Hey MarMam! We are excited to announce that the Xcertainty R package is now available on CRAN! Xcertainty is an easy-to-use R package that uses a Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements of marine mammals derived from drones. The easiest way to install Xcertainty is via CRAN: install.packages("Xcertainty") library(Xcertainty) Learn more: GitHub: https://github.com/MMI-CODEX/Xcertainty CODEX website: https://mmi.oregonstate.edu/centers-excellence/codex/software-hardware/xcert... CRAN: https://cran.r-project.org/web/packages/Xcertainty/index.html All morphological measurements derived using drone-based photogrammetry are susceptible to uncertainty. This uncertainty often varies by the drone system used. Thus, it is critical to incorporate photogrammetric uncertainty associated with measurements collected using different drones so that results are robust and comparable across studies and over long-term datasets. The Xcertainty R package makes this simple and easy by producing a predictive posterior distribution for each measurement. This posterior distribution can be summarized to describe the measurement (i.e., mean, median) and its associated uncertainty (i.e., standard deviation, credible intervals). The posterior distributions are also useful for making probabilistic statements, such as classifying maturity or diagnosing pregnancy if a proportion of the posterior distribution for a given measurement is greater than a specified threshold (e.g., if greater than 50% of posterior distribution for total body length is > 10 m, the individual is classified as mature). Xcertainty is based off of previously published Bayesian statistical models. In essence, measurements of known-sized objects (‘calibration objects’) collected at various altitudes are used as training data to predict morphological measurements (e.g., body length) and associated uncertainty of unknown-sized objects (e.g., whales). Xcertainty also includes functions that incorporate multiple measurements (body length and width) to estimate different body condition metrics (i.e., single widths, surface area, body volume, body area index) with associated uncertainty, as well as combine body length with age information to construct growth curves Cheers, KC Bierlich & Josh Hewitt KC (Kevin) Bierlich, PhD, MEM Assistant Professor Senior Research Center of Drone Excellence (CODEX<https://mmi.oregonstate.edu/centers-excellence/codex>) Marine Mammal Institute, Dept. of Fisheries, Wildlife, & Conservation Sciences, Oregon State University Pronouns: he, him, his kevin.bierlich@oregonstate.edu<mailto:kevin.bierlich@oregonstate.edu>