clubSandwich
Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections
Provides several cluster-robust variance estimators (i.e., sandwich estimators) for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator introduced by Bell and McCaffrey (2002) https://www150.statcan.gc.ca/n1/pub/12-001-x/2002002/article/9058-eng.pdf and developed further by Pustejovsky and Tipton (2017) doi:10.1080/07350015.2016.1247004. The package includes functions for estimating the variance- covariance matrix and for testing single- and multiple- contrast hypotheses based on Wald test statistics. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple- contrast hypotheses use an approximation to Hotelling's T-squared distribution. Methods are provided for a variety of fitted models, including lm() and mlm objects, glm(), geeglm() (from package 'geepack'), ivreg() (from package 'AER'), ivreg() (from package 'ivreg' when estimated by ordinary least squares), plm() (from package 'plm'), gls() and lme() (from 'nlme'), lmer() (from ‘lme4'), robu() (from ’robumeta'), and rma.uni() and rma.mv() (from 'metafor').
- Version0.6.0
- R versionR (≥ 3.0.0)
- LicenseGPL-3
- Needs compilation?No
- Languageen-US
- Last release04/01/2025
Documentation
Team
James Pustejovsky
Jingru Zhang
Show author detailsRolesContributorSamuel Pekofsky
Show author detailsRolesContributor
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- Imports2 packages
- Suggests18 packages
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