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'), lmer() (from `lme4`), robu() (from 'robumeta'), and
rma.uni() and rma.mv() (from 'metafor').
Formula, knitr, carData, geepack, metafor, robumeta, nlme,
mlmRev, AER, plm (>= 1.6-4), Matrix, lme4, zoo, testthat,
rmarkdown

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://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'), lmer() (from `lme4`), robu() (from 'robumeta'), and
rma.uni() and rma.mv() (from 'metafor').