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Bestie  

Bayesian Estimation of Intervention Effects
View on CRAN: Click here


Download and install Bestie package within the R console
Install from CRAN:
install.packages("Bestie")

Install from Github:
library("remotes")
install_github("cran/Bestie")

Install by package version:
library("remotes")
install_version("Bestie", "0.1.5")



Attach the package and use:
library("Bestie")
Maintained by
Jack Kuipers
[Scholar Profile | Author Map]
First Published: 2020-03-30
Latest Update: 2022-04-28
Description:
An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.
How to cite:
Jack Kuipers (2020). Bestie: Bayesian Estimation of Intervention Effects. R package version 0.1.5, https://cran.r-project.org/web/packages/Bestie. Accessed 11 Apr. 2025.
Previous versions and publish date:
0.1.1 (2020-03-30 17:30), 0.1.2 (2021-02-26 12:00), 0.1.3 (2021-11-22 10:40), 0.1.4 (2022-02-02 15:10)
Other packages that cited Bestie R package
View Bestie citation profile
Other R packages that Bestie depends, imports, suggests or enhances
Complete documentation for Bestie
Functions, R codes and Examples using the Bestie R package
Some associated functions: DAGintervention . DAGinterventionMC . DAGparameters . 
Some associated R codes: RcppExports.R . interventionest.R . interventionestMC.R . posteriorparameters.R .  Full Bestie package functions and examples
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