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cran
nlme:Linear and Nonlinear Mixed Effects Models
Fit and compare Gaussian linear and nonlinear mixed-effects models.
Maintained by R Core Team. Last updated 2 months ago.
6 stars 9.77 score 8.8k dependentsroustant
kergp:Gaussian Process Laboratory
Gaussian process regression with an emphasis on kernels. Quantitative and qualitative inputs are accepted. Some pre-defined kernels are available, such as radial or tensor-sum for quantitative inputs, and compound symmetry, low rank, group kernel for qualitative inputs. The user can define new kernels and composite kernels through a formula mechanism. Useful methods include parameter estimation by maximum likelihood, simulation, prediction and leave-one-out validation.
Maintained by Olivier Roustant. Last updated 4 months ago.
1 stars 1.83 score 67 scripts