This package 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) http://www.statcan.gc.ca/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(), ivreg (from package AER), plm() (from package plm), gls() and lme() (from nlme), robu() (from robumeta), and rma.uni() and rma.mv() (from metafor).