Title: Bayesian BIN (Bias, Information, Noise) Model of Forecasting
Description: A recently proposed Bayesian BIN model disentangles the underlying processes
that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into
three components: bias, partial information, and noise. By describing the differences between two
groups of forecasters, the model allows the user to carry out useful inference, such as calculating
the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information.
It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient
extraction of valid information from the environment improves forecasting accuracy. This package provides
easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov,
Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN
Imports: methods, Rcpp (>= 0.12.0), rstan (>= 2.18.1), dplyr (>=
Suggests: testthat, knitr, rmarkdown, pacman (>= 0.5.1), rstantools (>=
Packaged: 2021-06-09 14:12:11 UTC; elviramoreno
Author: Ville Satopää [aut, cre] (<https://orcid.org/0000-0002-3787-7303>),

Suggests: testthat, knitr, rmarkdown, pacman (>= 0.5.1), RcppParallel
Packaged: 2022-04-04 11:58:36 UTC; ville.satopaa

Title: Utilities for the BIOM (Biological Observation Matrix) Format
Authors@R: person (c("Daniel", "T."), "Braithwaite", role=c("aut", "cre"),
Description: Provides utilities to facilitate import, export and computation with the
BIOM (Biological Observation Matrix) format (http://biom-format.org).
Author: Daniel T. Braithwaite [aut, cre]
Maintainer: Daniel T. Braithwaite <contact.dtb@gmail.com>

Title: Estimating Above-Ground Biomass and Its Uncertainty in Tropical
Author: Maxime REJOU-MECHAIN, Ariane TANGUY, Camille PIPONIOT, Jerome CHAVE, Bruno HERAULT
Description: Contains data analysis functions to estimate above-ground biomass and its uncertainty in tropical forests. BIOMASS functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty, (3) construct height-diameter models, (4) estimate the above-ground biomass at the stand level with associated uncertainty.
Imports: minpack.lm, raster, msm, httr, jsonlite, methods, graphics,