Increased efficiency of dynamic memory allocation to handle very large problems, eliminating the need to recompile the NONMEM® program for unusually large problemsĪdditional features, some new to NONMEM 7. Parallel computing of a single problem over multiple cores or computers, for estimation, covariance assessment, simulation, nonparametric analysis and posthoc parameter and weighted residual diagnostic evaluation, significantly reducing completion timeģ. Markov-Chain Monte Carlo Bayesian Analysis (BAYES, NUTS)Ģ.Stochastic Approximation Expectation-Maximization (SAEM).Importance Sampling Expectation-Maximization (IMP).First Order Conditional Estimation (FOCE) Therefore, this study proposed an advanced adaptive weighted phase optimization algorithm (AWPOA).Population analysis methods available for handling a variety of PK/PD population analysis problems: Therefore, it was recommended that the clinical dose of artemisinin for children should be based.
NONMEM typically tries to optimize these, but they can be. Optimization of a single goal in practical applications often can not meet the requirements, then introduces the concept of multi-objective genetic algorithm, the coefficient weighted method, VEGA, based on the Pareto rank MOGA is introduced, given the optimization process. Body weight clearly affected CL/F, V/F, and other parameters. The latest release of NONMEM ® includes these enhancements and more.ġ. the fixed effects, like the influence of weight on clearance in the example above.