Nonlinear Regression with R

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Nonlinear Regression Models and Applications in Agricultural Research (2015, methodology): “Depending on the structure of the models, different statistical criteria can be used to find the best model: F test, Akaike information criterion (AIC), Bayesian information criterion (BIC), or the likelihood ratio test (Zucchini, 2000; Burnham and Anderson, 2002; Hoffmann, 2005; Ritz and Streibig, ”
Plantecophys - An R Package for Analysing and Modelling Leaf Gas Exchange Data (2015, methodology): “The fit of the FvCB model to data is achieved with non-linear least squares, and standard errors of the parameters are estimated with standard methods (nls function in base R, see [38]).”
Forest Growth and Yield Modeling (2011, background): “Unfortunately, problems with ill-conditioning in the correlation matrix of the predictor variables also carries into nonlinear models (Seber and Wild, 2003), and misspecification of nonlinear model forms can also lead to biased and inconsistent predictions (Kmenta, 1997; Ritz and Streibig, 2008).”
Copula-Based Regression Estimation and Inference (2013, background): “For details, we refer to Ritz and Streibig (2008).”
Dose-Response Analysis Using R (2015, methodology): “One approach is to use iteratively weighted least squares as is done in nls() , which is part of standard installation in R [30].”