Introduction | Citing cobalt | Why cobalt? | Visual clarity | Useful summaries | One tool to rule them all | Flexibility | Pretty plots | Unique features | How To Use cobalt | Utilities | bal.tab() | Using bal.tab() on its own | Using bal.tab() with MatchIt | Using bal.tab() with WeightIt | bal.plot() | love.plot() | Additional Features | Using cobalt with continuous treatments | Using cobalt with multi-category treatments | Comparing balancing methods | Using the prognostic score for balance assessment | Details on Calculations | Variance in Standardized Mean Differences and Correlations | Weighted Variance | Effective Sample Size for Weighting | What's Missing in cobalt | Test Statistics and P-values | Q-Q Plots and Summaries | What's Added in cobalt | Density Plots | Variance Ratios | Distinguishing Continuous and Binary Covariates | Interactions and Polynomials | Clusters | Missing Data | For Programmers: Integrating cobalt with Your Package | Acknowledgments | References
Package: cobalt 4.6.3
cobalt: Covariate Balance Tables and Plots
Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'WeightIt', 'MatchThem', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments.
Authors:
cobalt_4.6.3.tar.gz
cobalt_4.6.3.zip(r-4.7)cobalt_4.6.3.zip(r-4.6)cobalt_4.6.3.zip(r-4.5)
cobalt_4.6.3.tgz(r-4.6-any)cobalt_4.6.3.tgz(r-4.5-any)
cobalt_4.6.3.tar.gz(r-4.7-any)cobalt_4.6.3.tar.gz(r-4.6-any)
cobalt_4.6.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cobalt/json (API)
| # Install 'cobalt' in R: |
| install.packages('cobalt', repos = c('https://ngreifer.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ngreifer/cobalt/issues
Pkgdown/docs site:https://ngreifer.github.io
Datasets:
- lalonde - Data from National Supported Work Demonstration and PSID, as analyzed by Dehejia and Wahba (1999).
- lalonde_mis - Data from National Supported Work Demonstration and PSID, as analyzed by Dehejia and Wahba (1999).
causal-inferencepropensity-scores
Last updated from:d955e34778. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 225 | ||
| source / vignettes | OK | 416 | ||
| linux-release-x86_64 | OK | 226 | ||
| macos-release-arm64 | OK | 137 | ||
| macos-oldrel-arm64 | OK | 164 | ||
| windows-devel | OK | 162 | ||
| windows-release | OK | 135 | ||
| windows-oldrel | OK | 153 | ||
| wasm-release | OK | 145 |
Exports:available.statsbal.computebal.initbal.plotbal.tabcol_w_corrcol_w_covcol_w_dcorrcol_w_dcovcol_w_kscol_w_meancol_w_ovlcol_w_sdcol_w_smdcol_w_vrf.buildget.cobalt.optionsget.wlove.plotset.cobalt.optionssplitfactorunsplitfactorvar.names
Dependencies:argclicpp11farverggplot2gluegridExtragtableisobandlabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr
Covariate Balance Tables and Plots: A Guide to the cobalt Package
Last update: 2026-05-29
Started: 2016-07-18
Optimizing Tuning Parameters for Balance
bal.compute() and bal.init() | Balance statistics | smd.mean, smd.max, smd.rms | ks.mean, ks.max, ks.rms | ovl.mean, ovl.max, ovl.rms | mahalanobis | energy.dist | kernel.dist | l1.med | r2, r2.2, r2.3 | p.mean, p.max, p.rms | s.mean, s.max, s.rms | distance.cov, distance.cor | Choosing a balance statistic | Example | Tuning GBM for balance | References
Last update: 2026-05-28
Started: 2023-03-16
Using love.plot() To Generate Love Plots
References
Last update: 2026-05-28
Started: 2023-03-16
Using cobalt with Clustered, Multiply Imputed, and Other Segmented Data
cobalt and Segmented Data | Clustered Data | bal.tab() | bal.plot() | love.plot() | Multiply Imputed Data | Multi-Category Treatments with Multiply Imputed Data | Concluding Remarks
Last update: 2026-05-28
Started: 2023-03-16
Using cobalt with Longitudinal Treatments
Setup | bal.tab() | bal.plot() | love.plot() | Other Packages | References
Last update: 2026-05-28
Started: 2023-03-16
Using cobalt with Other Preprocessing Packages
Utilities | splitfactor() and unsplitfactor() | get.w() | bal.tab() | Using bal.tab() with twang | Using bal.tab() with Matching | Using bal.tab() with optmatch | Using bal.tab() with CBPS | Using bal.tab() with ebal | Using bal.tab() with designmatch | Using bal.tab() with sbw | Using bal.tab() with MatchThem | Using bal.tab() with cem | Using bal.tab() with other packages
Last update: 2026-01-23
Started: 2023-03-16
Frequently Asked Questions
How are standardized mean differences computed in cobalt? | Why are mean differences not standardized for binary covariates? | Why do you use the same standardization factor before and after adjustment? | How do I extract the balance tables from the bal.tab() object? | How are balance statistics computed when using subclassification? | Why don't I get the same balance statistics when using cobalt as I do when using tableone? | Why doesn't thresholds work with bal.tab() with multiply imputed or clustered data? | References
Last update: 2026-01-23
Started: 2024-02-26
