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BINtools
View on CRAN: Click
here
Download and install BINtools package within the R console
Install from CRAN:
install.packages("BINtools")
Install from Github:
library("remotes")
install_github("cran/BINtools")
Install by package version:
library("remotes")
install_version("BINtools", "0.2.0")
Attach the package and use:
library("BINtools")
Maintained by
Ville Satopää
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-06-10
Latest Update: 2022-04-04
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. Satop
How to cite:
Ville Satopää (2021). BINtools: Bayesian BIN (Bias, Information, Noise) Model of Forecasting. R package version 0.2.0, https://cran.r-project.org/web/packages/BINtools
Previous versions and publish date:
0.1.0 (2021-06-10 10:20)
Other packages that cited BINtools R package
View BINtools citation profile
Other R packages that BINtools depends,
imports, suggests or enhances
Functions, R codes and Examples using
the BINtools R package
Some associated functions: BINtools-package . complete_summary . estimate_BIN . simulate_data .
Some associated R codes: BINtools-package.R . complete_summary.R . data_preparation.R . estimate_BIN.R . simulate_data.R . stanmodels.R . Full BINtools package functions and examples
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