regsensitivity

Regression sensitivity analysis for omitted-variable bias in R.

Implements the identified-set and breakdown-point analyses described in:

• Diegert, Masten and Poirier (2026) — bounds on the long-regression coefficient under relaxations of the no-omitted-variables assumption, indexed by rxbar / rybar / cbar.
• Oster (2019) — identified set in (delta, R²(long)) and the associated breakdown point.
• Masten and Poirier (2026) — extension with a maximum omitted-variable-bias constraint.

Both arXiv working papers above are still revised periodically; citations in this package track the year of the latest arXiv revision.

The package provides a formula + data.frame API, ggplot2-based plotting, and a percentile/cluster bootstrap on the breakdown point.

Installation

# Released version (once accepted on CRAN):
install.packages("regsensitivity")

# Development version from GitHub:
# install.packages("remotes")
remotes::install_github("mikenguyen13/regsensitivity")

# Pre-built binary from r-universe:
install.packages("regsensitivity",
                 repos = c("https://mikenguyen13.r-universe.dev",
                           "https://cloud.r-project.org"))

Quickstart

library(regsensitivity)
data(bfg2020)
bfg2020$statea <- factor(bfg2020$statea)

compare <- c("log_area_2010", "lat", "lon", "temp_mean", "rain_mean",
             "elev_mean", "d_coa", "d_riv", "d_lak", "ave_gyi")
form <- avgrep2000to2016 ~ tye_tfe890_500kNI_100_l6 +
    log_area_2010 + lat + lon + temp_mean + rain_mean + elev_mean +
    d_coa + d_riv + d_lak + ave_gyi + statea

# Default DMP analysis, cbar = 0.1
bnds <- regsen_bounds(form, bfg2020, compare = compare, cbar = 0.1)
print(bnds)
plot(bnds)

# Breakdown across cbar grid
bd <- regsen_breakdown(form, bfg2020, compare = compare,
                        cbar = seq(0, 1, 0.05))
plot(bd)

# Cluster bootstrap CI on the breakdown point
boot <- regsen_boot(form, bfg2020, compare = compare,
                     cbar = 1, cluster = "km_grid_cel_code",
                     R = 199)
print(boot)

See vignette("regsensitivity") for a full tour, and vignette("dmp2022-replication") for the paper-exact replication.

Crosswalk: Stata → R

Stata syntax	R equivalent
regsensitivity bounds y x w, compare(w1) cbar(.1)	regsen_bounds(y ~ x + w, data, compare = w1, cbar = 0.1)
regsensitivity bounds ... cbar(0(.2)1)	regsen_bounds(..., cbar = seq(0, 1, 0.2))
regsensitivity bounds ... rybar(=rxbar)	regsen_bounds(..., rybar_expr = function(rx) rx)
regsensitivity bounds ... oster	regsen_bounds(..., analysis = "oster")
regsensitivity bounds ... oster delta(-3 3 eq)	regsen_bounds(..., analysis = "oster", delta = seq(-3, 3, 0.05))
regsensitivity bounds ... oster delta(0(.001).999 bound)	regsen_bounds(..., analysis = "oster", delta = seq(0, .999, .001), delta_type = "bound")
regsensitivity breakdown ... cbar(0(.1)1)	regsen_breakdown(..., cbar = seq(0, 1, 0.1))
regsensitivity breakdown ... beta(-1(.2)1 lb)	regsen_breakdown(..., beta = bnd_lb(seq(-1, 1, 0.2)))
regsensitivity breakdown ... beta(4 ub)	regsen_breakdown(..., beta = bnd_ub(4))
regsensitivity breakdown ... oster rmax(0(.1)1) beta(0 eq)	regsen_breakdown(..., analysis = "oster", r2long = seq(0, 1, 0.1), beta = bnd_eq(0))
regsensitivity plot	plot(result)
regsensitivity (no subcommand)	regsen_summary(...)

Citation

Inside R, every format is one call away:

citation("regsensitivity")                       # rendered text
print(citation("regsensitivity"), bibtex = TRUE) # with BibTeX
toBibtex(citation("regsensitivity"))             # BibTeX only

Copy-pasteable forms below.

BibTeX

@Manual{regsensitivity,
    title  = {regsensitivity: Regression Sensitivity Analysis for Omitted Variable Bias},
    author = {Mike Nguyen},
    year   = {2026},
    note   = {R package version 0.1.1},
    url    = {https://github.com/mikenguyen13/regsensitivity}
}

RIS (Zotero, EndNote, Mendeley)

TY  - COMP
TI  - regsensitivity: Regression Sensitivity Analysis for Omitted Variable Bias
AU  - Nguyen, Mike
PY  - 2026
PB  - GitHub
UR  - https://github.com/mikenguyen13/regsensitivity
ER  -

APA 7

Nguyen, M. (2026). regsensitivity: Regression sensitivity analysis for omitted variable bias (Version 0.1.1) [R package]. https://github.com/mikenguyen13/regsensitivity

MLA 9

Nguyen, Mike. regsensitivity: Regression Sensitivity Analysis for Omitted Variable Bias. Version 0.1.1, 2026. https://github.com/mikenguyen13/regsensitivity.

Chicago (author-date)

Nguyen, Mike. 2026. “regsensitivity: Regression Sensitivity Analysis for Omitted Variable Bias.” R package version 0.1.1. https://github.com/mikenguyen13/regsensitivity.

Machine-readable

• CITATION.cff — used by GitHub’s “Cite this repository” widget
• codemeta.json — CodeMeta JSON-LD, consumed by Zenodo, r-universe, OpenAIRE
• inst/CITATION — R-side utils::citation() source

Methodology citations (the underlying papers, which are separate works) live below in References.

References

• Diegert, Masten, Poirier (2026). Assessing Omitted Variable Bias when the Controls are Endogenous. arXiv:2206.02303.
• Oster (2019). Unobservable Selection and Coefficient Stability. JBES 37(2), 187–204.
• Masten, Poirier (2026). The Effect of Omitted Variables on the Sign of Regression Coefficients. arXiv:2208.00552.

Code of conduct

Please note that the regsensitivity project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

MIT. See LICENSE.md. The bundled bfg2020 data set is a subset of the replication data from Bazzi, Fiszbein and Gebresilasse (2020), Frontier Culture, Econometrica.