To be useful, definitions of animal movement behavior (e.g. migration) should be quantitatively rigorous, flexible enough to accommodate variation in species biology (e.g. latitudinal vs elevational movement) and sufficiently general to allow comparison among different species. Recent studies have applied a model-driven approach to classifying and quantifying animal movement from global positioning system (GPS) location data. We improve upon these methods by 1) revising model structure to provide a simple biologically-defensible basis to reduce misclassification; 2) introducing a data-efficient tool that can be used to quantify and circumvent model sensitivity to starting location; and 3) illustrating how existing models can be adapted to describe short-distance migration, using elevational migration as an example. These improvements are included in ‘migrateR’, an open source R package that expands and automates model-driven classification and quantification of animal movement behavior. We demonstrate the software and these improved methods using GPS-collar location data from a long-distance migrant, elk Cervus elaphus, and a short-distance elevational migrant, Sierra Nevada bighorn sheep Ovis canadensis sierrae. We provide in-text example code and a supplementary script illustrating how default options can be revised to meet several common challenges in fitting movement models.