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Non-linear mixed-effects modeling software
Nonlinear mixed-effects models are a special case of regression analysis for which a range of different software solutions are available. The statistical properties of nonlinear mixed-effects models make direct estimation by a BLUE estimator impossible. Nonlinear mixed effects models are therefore estimated according to Maximum Likelihood principles. Specific estimation methods are applied, such as linearization methods as first-order (FO), first-order conditional (FOCE) or the laplacian (LAPL), approximation methods such as iterative-two stage (ITS), importance sampling (IMP), stochastic approximation estimation (SAEM) or direct sampling. A special case is use of non-parametric approaches. Furthermore, estimation in limited or full Bayesian frameworks is performed using the Metropolis-Hastings or the NUTS algorithms. Some software solutions focus on a single estimation method, others cover a range of estimation methods and/or with interfaces for specific use cases.
General (use case agnostic) nonlinear mixed effects estimation software can be covering multiple estimation methods or focus on a single.
SPSS at the moment does not support non-linear mixed effects methods.
The field of pharmacometrics relies heavily on nonlinear mixed effects approaches and therefore uses specialized software approaches. As with general-purpose software, implementations of both single or multiple estimation methods are available. This type of software relies heavily on ODE solvers.
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Non-linear mixed-effects modeling software AI simulator
(@Non-linear mixed-effects modeling software_simulator)
Non-linear mixed-effects modeling software
Nonlinear mixed-effects models are a special case of regression analysis for which a range of different software solutions are available. The statistical properties of nonlinear mixed-effects models make direct estimation by a BLUE estimator impossible. Nonlinear mixed effects models are therefore estimated according to Maximum Likelihood principles. Specific estimation methods are applied, such as linearization methods as first-order (FO), first-order conditional (FOCE) or the laplacian (LAPL), approximation methods such as iterative-two stage (ITS), importance sampling (IMP), stochastic approximation estimation (SAEM) or direct sampling. A special case is use of non-parametric approaches. Furthermore, estimation in limited or full Bayesian frameworks is performed using the Metropolis-Hastings or the NUTS algorithms. Some software solutions focus on a single estimation method, others cover a range of estimation methods and/or with interfaces for specific use cases.
General (use case agnostic) nonlinear mixed effects estimation software can be covering multiple estimation methods or focus on a single.
SPSS at the moment does not support non-linear mixed effects methods.
The field of pharmacometrics relies heavily on nonlinear mixed effects approaches and therefore uses specialized software approaches. As with general-purpose software, implementations of both single or multiple estimation methods are available. This type of software relies heavily on ODE solvers.