Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections
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...
Impute a patterned block-diagonal covariance matrix
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 estimatr::lm_robust...
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'), lm_robust() and lm_lin() (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'), and rma.uni() and rma.mv() (from 'metafor').