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✨
DESCRIPTION |NEWS
card.svg |card.png
adjustr/json (API)

# 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:

4.74 score 11 stars 4 scripts 494 downloads 5 exports 49 dependencies

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

Target	Result	Time
linux-devel-x86_64	OK	184
source / vignettes	OK	208
linux-release-x86_64	OK	228
macos-release-arm64	OK	122
macos-oldrel-arm64	OK	149
windows-devel	OK	122
windows-release	OK	179
windows-oldrel	OK	121
wasm-release	OK	173

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 otel 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

Introduction | Basic Workflow Example | Multiple Alternative Specifications

Rendered fromadjustr.Rmdusingknitr::rmarkdown

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