GSL Multi-Start Nonlinear Least-Squares Fitting
Calculate variance-covariance matrix
Anova tables
Extract model coefficients
Confidence interval for model parameters
Confidence intervals for derived parameters
Model summary
Confidence intervals for derived parameters
Model deviance
Residual degrees-of-freedom
Extract model fitted values
Extract model formula
GSL Nonlinear Least Squares fitting
Tunable Nonlinear Least Squares iteration parameters
GSL Large-scale Nonlinear Least Squares fitting
Calculate leverage values
Extract model log-likelihood
Available NLS test problems
Retrieve an NLS test problem
Extract the number of observations
Calculate model predicted values
Extract model residuals
Residual standard deviation
An R interface to nonlinear least-squares optimization with the GNU Scientific Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078). The available trust region methods include the Levenberg-Marquardt algorithm with and without geodesic acceleration, the Steihaug-Toint conjugate gradient algorithm for large systems and several variants of Powell's dogleg algorithm. Multi-start optimization based on quasi-random samples is implemented using a modified version of the algorithm in Hickernell and Yuan (1997, OR Transactions). Bindings are provided to tune a number of parameters affecting the low-level aspects of the trust region algorithms. The interface mimics R's nls() function and returns model objects inheriting from the same class.