Package: sbim 1.0.0

sbim: Simulation-Based Inference using a Metamodel for Log-Likelihood Estimator

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).

Authors:Joonha Park [aut, cre]

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sbim.pdf |sbim.html
sbim/json (API)
NEWS

# Install 'sbim' in R:
install.packages('sbim', repos = c('https://joonhap.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

2.00 score 7 exports 1 dependencies

Last updated 17 hours agofrom:b27ad9d843. Checks:12 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 14 2025
R-4.5-win-x86_64OKMar 14 2025
R-4.5-mac-x86_64OKMar 14 2025
R-4.5-mac-aarch64OKMar 14 2025
R-4.5-linux-x86_64OKMar 14 2025
R-4.4-win-x86_64OKMar 14 2025
R-4.4-mac-x86_64OKMar 14 2025
R-4.4-mac-aarch64OKMar 14 2025
R-4.4-linux-x86_64OKMar 14 2025
R-4.3-win-x86_64OKMar 14 2025
R-4.3-mac-x86_64OKMar 14 2025
R-4.3-mac-aarch64OKMar 14 2025

Exports:cihtoptDesignpsclqsclrsclsimll

Dependencies:Rcpp

How to use package sbim: Simulation-Based Inference using a Metamodel for Log-Likelihood Estimator

Rendered fromhow-to-use-sbim.Rmdusingknitr::rmarkdownon Mar 14 2025.

Last update: 2025-03-13
Started: 2025-03-13