bayesTPC: Bayesian inference for Thermal Performance Curves in R

1. Reliable predictions of arthropod responses to climatic warming are important because many of these species perform important roles that can directly impact human society. 2. Thermal performance curves (TPCs) provide useful information on the physiological constraints that limit the capacity of temperature-sensitive organisms (like arthropods) to exist and grow. 3. NLS pipelines for fitting TPCs are widely available, but these approaches rely on assumptions that can yield unreliable parameter estimates. 4. We present bayesTPC, an R package for fitting TPCs to trait responses using the "nimble" language and machinery as the underlying engine for Markov Chain Monte Carlo. bayesTPC aims to support the adoption of Bayesian approaches in thermal physiology, and promote TPC fitting that adequately quantifies uncertainty.