Introduction | Related software | Using clarify | 1. Fitting the model | 2. Drawing from the coefficient distribution | 3. Computing derived quantities | 4. Summarize and visualize the simulated distribution | Wrappers for sim_apply(): sim_setx(), sim_ame(), and sim_adrf() | sim_setx(): predictions at representative values | sim_ame(): average adjusted predictions and average marginal effects | sim_adrf(): average dose-response functions | Transforming and combining estimates | transform() | cbind() | Using clarify with multiply imputed data | Comparison to other packages | Conclusion | References
Package: clarify 0.2.2
Noah Greifer
clarify: Simulation-Based Inference for Regression Models
Performs simulation-based inference as an alternative to the delta method for obtaining valid confidence intervals and p-values for regression post-estimation quantities, such as average marginal effects and predictions at representative values. This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in Greifer, et al. (2025) <doi:10.32614/RJ-2024-015>. 'clarify' is meant to replace some of the functionality of the archived package 'Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality.
Authors:
clarify_0.2.2.tar.gz
clarify_0.2.2.zip(r-4.7)clarify_0.2.2.zip(r-4.6)clarify_0.2.2.zip(r-4.5)
clarify_0.2.2.tgz(r-4.6-any)clarify_0.2.2.tgz(r-4.5-any)
clarify_0.2.2.tar.gz(r-4.7-any)clarify_0.2.2.tar.gz(r-4.6-any)
clarify_0.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
clarify/json (API)
| # Install 'clarify' in R: |
| install.packages('clarify', repos = c('https://iqss.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/iqss/clarify/issues
Pkgdown/docs site:https://iqss.github.io
Last updated from:8b1cefe02a. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 193 | ||
| source / vignettes | OK | 298 | ||
| linux-release-x86_64 | OK | 210 | ||
| macos-release-arm64 | OK | 165 | ||
| macos-oldrel-arm64 | OK | 162 | ||
| windows-devel | OK | 153 | ||
| windows-release | OK | 149 | ||
| windows-oldrel | OK | 182 | ||
| wasm-release | OK | 140 |
Exports:misimsimsim_adrfsim_amesim_applysim_setx
Dependencies:backportscheckmatechkclicpp11data.tablefarverFormulagenericsggplot2gluegtableinsightisobandlabelinglifecyclemarginaleffectspbapplyR6RColorBrewerrlangS7scalesvctrsviridisLitewithr
clarify: Simulation-Based Inference for Regression Models
Last update: 2025-09-19
Started: 2022-12-21
Translating Zelig to clarify
Introduction | Predictions at representative values | Zelig workflow | clarify workflow | Rare-events logit | Estimating the ATT after matching | Combining results after multiple imputation | References
Last update: 2025-09-18
Started: 2022-12-14
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Simulate model coefficients after multiple imputation | misim |
| Plot marginal predictions from 'sim_adrf()' | plot.clarify_adrf |
| Plotting and inference for 'clarify_est' objects | confint.clarify_est plot.clarify_est summary.clarify_est |
| Plot marginal predictions from 'sim_setx()' | plot.clarify_setx |
| Simulate model parameters | sim |
| Compute an average dose-response function | print.clarify_adrf sim_adrf |
| Compute average marginal effects | print.clarify_ame sim_ame |
| Apply a function to simulated parameter values | print.clarify_est sim_apply |
| Compute predictions and first differences at set values | print.clarify_setx sim_setx |
| Transform and combine 'clarify_est' objects | cbind.clarify_est transform.clarify_est |
