Nicholas Clark's repositories
physalia-forecasting-course
Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}
phylo_func_trends
Using phylogenetic and functional relationships to inform nonlinear trend estimates from long-term biodiversity data
pfilter_mvgam
Particle filtering code from original mvgam submission; not currently supported
rweekly.org
R Weekly
portal_VAR
R code to replicate analyses in Clark et al in review (Forecasting rodent population dynamics with dynamic Generalized Additive Models)
spatialepi_website
Github repository for our research group's website
GAMbler
The GAMbler blog
nmixture_mvgam
Dynamic GAM N-mixture simulations
marginaleffects
R package to compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 80 classes of statistical models. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference.
EFI_seminar
A seminar on ecological forecasting with the {mvgam} R 📦 for the Ecological Forecasting Initiative
QUT_seminar
A seminar on ecological forecasting with the {mvgam} R 📦
CV
Nicholas J Clark curriculum vitae
mvforecast
Tools to fit, interrogate and forecast multivariate statistical and machine learning timeseries models
Mediterranean-Fishes-MRF
R code to reproduce analyses in "Rapid winter warming could disrupt coastal marine fish community structure" (Clark et al, Nature Climate Change, 2020)
BBS.occurrences
Effects of landcover on avian community ecology
website
Hugo source code for the QERC website
EF_Activities
Hands-on activities associated with the Ecological Forecasting book and graduate class
malaviR
An R interface to MalAvi
LandcoverMODIS
Functions for downloading and processing MODIS Land_Cover_Type_1 data
TwitterParasites
Track public mentions of ectoparasites
rethinking
Statistical Rethinking course and book package
twitter-blogdown
Fetching Twitter messages daily and turn them into a website based on blogdown
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.