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:
sbim_1.0.0.tar.gz
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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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 17 hours agofrom:b27ad9d843. Checks:12 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 14 2025 |
R-4.5-win-x86_64 | OK | Mar 14 2025 |
R-4.5-mac-x86_64 | OK | Mar 14 2025 |
R-4.5-mac-aarch64 | OK | Mar 14 2025 |
R-4.5-linux-x86_64 | OK | Mar 14 2025 |
R-4.4-win-x86_64 | OK | Mar 14 2025 |
R-4.4-mac-x86_64 | OK | Mar 14 2025 |
R-4.4-mac-aarch64 | OK | Mar 14 2025 |
R-4.4-linux-x86_64 | OK | Mar 14 2025 |
R-4.3-win-x86_64 | OK | Mar 14 2025 |
R-4.3-mac-x86_64 | OK | Mar 14 2025 |
R-4.3-mac-aarch64 | OK | Mar 14 2025 |
Exports:cihtoptDesignpsclqsclrsclsimll
Dependencies:Rcpp
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Confidence interval for scalar parameter constructed using simulated log likelihoods | ci ci.simll |
Hypothesis tests based on simulation based log likelihood estimates | ht ht.simll |
Find the next optimal design point for simulation-based inference | optDesign optDesign.simll |
The SCL distribution | pscl qscl rscl SCL |
Simulation Log Likelihood class | simll |