Skip to content
View nicholasjclark's full-sized avatar
85 followers · 39 following

Achievements

Achievement: Galaxy BrainAchievement: YOLOAchievement: QuickdrawAchievement: StarstruckAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx2

Achievements

Achievement: Galaxy BrainAchievement: YOLOAchievement: QuickdrawAchievement: StarstruckAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx2

Block or report nicholasjclark

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
nicholasjclark/README.md

I'm a Lecturer and research fellow at the University of Queensland's School of Veterinary Science. I am broadly interested in exploring new ways to (1) understand how ecological communities are formed and (2) predict how they will change over time.

Github

Github stats

I'm actively seeking Honours and PhD students to work in the areas of ecological forecasting, multivariate model evaluation, hierarchical GAMs and development of {mvgam}. Please reach out if you are interested

Pinned Loading

  1. mvgam mvgam Public

    {mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for multivariate modeling and forecasting

    R 112 14

  2. physalia-forecasting-course physalia-forecasting-course Public

    Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}

    HTML 36 10

  3. MRFcov MRFcov Public

    Markov random fields with covariates

    R 24 5

  4. portal_VAR portal_VAR Public

    R code to replicate analyses in Clark et al in review (Beyond single-species models: leveraging multispecies forecasts to navigate the dynamics of ecological predictability)

    R 11