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 Model of Forecasting" <doi:10.1287/mnsc.2020.3882>.
Version: 0.2.0 Depends: R (≥ 3.4.0) Imports: methods, Rcpp (≥ 0.12.0), rstan (≥ 2.18.1), dplyr (≥ 1.0.2), tibble (≥ 3.0.3), stringi (≥ 1.4.6), mvtnorm (≥ 1.1.1), combinat (≥ 0.0.8), rstantools LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0) Suggests: testthat, knitr, rmarkdown, pacman (≥ 0.5.1), RcppParallel (≥ 5.0.1) Published: 2022-04-04 DOI: 10.32614/CRAN.package.BINtools Author: Ville Satopää [aut, cre], Marat Salikhov [aut], Elvira Moreno [aut] Maintainer: Ville Satopää <ville.satopaa at gmail.com> License: GPL-3 NeedsCompilation: yes SystemRequirements: GNU make Materials: README NEWS CRAN checks: BINtools results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=BINtools to link to this page.
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