drda

drda performs nonlinear dose-response model fitting and statistical evaluation in R to support analysis in pharmacology, toxicology, epidemiology, and drug sensitivity screening.

Key Features:

• Model fitting by least squares: Fits dose-response models using the method of least squares with a strategic choice of starting points to improve accuracy and convergence.
• Goodness-of-fit tests and model selection: Provides goodness-of-fit testing and model selection procedures to assess model adequacy and choose among competing dose-response models.
• Advanced optimization techniques: Implements the Newton method with a trust-region approach using analytical gradients and Hessian matrices for parameter estimation.

Scientific Applications:

• Pharmacology: Characterizes drug effect curves and estimates potency and efficacy parameters from dose-response experiments.
• Toxicology: Quantifies toxic responses across dose ranges and supports toxicological assessment via dose-response modeling.
• Epidemiology: Analyzes exposure–response relationships in population-level dose-response studies.
• Drug sensitivity screening: Fits and evaluates dose-response models for large-scale drug sensitivity screening datasets.

Methodology:

Computational methods explicitly include least squares dose-response fitting with strategic starting points, Newton optimization with a trust-region framework using analytical gradients and Hessians, plus goodness-of-fit testing and model selection.