Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections
Impute a patterned block-diagonal covariance matrix
Test all or selected regression coefficients in a fitted model
Calculate confidence intervals for all or selected regression coeffici...
Create constraint matrices
Detect cluster structure of an rma.mv object
Impute a block-diagonal covariance matrix
Calculate confidence intervals and p-values for linear contrasts of re...
Cluster-robust variance-covariance matrix for a geeglm object.
Cluster-robust variance-covariance matrix for a glm object.
Cluster-robust variance-covariance matrix for a gls object.
Cluster-robust variance-covariance matrix for an ivreg object.
Cluster-robust variance-covariance matrix for an lm object.
Cluster-robust variance-covariance matrix for an lme object.
Cluster-robust variance-covariance matrix for an lmerMod object.
Cluster-robust variance-covariance matrix for an mlm object.
Cluster-robust variance-covariance matrix for a plm object.
Cluster-robust variance-covariance matrix
Cluster-robust variance-covariance matrix for a rma.mv object.
Cluster-robust variance-covariance matrix for a rma.uni object.
Cluster-robust variance-covariance matrix for a robu object.
Test parameter constraints in a fitted linear regression model
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) <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(), 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').