Name: Dan Ovando
Type: User
Company: Inter-American Tropical Tuna Commission
Bio: Quantitative scientist studying fisheries, economics, and data science to improve marine resource management
Twitter: danovand0
Location: San Diego, CA
Blog: http://www.weirdfishes.blog
Dan Ovando's Projects
Repository to explore enhanced stock assessment regressions
A History and Evaluation of Catch-Only Stock Assessment Models
Example models for Stan
R scripts and data files for the Fantasy Football Analytics website
UW Workshop
Code repository for Fall 2015 FISH 558
Compile and harmonize catch rates from fishery-independent surveys of fish populations
A manual on the collection and interpretation of basic fishery management data
A generalized simulation framework for testing management strategies
Estimate fish traits for all marine fish species globally
An interactive application for visualizing fishery data
Storage for Galapagos assessment code
Code and data to run tradeoff analysis and marine spatial planning for Galapagos
An R Markdown template using the bookdown package for preparing a PhD thesis at the University of California Santa Barbara
A 2-box, generalized management strategy evaluation framework that allows time-varying processes.
Tool for geostatistical analysis of survey data, for use when estimating an index of abundance
Scrape Yahoo Fantasy Baseball player page for statistics.
Color palettes inspired by album covers
Intro to GitHub Workshop
2-day workshop on Generalized Linear Mixed-Effects Modeling in R
Code respostiry for the Packard/Etc. project creating a unified global fishery database and estimating current status, future status, and recovery trajectories for these fisheries.
Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo
Run Global Upside Model in the manner of Costello et al 2016
A ported theme with some extras for the Hugo static website engine
Exploration of the effect of hyperallometry in MPAs
Analysis of indonesian blue swimming crab stocks
An introductory eco-data science course for R markdown
Introduction to Python - EcoDataScience