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) . 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'); lm_robust(), lm_lin(), and iv_robust() (from package 'estimatr'); 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'); rma.uni() and rma.mv() (from 'metafor'); and mmrm() (from 'mmrm').