R Package:  Mast Inference and Forecasting (MASTIF)MASTIF probabilistically estimates the seed production and dispersal of trees given seed trap data and tree locations. The model builds on the rich literature of seed dispersal models that employ a bivariate Student’s t (2Dt) by accommodating for uncertainty in seed assignment, modeling maturition status for unknown trees, and explicitly describing the space-time covariance structure of seed production between trees. Accepted manuscript can be found here, and supplementary material can be found here.
After downloading here, install it by calling the following in R:
install.packages('mastif_1.0.tar.gz',repos=NULL, type='source') library('mastif')
Help and documentation here:
R Package:  Generalized Joint Attribute Model (GJAM)GJAM was developed to resolve some of the main challenges for ecological forecasting with biodiversity data. i) data are multivariate, ii) they are observed on different scales, and iii) they are mostly zeros. Probabilistic inference in GJAM admits data with disparate scales and massive zero inflation. That is, data are often collected in different ways, and species observations are a rare occurrence. By avoiding the distortion of scales implemented in generalized linear models, GJAM estimates can be interpreted on the scale of the observations, accounting for sample effort. Explanation of model structure can be found here. Original manuscript can be found here.
Install it by calling the following in R:
Get help and documentation by calling the following in R: