person("Bruno", "Hérault", email = "bruno.herault@cirad.fr", role = c("aut")),

Imports: minpack.lm, raster, jsonlite, methods, proj4, graphics, stats,
utils, data.table (>= 1.9.8), rappdirs, sp
Suggests: knitr, rmarkdown, testthat, covr, httr, rvest, prettydoc,

Suggests: knitr, rmarkdown, prettydoc, testthat, httr, curl, sf

To cite 'BIOMASS', please use citation("BIOMASS").

See more in the article of Réjou-Méchain et al. (2017) <https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12753>.

Title: Multilevel Modeling of Dendroclimatical Fluctuations
Author: Wilson Lara <wilarhen@gmail.com>, Felipe Bravo <fbravo@pvs.uva.es>
Description: Multilevel data frames of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity.  Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in functions modelFrame and ringApply.

Description: Multilevel data frames of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity.  Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in functions ringApply(), modelFrame(), and muleMan().

Description: Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity.  Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions.

Title: BIPOD (Bayesian Inference for Partially Observed diffusions)
Maintainer: Anders Chr. Jensen <anders@math.ku.dk>
Description: Bayesian parameter estimation for (partially observed)