Robust Covariance Matrix Estimators
Bread for Sandwiches
Extract Empirical Estimating Functions
Isotonic Autocorrelation Function
Kernel Weights
Long-Run Variance of the Mean
A Simple Meat Matrix Estimator
Newey-West HAC Covariance Matrix Estimation
Making Sandwiches with Bread and Meat
(Clustered) Bootstrap Covariance Matrix Estimation
Clustered Covariance Matrix Estimation
Heteroscedasticity and Autocorrelation Consistent (HAC) Covariance Mat...
Heteroscedasticity-Consistent Covariance Matrix Estimation
(Clustered) Jackknife Covariance Matrix Estimation
Outer-Product-of-Gradients Covariance Matrix Estimation
Panel-Corrected Covariance Matrix Estimation
Clustered Covariance Matrix Estimation for Panel Data
Kernel-based HAC Covariance Matrix Estimation
Weighted Empirical Adaptive Variance Estimation
Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC) covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC) covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators); clustered covariances (one-way and multi-way); panel and panel-corrected covariances; outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>, Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.
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