## Recently Published

##### multiple time series

state-space modeling

##### Document

unit 2: R

##### mapMastif

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

##### Unit 5

network design

##### GJAM vignette

Vignette for R package gjam

##### 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

gjamTime example used for Grenoble workshop 19 November

##### mastif

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