James S Clark

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16. traits
15. Spatial models
14. MV time series
multiple time series
state-space modeling
12. Time series
4. Drifting continents
Duke Forest Data
Day 2
3. FIA
CO2 vignette
unit 2: R
unit 1, 2024
25. Intern Court of Justice
23. Debate preparation
21. Human population growth
19. Fisheries debate prep
18. Fisheries background
final paper
17. Fisheries trends
12. eBird data
14. ESA debate
13. Debate prep, ESA
12. eBird data
10. Endangered species act
9. Megaherbivores
8. Megaherbivores
4. Extreme weather
4. Extreme weather
seed production in North America and Europe
monthly climate data
available on clark.unix
mastif vignette
Using the MASTIF package for tree-fecundity analysis
demographic correlation
compares intra- and interspecific correlation between trees in growth and fecundity
1. Intro
19. Debate preparation
18. Debate background
15. Space
14. Multivariate
15. Fisheries background
13. Time series biogeochem
12. State-space
12 Time series
12. Bird trends synthesis
semester project
11. Uncertainty
11. ebird data
10. MCMC
10. Trends in BBS data
8. Probability applications
9. Discussion
8. BBS data
7. Probability foundations
Document6. Growth analysis
7. Climate debate
Unit 5
network design
unit 4 EDA with BBS
Intro to R
unit 1
Unit 3
unit 2
Intro to R
Introduction to R
Unit 1
R fundamentals for 89S
4. Extreme weather
Unit 3
Unit 2
8. Probability applications
3. EDA
15. Traits
GJAM vignette
Vignette for R package gjam
16. Traits
15. Spatial models
longline debate
17. Fisheries trends
final paper
14 Multivariate models
17 Seasonal trends
16. Fisheries EDA
15. Fisheries on the brink
12. State space models
13 Projects
13ESA debate
12. Uncertainty
Semester project
10: MCMC applications
11 Declining wood thrush
ebird science
9 BBS declines discussion
7. Probability foundations
Big oil vs NY city debate
EDA with forest inventories
Extreme weather discussion
4. Extreme weather
3. The evidence
Mauna Loa data
Getting started with R
Intro to ENV/BIO665
Bayesian analysis for environmental data
Climate change and biodiversity in the big-data era
What are the changes happening now and where they are leading us? This course combines key topics in climate change, biodiversity, and big data, examining scientific issues, their importance for the public at large, and how well we understand them. 89S courses focus on student discussions. In this case, discussions consider a combination of scientific literature, contemporary media, and analysis of data. Our first meeting provides logistics for the class and introduces the software package R. This vignette introduces issues in the media and courts, the promise/limitations of big data, and challenges of translating data to basic concepts like risk and cost.
Getting started with R
This brief overview introduces some of the key elements, intended for those with limited experience in R. It introduces R objects, basic operations, storage modes, and functions. Concepts include vectorization.
gjamTime: Generalized Joint Attribute Modeling for Dynamic Data
Generalized joint attribute modeling for dynamic species data, where there are interactions between species and their environment.
gjamTime example used for Grenoble workshop 19 November
Mast Inference and Forecasting (mastif) uses seed counts from seed traps to estimate seed productivity by trees and seed dispersion. Attributes of individual trees and their local environments could explain their differences in fecundity. Inference requires information on locations of trees and seed traps, and predictors (covariates and factors) that could explain source strength.
Generalized joint attribute modeling - gjam
description of gjam package on CRAN