minpack.lm, raster, jsonlite, methods, proj4, graphics, stats,
utils, data.table (>= 1.9.8), rappdirs, sp
knitr, rmarkdown, testthat, covr, httr, rvest, prettydoc,
usethis, MCMCglmm, taxize, lmfor, sf

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

Contains functions to estimate aboveground biomass/carbon and its uncertainty in tropical forests.
These functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty,
(3) construct height-diameter models, (4) manage tree and plot coordinates,
(5) estimate the aboveground biomass/carbon at the stand level with associated uncertainty.
To cite 'BIOMASS', please use citation("BIOMASS").
See more in the article of Réjou-Méchain et al. (2017) <doi:10.1111/2041-210X.12753>.

Contains functions to estimate aboveground biomass/carbon and its uncertainty in tropical forests.
These functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty,
(3) construct height-diameter models, (4) manage tree and plot coordinates,
(5) estimate the aboveground biomass/carbon at the stand level with associated uncertainty.
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>.

Multilevel Modeling of Dendroclimatical Fluctuations
Wilson Lara <wilarhen@gmail.com>, Felipe Bravo <fbravo@pvs.uva.es>
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.

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().