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) <> and developed further by Pustejovsky and Tipton (2017) . 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(), glm(), plm() (from package 'plm'), gls() and lme() (from 'nlme'), robu() (from 'robumeta'), and rma.uni() and (from 'metafor').


clubSandwich 0.2.2

  • Added bread() methods for all supported model classes.
  • vcovCR() is now calculated using bread(), and carries attributes for bread, est_mat, and adjustment matrices.
  • vcovCR() gains a 'form' argument to obtain just the meat of the sandwich, or to use a user-specified bread matrix.
  • Refactored internal functions for degrees of freedom calculation to improve speed and memory usage.
  • Bug fixes:
    • updated nobs.plm method to handle first-differenced models

clubSandwich 0.2.1

  • First version released on CRAN.

Reference manual

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0.2.3 by James Pustejovsky, a month ago

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Browse source code at

Authors: James Pustejovsky [aut, cre]

Documentation:   PDF Manual  

Task views: Econometrics

GPL-3 license

Imports stats, sandwich

Suggests Formula, knitr, car, geepack, metafor, robumeta, nlme, mlmRev, AER, plm, testthat, rmarkdown

Suggested by robumeta.

See at CRAN