Module nonparametric 

Non-parametric algorithm implementations

This module contains the trait definition and implementations for non-parametric population pharmacokinetic algorithms. These algorithms estimate the population distribution as a discrete set of support points with associated probability weights.

Available Algorithms

• NPAG: Non-Parametric Adaptive Grid
• NPOD: Non-Parametric Optimal Design
• NPMAP: Maximum a posteriori reweighting

Algorithm Selection

Use the NonParametricAlgorithm enum to select and configure an algorithm. Each variant wraps its algorithm-specific configuration struct (e.g. NpagConfig). The internal execution state used while fitting implements the NonParametricRunner trait, which defines the common interface for initialization, estimation, condensation, expansion, and convergence evaluation.

Re-exports

pub use ncnpag::NCNPAG;

pub use npag::NPAG;

pub use npmap::NPMAP;

pub use npod::NPOD;

pub use error_optim::ErrorOptimConfig;

pub use ncnpag::NcnpagConfig;

pub use npag::NpagConfig;

pub use npmap::NpmapConfig;

pub use npod::NpodConfig;

pub use controller::CycleFlow;

pub use controller::FitController;

pub use controller::FitObserver;

Modules

controller

Step through a fit cycle by cycle, rather than running it all at once.

error_optim

Error-model factor optimization used by non-parametric algorithms.

ncnpag

npag

npmap

npod

Enums

NonParametricAlgorithm

The non-parametric algorithms supported by PMcore.