Package: IJSE 0.3.4

IJSE: Infinitesimal Jackknife Standard Errors for 'brms' Models

Computes infinitesimal jackknife standard errors and covariance matrices for posterior expectations from 'brms' models or matched posterior draws and pointwise log likelihoods. Supports independent and clustered sampling units, transformed quantities, blockwise likelihood evaluation, and chain stability diagnostics. References: Ji et al. (2026) <doi:10.3102/10769986251379738>; Giordano and Broderick (2024) <doi:10.48550/arXiv.2305.06466>.

Authors:Feng Ji [aut, cre], JoonHo Lee [aut], Sophia Rabe-Hesketh [aut]

IJSE_0.3.4.tar.gz
IJSE_0.3.4.zip(r-4.7-any)IJSE_0.3.4.zip(r-4.6-any)IJSE_0.3.4.zip(r-4.5-any)
IJSE_0.3.4.tgz(r-4.6-any)IJSE_0.3.4.tgz(r-4.5-any)
IJSE_0.3.4.tar.gz(r-4.7-any)IJSE_0.3.4.tar.gz(r-4.6-any)
IJSE_0.3.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
IJSE/json (API)

# Install 'IJSE' in R:
install.packages('IJSE', repos = c('https://trtfj.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

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

3.72 score 26 scripts 244 downloads 3 exports 19 dependencies

Last updated from:a01e430ca2. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK163
source / vignettesOK433
linux-releaseOK159
macos-releaseOK193
macos-oldrelOK270
windows-develOK96
windows-releaseOK120
windows-oldrelOK125
wasm-releaseOK118

Exports:IJ_seijseijse_draws

Dependencies:abindbackportscheckmateclidistributionalgenericsgluelifecyclemagrittrmatrixStatsnumDerivpillarpkgconfigposteriorrlangtensorAtibbleutf8vctrs

Infinitesimal Jackknife Inference with IJSE: A Reproducible Vignette Study
Purpose and scope | What is being estimated? | A short executable example | Part A: repeated sampling with an exact posterior | Data-generating mechanisms | Exact calculations and evaluation criteria | Coverage and calibration | Part B: numerical precision of the package calculation | Part C: actual brms median regression | Design and fitting protocol | Results | Worked brms usage and transformed quantities | Computation, diagnostics and reproducibility | Interpretation and remaining limits | References
Rendered fromijse-study.Rmdusingknitr::rmarkdown

Last update: 2026-10-22
Started: 2026-10-22

Tutorial 1: Getting Started with IJSE
What you will learn | Construct an example with a known posterior | Read intervals and covariance correctly | Check against a calculation you know | Transform every draw | Evaluate likelihood blocks | Keep a useful result record
Rendered fromijse.Rmdusingknitr::rmarkdown

Last update: 2026-10-22
Started: 2026-10-22

Tutorial 2: brms, Clusters and Joint Contrasts
What you will learn | Load a fit together with its sampling IDs | Begin with independent observations | Use clusters as the sampling units | Compute predictions and a joint contrast | Select draws without losing alignment | Verify streamed likelihood extraction | Review diagnostics and save the analysis | Optional: fit the model yourself
Rendered fromijse-brms.Rmdusingknitr::rmarkdown

Last update: 2026-10-22
Started: 2026-10-22

Tutorial 3: Diagnostics, Influence and Troubleshooting
What you will learn | Set up clustered observations and a working posterior | Choose how much diagnostic computation to do | Preserve unequal retained chains | Interpret influence as local sensitivity | Independently check the sign and scale | Reproduce common input failures | Check the sampling units, not only the dimensions
Rendered fromijse-diagnostics.Rmdusingknitr::rmarkdown

Last update: 2026-10-22
Started: 2026-10-22

Readme and manuals

Help Manual

Help pageTopics
IJ inference for posterior expectationsIJSE-package IJSE
Infinitesimal jackknife inference for brms modelsijse IJ_se
Infinitesimal jackknife inference from posterior drawsijse_draws
Summarize IJ inferencecoef.ijse confint.ijse influence.ijse print.ijse print.summary.ijse summary.ijse vcov.ijse