Dose–response analysis is widely used in biological sciences and has application to a variety of risk assessment, bioassay, and calibration problems. In weed science, dose–response methodologies have typically relied on least squares estimation under the assumptions of normal, homoscedastic, and independent errors. Advances in computational abilities and available software, however, have given researchers more flexibility and choices for data analysis when these assumptions are not appropriate. This article will explore these techniques and demonstrate their use to provide researchers with an up-to-date set of tools necessary for analysis of dose–response problems. Demonstrations of the techniques are provided using a variety of data examples from weed science.