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jimclark

James S Clark

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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.
mastif
mastif uses seed counts from seed traps to estimate seed productivity by trees and seed dispersion. Attributes of individual trees and their local environments explain their differences in fecundity. Inference uses information on locations of trees and seed traps, and covariates that could explain source strength.
mast
mast
Seed and pollen production and dispersal: inference and prediction
Generalized joint attribute modeling - gjam
description of gjam package on CRAN
Generalized joint attribute modeling - gjam
gjam R package vignette
gjam
generalized joint attribute modeling: Analysis of multivariate data that are combinations of presence-absence, ordinal, continuous, discrete, composition, zero-inflated, and censored. gjam models the joint distribution and provides inference on sensitivity to input variables, correlations between responses, model selection, and prediction.