross-validation criterion as implemented in the R (R Core Team 2016) package mgcv (Wood 2016) and explained by Wood (2006).”
40 Years of benthic community change on the Caribbean reefs of Curaçao and Bonaire: the rise of slimy cyanobacterial mats (2017, methodology): “Within R the following packages were used: vegan (Oksanen et al. 2016) and mgcv (Wood 2012).”
Soil pH mediates the balance between stochastic and deterministic assembly of bacteria (2018, unknown): “GAMM was fitted using the “gamm” function of “mgcv” R package [48].”
Quantile regression: A short story on how and why (2018, methodology): “1 Mean regression The conventional non-linear regression model estimating the association of age with the expected value of BMI was calculated with R-package mgcv (Wood, 2014).”
Water temperature modelling: comparison between the generalized additive model, logistic, residuals regression and linear regression models (2017, methodology): “The implementation of the GAM model was made with R software (Team 2014) provided with the package “mgcv” (minimizing generalized cross-validation; Wood 2013) version 1.7–22.”
Habitat models of wood-inhabiting fungi along a decay gradient of Norway spruce logs (2015, methodology): “Fungal response curves along a wood density gradient were estimated using the generalized additive mixed model (gamm) function in the package mgcv (Wood, 2013) of R.”
Visualization of Regression Models Using visreg (2017, background): “…(Therneau, 2012), robust linear models produced by rlm (Venables and Ripley, 2002), negative binomial models produces by glm.nb (Venables and Ripley, 2002), generalized additive models produced by gam (Wood, 2012), local regression models produced by loess and locfit (Loader, 2010), and many more.”
Generalized additive and index models with shape constraints (2014, methodology): “ we only compared SCMLE with SCAM and GAMIS. 17 SCAM can be viewed as a shape-restricted version of GAMIS. It is implemented in the Rpackage scam(Pya, 2012). GAMIS is implemented in the Rpackage mgcv(Wood, 2012), while MARS can be founded in the Rpackage mda(Hastie et al., 2011). The method of regre”
Most Likely Transformations (2015, methodology): “The additive Poisson model was fitted by mgcv (version 1.8-17, Wood 2017).”