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sbim

1.0.0

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

0packages depend
1.9Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Joonha ParkFirst published 2025-03-131 releasesCRAN page ↗

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).

Install

Health

CRAN checks
13OK
Slowest check: 6.1 min · r-devel-windows-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
2
Dependencies · direct
Check history
  • OK2026-08-04
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-06-08
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-06-07
    12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-05-12
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Show 4 earlier snapshots
  • WARNING2026-05-11
    12 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-04-22
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-04-18
    13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMENoVignettesYes · dynamicpkgdown siteNoNEWSYes · 67% structuredCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
100%
Return-value docs
100%
References docs
67%

Downloads

1.9K
CRAN downloads in the past year
Rank #22,516 · ~5/day · ~158/mo
Daily download trend is not available in this view yet.
10830 days
47890 days
1.9K1 year
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Also on369 r2u16 autocran

Dependencies

Declared dependencies
10 external dependencies (excludes base and recommended)
Depends (1)
R >= 3.5
Imports (2)
Rcppstats
LinkingTo (1)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (1)
Maintainer (1)
Author, Maintainer
Authors (1)
Author, Maintainer
Package Timeline

1 release. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 1.0.0Latest
    2026-03-10 · current release
  • R
    R 4.5.0 released · 2025-04-11

Package metadata

First published
2025-03-13
Total releases
1 / 1 yrs
License
GPL (>= 3) OSI
Minimum R
≥ 3.5
Download size
2.0 MB
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("sbim")
Park, J. (2025). sbim: Simulation-Based Inference using a Metamodel for Log-Likelihood Estimator (Version 1.0.0) [Computer software]. https://doi.org/10.32614/CRAN.package.sbim

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for sbim version 1.0.0 [Data set]. HJJB, LLC. Data release v2026-08-15. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-15, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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