Package: adjustr 0.2.0

adjustr: Stan Model Adjustments and Sensitivity Analyses using Importance Sampling

Assess the sensitivity of a Bayesian model (fitted using 'Stan' via 'rstan', 'brms', or 'cmdstanr') to the specification of its likelihood and priors. Users provide a series of alternate sampling specifications, and the package uses Pareto-smoothed importance sampling (PSIS) to estimate posterior quantities of interest under each specification, without needing to refit the model. Methods are based on Vehtari, Simpson, Gelman, Yao, and Gabry (2024) <doi:10.48550/arXiv.1507.02646>.

Authors:Cory McCartan [aut, cre, cph]

adjustr_0.2.0.tar.gz
adjustr_0.2.0.zip(r-4.7)adjustr_0.2.0.zip(r-4.6)adjustr_0.2.0.zip(r-4.5)
adjustr_0.2.0.tgz(r-4.6-any)adjustr_0.2.0.tgz(r-4.5-any)
adjustr_0.2.0.tar.gz(r-4.7-any)adjustr_0.2.0.tar.gz(r-4.6-any)
adjustr_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
adjustr/json (API)
NEWS

# Install 'adjustr' in R:

install.packages('adjustr', repos = c('https://corymccartan.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/corymccartan/adjustr/issues

Pkgdown/docs site:https://corymccartan.com

On CRAN:

3.74 score 11 stars 4 scripts 5 exports 48 dependencies

Last updated from:e6d9ed7f84 (on v0.2.0). Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	188
source / vignettes	OK	223
linux-release-x86_64	OK	184
macos-release-arm64	OK	129
macos-oldrel-arm64	OK	116
windows-devel	OK	134
windows-release	OK	143
windows-oldrel	OK	147
wasm-release	OK	139

Exports:adjust_weights extract_samp_stmts get_resampling_idxs make_spec spec_plot

Dependencies:abind backports BH callr checkmate cli cpp11 desc distributional dplyr farver generics ggplot2 glue gridExtra gtable inline isoband labeling lifecycle loo magrittr matrixStats numDeriv pillar pkgbuild pkgconfig posterior processx ps QuickJSR R6 RColorBrewer Rcpp RcppEigen RcppParallel rlang rstan S7 scales StanHeaders tensorA tibble tidyselect utf8 vctrs viridisLite withr

Sensitivity Analysis of a Simple Hierarchical Model

Rendered fromadjustr.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2022-06-21
Started: 2021-05-11

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Compute Pareto-smoothed Importance Weights for Alternative Model Specifications	adjust_weights
Convert an 'adjustr_spec' Object Into a Data Frame	as.data.frame.adjustr_spec
'dplyr' Methods for 'adjustr_spec' Objects	arrange.adjustr_spec dplyr.adjustr_spec filter.adjustr_spec rename.adjustr_spec select.adjustr_spec slice.adjustr_spec
Extract Model Sampling Statements From a Stan Model.	extract_samp_stmts
Get Importance Resampling Indices From Weights	get_resampling_idxs
Set Up Model Adjustment Specifications	length.adjustr_spec make_spec print.adjustr_spec
Extract Weights From an 'adjustr_weighted' Object	pull.adjustr_weighted
Plot Posterior Quantities of Interest Under Alternative Model Specifications	spec_plot
Summarize Posterior Distributions Under Alternative Model Specifications	summarise.adjustr_weighted summarize.adjustr_weighted