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sbim
View on CRAN: Click
here
Download and install sbim package within the R console
Install from CRAN:
install.packages("sbim")
Install from Github:
library("remotes")
install_github("cran/sbim")
Install by package version:
library("remotes")
install_version("sbim", "1.0.0")
Attach the package and use:
library("sbim")
Maintained by
Joonha Park
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.sbim . sbim results . sbim.pdf . How to use package sbim: Simulation-Based Inference using a Metamodel for Log-Likelihood Estimator . sbim_1.0.0.tar.gz . sbim_1.0.0.zip . sbim_1.0.0.zip . sbim_1.0.0.zip . sbim_1.0.0.tgz . sbim_1.0.0.tgz . sbim_1.0.0.tgz . sbim_1.0.0.tgz . sbim_1.0.0.tgz . sbim_1.0.0.tgz . https://CRAN.R-project.org/package=sbim .
First Published: 2025-03-13
Latest Update: 2025-03-13
Description:
Parameter inference methods for models defined implicitly using a random simulator. Inference is carried out using simulation-based estimates of the log-likelihood of the data. The inference methods implemented in this package are explained in Park, J. (2025) <doi:10.48550/arxiv.2311.09446>. These methods are built on a simulation metamodel which assumes that the estimates of the log-likelihood are approximately normally distributed with the mean function that is locally quadratic around its maximum. Parameter estimation and uncertainty quantification can be carried out using the ht() function (for hypothesis testing) and the ci() function (for constructing a confidence interval for one-dimensional parameters).
How to cite:
Joonha Park (2025). sbim: Simulation-Based Inference using a Metamodel for Log-Likelihood Estimator. R package version 1.0.0, https://cran.r-project.org/web/packages/sbim. Accessed 04 Apr. 2025.
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