Cluster-Robust (Sandwich) Variance Estimators with Small-Sample
Corrections
Description
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() 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').