BINtools: Bayesian BIN (Bias, Information, Noise) Model of Forecasting
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 |
| 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.